In this work, we consider the thermo-mechanical exergy of a substance for cold applications, even as it approaches absolute zero. This is relevant for cold-service applications such as refrigeration, liquefied natural gas, air separation, and liquid hydrogen. We demonstrate how the optimization formulation for the determination of exergy is the most suitable way for process systems engineers to think about exergy. We provide an illustrative example by computing thermo-mechanical exergy of neon approaching absolute zero. We also discuss how this result relates with the Third Law of Thermodynamics, both how it is used to compute thermo-mechanical exergy, but also what it implies about the validity of the results and the equations used to compute them.

Sustainability of the chemical and materials industry (CMI) requires it to achieve net-zero emis-sions of greenhouse gases and other resources while making decisions that have a net-positive impact on nature and society. Many corporations, nations, and universities have pledged to meet such goals but systematic models, methods, and tools to guide this transition are missing. We pre-sent a framework to meet this need. It involves developing a comprehensive, open access model of the global CMI. In addition to existing technologies, this model includes emerging alternatives for renewable energy, circularization, and carbon capture, utilization and storage. Systematic methods help identify innovation opportunities and develop roadmaps that account for long-term changes such as technology evolution and climate change. Meeting the goal of net-zero emis-sions requires inclusion of life cycle impacts. Nature-positive decisions need to encourage eco-logical protection and restoration. This is enabled by a multiscale framework for determining the absolute environmental sustainability of products and processes by accounting for the availability of ecosystem services and their carrying capacities at multiple spatial scales. People-positive decisions need to account for the benefits to society versus harm. Issues of social justice and eq-uity also need to be included in the decisions. More work has focused on the goal of net-zero greenhouse gas emissions but the need for better models, methods and applications remains. Na-ture- and people-positive decisions need to consider spatial and temporal variation of ecological and social systems. Meeting these challenges presents many novel opportunities for socially-relevant process systems engineering.

Following the discovery of the least squares method in 1805 by Legendre and later in 1809 by Gauss, surrogate modeling and machine learning have come a long way. From identifying patterns and trends in process data to predictive modeling, optimization, fault detection, reaction network discovery, and process operations, machine learning became an integral part of all aspects of process design and process systems engineering. This is enabled, at the same time necessitated, by the vast amounts of data that are readily available from processes, increased digitalization, automation, increasing computation power, and simulation software that can model complex phenomena that span over several temporal and spatial scales. Although this paper is not a comprehensive review, it gives an overview of the recent history of machine learning models that we use every day and how they shaped process design problems from the recent advances to the exploration of their prospects.

Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Recent advances in AI and ML are highlighted in four contemporary application areas in chemical engineering design: (1) equitable energy systems, (2) decarbonizing the power sector, (3) circular economies for critical materials, and (4) next-generation heating and cooling. These examples illustrate how AI and ML enable more sophisticated interdisciplinary multiscale models, faster optimization algorithms, more accurate uncertainty quantification, smarter and faster data collection, and incorporation of diverse stakeholders into decision-making processes, improving the robustness of engineering and policy designs while focusing on the multifaceted goals and constraints in wicked problems.

This paper discusses the symbiotic relationship between technology research and development (R&D) and energy transition modeling. On the one hand, energy system modeling has a noteworthy history of providing macroscopic views and critical insights concerning the role that myriad technologies may play in the future energy system. On the other hand, R&D can lead to both incremental and disruptive technological advances that can shape energy transition planning. In this work, we focus on the bidirectional flow of information between the two with a particular focus on highlighting the potential role of carbon capture, storage, and sequestration technology.

The planetary boundaries (PBs) define ecological limits that are critical to preserve the stability of the Earth. Six of them have already been exceeded, which calls for urgent action to optimize industrial systems capable of operating within the safe operating space that they define for humanity. Here we discuss the challenges and opportunities of including PBs in a range of application domains in Process Systems Engineering, focusing on chemicals and fuels production and the use of mathematical programming coupled with life cycle assessment to support sustainable decision-making.

In designing low-carbon processes, the unintended emission of CO2 remains a significant concern due to its global environmental impact. This paper explores carbon management within chemical processes, specifically examining the correlation between the process material balance (PMB) and CO2 emissions to understand and identify the potential for reducing these emissions. We interrogate the foundational issue of carbon discharge by analyzing the interplay among mass, energy, and entropy balances, which collectively influence the PMB. We introduce the concept of the Target Material Balance (TMB), which represents the material balance of a process corresponding to minimum CO2 emissions within the given constraints. We could ask what decisions in the design and operation of processes result in higher CO2 emissions than the TMB. We will focus on the interaction between reactions and recycles and how the arrangement of recycles in processes can inadvertently change the PMB, thereby increasing CO2 emissions substantially above that of the TMB. We will demonstrate how recycle streams can be introduced to modify the PMB to approximate the TMB more closely. We will finally illustrate these concepts using simple examples, which demonstrate that a carbon-emitting process, such as reforming, can have the TMBs adjusted to create a design that not only reduces carbon emissions but eliminates them entirely.

This work presents an overview of the path towards the use of renewable and nonconventional resources for a sustainable chemical and process industry. The aim is not only to lead the way to meet the sustainable development goals but also to maintain the style and quality of life achieved by the technologies and products developed within this sector. Alternative raw materials are to be used and processed differently while a new paradigm for utilities is to be established. The development of technologies and their deployment faces several barriers that we as process engineers can help overcome by providing insight into the alternatives, the thresholds to achieve to become competitive, and strategic analyses.

Sustainability and circular economy enclose initiatives to achieve economic systems and industrial value chains by improving resource use, productivity, reuse, recycling, pollution prevention, and minimizing disposed material. However, shifting from the traditional linear economic production system to a circular economy is challenging. One of the most significant hurdles is the absence of sustainable end-of-life (EoL)/manufacturing loops for recycling and recovering material while minimizing negative impacts on human health and the environment. Overcoming these challenges is critical in returning materials to upstream life cycle stage facilities such as manufacturing. Chemical flow analysis (CFA), sustainability evaluation, and process systems engineering (PSE) can supply chemical products and processes performances from environmental, economic, material efficiency, energy footprint, and technology perspectives. These holistic evaluation techniques can improve productivity, source material reduction, reuse, recycling, and prevent and minimize releases and disposal rates. Therefore, this contribution offers a computational framework that covers CFA, sustainability assessment, and risk evaluation for quantifying the benefits and challenges of chemical circular economy routes versus conventional linear systems. Finally, this contribution shows promising techniques and challenges for employing CFA, sustainability evaluation, and PSE as multicriteria decision-making tools for designing a closed-loop chemical management infrastructure and transforming the US chemical industry sector from linear to circular.

Pharma 4.0 has continued to advance as the industry develops advances in process analytical technologies, automation, and digit-ization. Digital twins which transform on-line process measure-ments into meaningful outputs in real-time are being developed to seize the opportunity made possible with this shift. Digital twins can be used for improved process optimization on a range of scales, from determining optimal metabolite concentrations in upstream bioreactors to considering economic and environmental impacts of process decisions. In this paper, we explore the current uses of digital twins in solid-based pharmaceutical space and the bio-pharmaceutical manufacturing. Applications cover scale up of upstream processes, product quality control, and consideration of continuous systems. We also describe the intersection of digital twins in flow sheet modeling, sensitivity analysis and optimization, and design space evaluation. Finally, areas requiring further im-provement for industry adoption are addressed.

The (bio-)pharmaceutical industry is facing crossroads in an effort to ramp up its global capacity, while working to meet net-zero targets and to ensure continuous drug supply. Beyond geopolitical challenges faced worldwide, (bio-)pharmaceutical processes have been historically very complex to design, optimise and integrate in a global distribution network that is resilient and adaptable to changes. In this paper we offer a perspective of how Process Systems Engineering (PSE) tools can support and advance (bio-)pharma practices with an outlook towards 3-fold sustainability. The latter is considering three main pillars, namely social (drug supply), economical and environmental sustainability. We discuss PSE contributions that have revolutionised process design in this space, as well as the optimisation of distributions networks in pharmaceuticals. We do this by means of example cases: one on model-based unit operation design and a second one on sustainable supply chain networks in the space of advanced therapeutics. As such, this contribution offers a perspective on how PSE methodologies can offer a systematic way to integrate social, environmental, and economical sustainability throughout process design and product distribution.

Artificial intelligence (AI) and particularly generative AI led to recent breakthroughs, e.g., in generating text and images. There is also a potential of these technologies in chemical engineering, but the lack of structured big domain-relevant data hinders advancements. I envision an open Chemical Engineering Knowledge Graph (ChemEngKG) that provides big open and linked chemical process information. In this article, I present the concept of “flowsheet mining” as the first step towards the ChemEngKG. Flowsheet mining extracts process information from flowsheets and process descriptions found in scientific literature and patents. The proposed technology requires the integration of data mining, computer vision, natural language processing, and semantic web technologies. I present the concept of flowsheet mining, discuss previous literature, and show future potentials. I believe the availability of big data will enable breakthroughs in process design through artificial intelligence.

Chemical engineers are natural “systems-thinkers”; this is a skill that allows us to analyze highly complex processes that involve heterogeneous components, phenomena, and scales. Systems-thinking skills are fostered in the chemical engineering curriculum via integrative and project-based courses, such as process/product design and laboratories. However, existing curricula tends to focus scope to product/process boundaries, offering limited opportunities to capture connections to behavior occurring at small scales (e.g., atomistic and molecular) and at large scales (e.g., supply chains, policy, markets, and infrastructures). This limit in scope can hinder our ability to appreciate how products/processes that we develop impact society, markets, and the environment (e.g., the opioid addiction crisis, environmental impacts of forever chemicals and chemical fertilizers, and electricity markets). This limit in scope can also hinder our ability to appreciate how emerging tools from the molecular sciences can help us design better products/processes. Expanding the boundaries of our thinking is essential in overcoming these limitations. In this perspective, I discuss how emerging concepts and technologies from machine learning, data science, environmental sciences, molecular simulations, and mathematics provide powerful tools to help foster systems-thinking over a broad set of scales and to help establish connections with non-traditional disciplines (e.g., social sciences). In addition, I discuss the need to create new conceptual frameworks, case studies, and software that can help foster systems-thinking.

This paper presents an algorithm for developing sparse surrogate models that satisfy mass/energy conservation even when the training data are noisy and violate the conservation laws. In the first step, we employ the Bayesian Identification of Dynamic Sparse Algebraic Model (BIDSAM) algorithm proposed in our previous work to obtain a set of hierarchically ranked sparse models which approximate system behaviors with linear combinations of a set of well-defined basis functions. Although the model building algorithm was shown to be robust to noisy data, conservation laws may not be satisfied by the surrogate models. In this work we propose an algorithm that augments a data reconciliation step with the BIDSAM model for satisfaction of conservation laws. This method relies only on known boundary conditions and hence is generic for any chemical system. Two case studies are considered-one focused on mass conservation and another on energy conservation. Results show that models with minimum bias are built by using the developed algorithm while exactly satisfying the conservation laws for all data.

Process simulation problems often involve systems of nonlinear and nonconvex equations and may run into convergence issues due to the existence of recycle loops within such models. To that end, surrogate models have gained significant attention as an alternative to high-fidelity models as they significantly reduce the computational burden. However, these models do not always provide a guarantee on the prediction accuracy over the domain of interest. To address this issue, we strike a balance between computational complexity by developing a data-driven branch and prune-based framework that progressively leads to a guaranteed solution to the original system of equations. Specifically, we utilize interval arithmetic techniques to exploit Hessian information about the model of interest. Along with checking whether a solution can exist in the domain under consideration, we generate error-bounded convex hull surrogates using the sampled data and Hessian information. When integrated in a branch and prune framework, the branching leads to the domain under consideration becoming smaller, thereby reducing the quantified prediction error of the surrogate, ultimately yielding a solution to the system of equations. In this manner, we overcome the convergence issues that are faced by many simulation packages. We demonstrate the applicability of our framework through several case studies. We first utilize a set of test problems from literature. For each of these test systems, we can find a valid solution. We then demonstrate the efficacy of our framework on real-world process simulation problems.

Alternative formulations for the optimization of chemical process flowsheets are presented that leverage surrogate models and implicit functions to replace and remove, respectively, the algebraic equations that describe a difficult-to-converge Gibbs reactor unit operation. Convergence reliability, solve time, and solution quality of an optimization problem are compared among full-space, ALAMO surrogate, neural network surrogate, and implicit function formulations. Both surrogate and implicit formulations lead to better convergence reliability, with low sensitivity to process parameters. The surrogate formulations are faster at the cost of minor solution error, while the implicit formulation provides exact solutions with similar solve time. In a parameter sweep on the autothermal reformer flowsheet optimization problem, the full-space formulation solves 33 out of 64 instances, while the implicit function formulation solves 52 out of 64 instances, the ALAMO polynomial formulation solves 64 out of 64 instances, and the neural network formulation solves 48 out of 64 instances. This work demonstrates the trade-off between accuracy and solve time that exists in current methods for improving convergence reliability of chemical process flowsheet optimization problems.

Phthalates are the most widely used plasticizers in the polymers industry; however, their toxicity and environmental impacts have led to their ban in various applications. This has driven the search for more sustainable alternatives, including biobased citrate esters, especially tributyl citrate (TBC) and its acetylated form. TBC is typically produced by refined citric acid (CA) esterification with 1-butanol (BuOH). However, the high energy and materials-intensive downstream purification of fermentation-derived CA involves high production costs, thus limiting the widespread adoption of TBC as a plasticizer. This work presents an innovative approach for TBC production using calcium citrate as feedstock instead of pure CA. The process involves a simultaneous acidification-esterification stage and further hydration of calcium sulfate, thus reducing costs by avoiding multiple CA refining steps. The approach proceeds via a solid-solid-liquid reaction of calcium citrate with sulfuric acid in butanol, releasing CA, which is simultaneously esterified to form TBC. The resultant calcium sulfate aids in water removal to enhance esterification conversion. Based upon experimentally validated models and rigorous simulations, the proposed approach was evaluated, and it exhibited significant reductions in processing times and operating costs, with savings of at least 46% in utilities compared to traditional TBC production. The novel approach was found suitable and promising for industrial deployment.

Rapid and robust convergence of a process flowsheet is critical to enable large-scale simulations that address core scientific questions related to process design, optimization, and sustainability. However, due to the highly coupled and nonlinear nature of chemical processes, efficiently solving a flowsheet remains a challenge. In this work, we show that graph representations of the underlying physical phenomena in unit operations may help identify potential avenues to systematically reformulate the network of equations and enable more robust topology-based convergence of flowsheets. To this end, we developed graph abstractions of the governing equations of vapor-liquid and liquid-liquid equilibrium separation equipment. These graph abstractions consist of a mesh of interconnected variable nodes and equation nodes that are systematically generated through PhenomeNode, a new open-source library in Python developed in this study. We show that partitioning the graph into separate mass, energy, and equilibrium subgraphs can help decouple nonlinearities and guide decomposition algorithms. By employing the graph abstraction on an industrial separation process for separating glacial acetic acid from water, we implemented a new block decomposition scheme in BioSTEAM and demonstrated that this can accelerate convergence over a traditional sequential modular approach.

Maintaining product integrity in multi-product oil pipelines is crucial for efficiency and profit. This study presents a strategy combining design and process improvement to enhance flushing protocols, addressing the challenge of residual batch contamination. A pilot plant, mirroring industrial operations through dimensionless residence time distribution, was developed to identify and rectify bottlenecks during product transition. The pilot plant’s success in replicating industrial operations paves the way for targeted experiments and modelling to enhance optimized flushing, ensuring product quality and operational excellence.

We present new modifications to superstructure optimization paradigms to i) enable their robust solution and ii) extend their applicability. Superstructure optimization of chemical process flowsheets on the basis of rigorous and detailed models of the various unit operations, such as in the state operator network (SON) paradigm, is prone to non-convergence. A key challenge in this optimization-based approach is that when process units are deselected from a superstructure flowsheet, the constraints that represent the deselected process unit can be numerically singular (e.g., divide by zero, logarithm of zero and rank-deficient Jacobian). In this paper, we build upon the recently-proposed modified state operator network (MSON) that systematically eliminates singularities due to unit deselection and is equally applicable to the context of both simulation-based and equation-oriented optimization. A key drawback of the MSON is that it is only applicable to the design of isobaric flowsheets at a pressure fixed a priori. In this paper, as a first step towards the synthesis of general flowsheets with variable pressures, we extend the MSON to the synthesis of a gas-liquid absorption column at variable pressure (i.e., the pressure is a degree of freedom that may be optimized). We illustrate the use of the extended MSON on a carbon-capture process. The extended MSON is robust and enables the design of the column on the basis of detailed thermodynamic models and simulation-based optimization.

Optimization-based process design is a central task of process systems engineering. However, solely relying on steady-state models may potentially lead to dynamic constraint violations, hinder robust performance, or simply reduce the controllability of a process. This has led to the consideration of process dynamics in the design phase, which is commonly termed integration of design and operation / control. Recently, we proposed a framework to carry out this integrative task by formulating a large-scale nonlinear programming problem that is solved simultaneously. To this end, the dynamic process model was discretized, and dynamic variability and parametric uncertainty were included. However, the proposed framework only operates on constant lengths of the finite elements. The discretization error was not assessed. Within this contribution, a method for quantifying this discretization error and adapting the number of finite elements accordingly is incorporated into the recently proposed framework and applied on the case study of a continuous tank reactor. The obtained results with and without discretization error control are compared and, based thereon, a more suitable way to apply the control variables on the process is proposed.

Although global efforts for CO2 capture are underway, large-scale CO2 capture projects still face economic risks and technical challenges. The Rotating Packed Bed (RPB) provides an alternative solution by mitigating location constraints and enabling a gradual increase in the scale of CO2 capture through compact modular sizes. However, the main challenge in RPB-based CO2 capture processes lies in the limited experience with implementing industrial-scale RPB processes. The intricate relationship between RPB unit design, operating conditions, and process performance further complicates the process-level analysis for scale-up. To address these challenges, we propose an optimization-based process design for RPB-based CO2 capture. Leveraging rigorous process modeling and simulation, we aim to make simultaneous decisions on RPB unit design and operating conditions. Ultimately, our goal is to develop a cost-effective and optimal RPB-based CO2 capture process, supported by comprehensive cost evaluations. This modularized and cost-effective approach is expected to facilitate rapid implementation and gradual scale-up, thereby reducing entry barriers to CO2 capture technology for industries.

Early stage exploration of reaction systems, including catalyst selection, operating conditions’ specifications, reactor design, and optimization, is critical in the engineering field. It is general practice in the reaction engineering field to explore systems against certain performance metrics, of which yield is one of the most commonly utilized objectives. While the yield provides a quantitative measure of how efficiently reactants are converted into target product(s), its definition is ambiguous, particularly in the presence of side/ incomplete reactions, and multiple products. Most of the yield definitions focus on a specific target product; however, conditions within the reactor search space that provide a maximum yield for one product may not be the same as those for another. Moreover, the presence of other undesired products that are not considered may reduce the overall efficiency of the system. This necessitates the utilization of a more holistic metric that encompasses the value of all the generated products. Attempts to address this consider lumping components into a total yield metric. However, this assumes equal weights on all components without adequately capturing their individual significance on the actual performance. This study proposes the utilization of an “economic-value yield” objective that captures all the products’ value by using the market price as a weight factor. The traditional yield metric for the various products is contrasted against the economic one to highlight its ability of providing insight into regions within the reactor search space that are associated with high-value products that are otherwise not observed in the conventional definition. This is illustrated with a case study utilizing propane as a feedstock in the novel piston reactor technology.

Adsorption-based Atmospheric Water Extraction (AWE) is an energy-efficient distributed freshwater supply method. This research focuses on AWE’s kinetic analysis and stochastic optimization, investigating the impact of ambient conditions, kinetics, and weather variability. A one-dimensional fixed-bed system was numerically analyzed using the validated isotherm of MIL-100 (Fe), assuming different kinetic parameters within the linear driving force model. Stochastic optimization, based on annual weather data from Georgia (GA), illustrates the influence of weather conditions on AWE process performance, operation, and cost. Our study offers valuable insights for future research, including site selection, adsorbent material development, and process design. We outline three critical areas for further exploration: experimental verification, material screening, and meteorological site selection.

Superstructure optimization for process synthesis is a challenging endeavour typically leading to large scale MINLP formulations. By the combination of phenomena-based building blocks, accurate thermodynamics, and structural screening we obtain a new framework for optimal process synthesis, which overcomes prior limitations regarding solution by deterministic MINLP solvers in combination with accurate thermodynamics. This is facilitated by MOSAICmodeling’s generic formulation of models in MathML / XML and subsequent decomposition and code export to GAMS and C++. A branch & bound algorithm is implemented to solve the overall MINLP problem, wherein the structural screening penalizes instances, which are deemed nonsensical and should not be further pursued. The general capabilities of this approach are shown for the distillation-based separation of a ternary system.

Within an Industry 4.0 framework, a variety of new considerations are of increasing importance, such as securing processes against cyberattacks on the control systems or utilizing advances in image processing for image-based control. These new technologies impact relationships between process design and control. In this work, we discuss some of these potential relationships, beginning with a discussion of side channel attacks and what they suggest about ways of evaluating plant design and instrumentation selection, along with controller and security schemes, particularly as more data is collected and there is a move toward an industrial Internet of Things. Next, we highlight how the 3D computer graphics software tool set Blender can be utilized to analyze a variety of considerations related to ensuring safety of plant operation and facilitating the design of assemblies with image-based sensing.

The determination of the design space (DS) in a pharmaceutical process is a crucial aspect of the quality-by-design (QbD) initiative which promotes quality built into the desired product. This is achieved through a deep understanding of how the critical quality attributes (CQAs) and process parameters (CPPs) interact that have been demonstrated to provide quality assurance. For computational inexpensive models, the original process model can be directly deployed to identify the design space. One such crucial process is the Tablet Press (TP), which directly compresses the powder blend into individual units of the final product or adds dry or wet granulation to meet specific formulation needs. In this work, we identify the design space of input variables in a TP such that there is a (probabilistic) guarantee that the tablets meet the quality constraints under a set of operating conditions. A reduced-order model of TP is assigned for this purpose where the effects of lubricants and glidants are used to characterize the design space to achieve the desired tablet CQAs. The probabilistic design space, which takes into account interactions between crucial process parameters and important quality characteristics including model uncertainty, is also approximated because of the high cost associated with the comprehensive experiments.

In this work, we present recent algorithmic and implementation advances of the nonconvex two-stage robust optimization solver PyROS. Our advances include extensions of the scope of PyROS to models with uncertain variable bounds, improvements to the formulations and/or initializations of the various subproblems used by the underlying cutting set algorithm, and extensions to the pre-implemented uncertainty set interfaces. The effectiveness of PyROS is demonstrated through the results of an original benchmarking study on a library of over 8,500 small-scale instances, with variations in the nonlinearities, degree-of-freedom partitioning, uncertainty sets, and polynomial decision rule approximations. To demonstrate the utility of PyROS for large-scale process models, we present the results of a carbon capture case study. Overall, our results highlight the effectiveness of PyROS for obtaining robust solutions to optimization problems with uncertain equality constraints.

Developing methods for rapid, large-scale deployment of carbon capture systems is critical for meeting climate change goals. Optimization-based decisions can be employed at the design and manufacturing phases to minimize the costs of deployment and operation. Manufacturing standardization results in significant cost savings due to economies of numbers. Building on previous work, we present a process family design approach to design a set of carbon capture systems while explicitly including economies of numbers savings within the formulation. Our formulation optimizes both the number and characteristics of the common components in the platform and simultaneously designs the resulting set of carbon capture systems. Savings from economies of numbers are explicitly included in the formulation to determine the number of components in the platform. We show and discuss the savings we gain from economies of numbers.

Cyclic adsorption processes attain a cyclic-steady state (CSS) condition by undergoing repeated cycles in time, owing to their transient and modular nature. Mathematically, solving a set of underlying nonlinear partial differential equations iteratively for different steps in a cycle until the CSS condition is attained presents a computational challenge, making the simulation and optimization of cyclic adsorption processes time-consuming. This paper focuses on expediting the CSS convergence in adsorption process simulations by implementing two vector-based acceleration methods that offer quadratic convergence akin to Newton’s methods. These methods are straightforward to implement, requiring no prior knowledge of the first derivatives (or Jacobian). The study demonstrates the efficacy of accelerated convergence by considering two adsorption processes that exhibit complex dynamics, namely, a four-step vacuum swing adsorption and a six-step temperature swing adsorption cycles for post-combustion CO2 capture. The case studies showcase the potential for improved computational efficiency in adsorption process simulations.

Solvent-based carbon capture processes typically suffer from the temperature rise of the solvent due to the heat of absorption of CO2. This increased temperature is not thermodynamically favorable and results in a significant reduction in performance in the absorber column. As opposed to interstage coolers, which only remove, cool, and return the solvent at discrete locations in the column, internal coolers that are integrated with the packing can cool the process inline, which can result in improved efficiency. This work presents the modeling of these internal coolers within an existing generic, equation-oriented absorber column model that can cool the process while allowing for simultaneous mass transfer. Optimization of this model is also performed, which is capable of optimally choosing the best locations to place these devices, such that heat removal and mass transfer area are balanced. Results of the optimization have shown that optimally placed cooling elements result in a significant increase in the capture efficiency of the process, compared to a similar column with no internal cooling, with a common trend being the cooling of the column in the temperature bulge region. It is observed that by optimally placing an internal cooler, the solvent flow rate can be decreased, and the CO2 lean loading can be increased while still maintaining the same efficiency. These process changes can lead to a substantial reduction in costs due to lower reboiler duty.

Chemical recycling of plastics is a promising technology to reduce carbon footprint and ease the pressure of waste treatment. Specifically, highly efficient conversion technologies for polyolefins will be the most effective solution to address the plastic waste crisis, given that polyolefins are the primary contributors to global plastic production. Significant challenges encountered by plastic waste valorization facilities include the uncertainty in the composition of the waste feedstock, process yield, and product price. These variabilities can lead to compromised performance or even render operations infeasible. To address these challenges, this work applied the robust optimization-based framework to design an integrated polyolefin chemical recycling plant. Data-driven surrogate model was built to capture the separation units’ behavior and reduce the computational complexity of the optimization problem. It was found that when process yield and price uncertainties were considered, wax products became more favorable, and pyrolysis became the preferred reaction technology.

Design problems for process systems engineering applications often require multi-scale modeling integrating detailed process models. Consequently, black-box optimization and surrogate modeling have continued to play a fundamental role in mission-critical design applications. Inherent in surrogate modeling applications, particularly those constrained by “expensive” function evaluations, are the questions of how to properly balance “exploration” and “exploitation” and how to do so while harnessing parallel computing in techniques. We devise and investigate a one-step look-ahead GRASP heuristic for balancing exploration and exploitation in a parallel environment. Computational results reveal that our approach can yield equivalent or superior surrogate quality with near linear scaling in the number of parallel samples.

This paper proposes an AI-assisted approach aimed at accelerating chemical process design through causal incremental reinforcement learning (CIRL) where an intelligent agent is interacting iteratively with a process simulation environment (e.g., Aspen HYSYS, DWSIM, etc.). The proposed approach is based on an incremental learnable optimizer capable of guiding multi-objective optimization towards optimal design variable configurations, depending on several factors including the problem complexity, selected RL algorithm and hyperparameters tuning. One advantage of this approach is that the agent-simulator interaction significantly reduces the vast search space of design variables, leading to an accelerated and optimized design process. This is a generic causal approach that enables the exploration of new process configurations and provides actionable insights to designers to improve not only the process design but also the design process across various applications. The approach was validated on industrial processes including an absorption-based carbon capture, considering the economic and technological uncertainties of different capture processes, such as energy price, production cost, and storage capacity. It achieved a cost reduction of up to 5.5% for the designed capture process, after a few iterations, while also providing the designer with actionable insights. From a broader perspective, the proposed approach paves the way for accelerating the adoption of decarbonization technologies (CCUS value chains, clean fuel production, etc.) at a larger scale, thus catalyzing climate change mitigation.

Explicitly incorporating the effects of chemical phenomena such as chemical pretreatment and mineral scaling during the design of treatment systems is critical; however, the complexity of these phenomena and limitations on data have historically hindered the incorporation of detailed water chemistry into the modeling and optimization of water desalination systems. Thus, while qualitative assessments and experimental studies on chemical pretreatment and scaling are abundant in the literature, very little has been done to assess the technoeconomic implications of different chemical pretreatment alternatives within the context of end-to-end water treatment train optimization. In this work, we begin to address this challenge by exploring the impact of pH control during pretreatment on the cost and operation of a high-recovery desalination train. We compare three pH control methods used in water treatment (H2SO4, HCl, and CO2) and assess their impact on the operation of a desalination plant for brackish water and seawater. Our results show that the impact of the acid choice on the cost can vary widely depending on the water source, with CO2 found to be up to 11% and 49% more expensive than HCl in the seawater and brackish cases, respectively. We also find that the acid chemistry can significantly influence upstream processes, with use of H2SO4 requiring more calcium removal in the softening step to prevent gypsum scaling in HPRO system. Our work highlights why incorporating water chemistry information is critical when evaluating the key cost and operational drivers for high-recovery desalination treatment trains.

In this paper, a hybrid heuristic rule-based and deterministic optimization-driven process decision framework is presented for the analysis and optimization of process flowsheets for end-to-end optimal (E2E0) pharmaceutical manufacturing. The framework accommodates various operating modes, such as batch, semi-batch and continuous, for the different unit operations that implement each manufacturing step. To address the challenges associated with solving process synthesis problems using a simulation-optimization approach, heuristic-based process synthesis rules are employed to facilitate the reduction of the superstructure into smaller sub-structures that can be more readily optimized. The practical application of the framework is demonstrated through a case study involving the end-to-end continuous manufacturing of an anti-cancer drug, lomustine. Alternative flowsheet structures are evaluated in terms of the sustainability metric, E-factor while ensuring compliance with the required production targets and critical product quality attributes.

Water chemistry plays a critical role in the design and operation of water treatment processes. Detailed chemistry modeling tools use a combination of advanced thermodynamic models and extensive databases to predict phase equilibria and reaction phenomena. The complexity and formulation of these models preclude their direct integration in equation-oriented modeling platforms, making it difficult to use their capabilities for rigorous water treatment process optimization. Neural networks (NN) can provide a pathway for integrating the predictive capability of chemistry software into equation-oriented models and enable optimization of complex water treatment processes across a broad range of conditions and process designs. Herein, we assess how NN architecture and training data impact their accuracy and use in equation-oriented water treatment models. We generate training data using PhreeqC software and determine how data generation and sample size impact the accuracy of trained NNs. The effect of NN architecture on optimization is evaluated by optimizing hypothetical black-box desalination processes using a range of feed compositions from USGS brackish water data set, tracking the number of successful optimizations, and testing the impact of initial guess on the final solution. Our results clearly demonstrate that data generation and architecture impact NN accuracy and viability for use in equation-oriented optimization problems.

Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data. While the mechanistic model broadly captured the overall growth trend, discrepancies remained between the model and data due to the model’s simplicity and non-ideal behavior of real-world measurement. SINDy was applied to model the residual error by identifying an error derivative correction term. Addition of this SINDy-informed error dynamics term shows improvement to model accuracy while maintaining interpretability of the underlying mechanistic framework. This work demonstrates the potential for machine learning techniques like SINDy to aid simple mechanistic models in scale-specific predictive accuracy.

Antibody therapies are important in treating life-threatening ailments such as cancer and autoimmune diseases. Purity of the antibody is essential for successful applications and Protein A selective resin extraction is the standard step for antibody recovery. Unfortunately, such resins can cost up to 30% of the total cost of antibody production. Hence, the optimal design of this purification step becomes a critical factor in downstream processing to minimize the size of the column needed. An accurate predictive model, as a digital twin representing the purification process, is necessary where changes in the flow rates and the inlet concentrations are modeled via the Method of Moments. The system uncertainties are captured by including the stochastic Ito process model of Brownian motion with drift. Pontryagin’s Maximum Principle under uncertainty is then applied to predict the flowrate control strategy for optimized resin use, column design, and efficient capturing of the antibodies. In this study, the flow rate is controlled to optimize the process efficiency via maximizing the theoretical plate number with time, the objective for efficient resin usage within a fixed-size column. This work successfully achieved optimality, which was also confirmed via experimentation, leading to higher antibody resin loading capacity. When the work was expanded to 200 cycles of Protein A usage, significant improvements in the downstream process productivity were achieved allowing for smaller footprint columns to be used.

Computer-aided Molecular and Process Design (CAMPD) is an equation-oriented multi-scale decision making framework for designing both materials (molecules) and processes for separation, reaction, and reactive separation whenever material choice significantly impacts process performance. The inherent nonlinearity and nonconvexity in CAMPD optimization models, introduced through the property and process models, pose challenges to state-of-the-art solvers. Recently, quantum computing (QC) has shown promise for solving complex optimization problems, especially those involving discrete decisions. This motivates us to explore the potential usage of quantum optimization techniques for solving CAMPD problems. We have developed a technique for directly solving a class of mixed integer nonlinear programs using QC. Our approach represents both continuous and integer design decisions by a set of binary variables through encoding schemes. This transformation allows to reformulate certain types of CAMPD problems into Quadratic Unconstrained Binary Optimization (QUBO) models that can be directly solved using quantum annealing techniques. We illustrate this technique for the selection of optimal ionic liquids (IL) and the configuration of a reactor-separator process network. We also discuss several challenges that are associated with quantum optimization when solving large scale CAMPD problems.

We present a string representation for hybrid extraction and distillation using symbols representing phenomena building blocks. Unlike the conventional equipment-based string representation, the proposed representation captures the design details of liquid-liquid extraction and distillation. We generate a set of samples through the procedure of input parameter sampling and superstructure optimization that minimizes separation cost. We convert these generated samples into a set of string representations based on pre-defined rules. We use these string representations as descriptors and connect them with conditional variational encoder. The trained conditional variational encoder shows good prediction accuracy. We further use the trained conditional variational encoder to screen designs of hybrid extraction and distillation with desired cost investment.

Adaptive and self-optimizing intelligent systems such as digital twins are increasingly important in science and engineering. Digital twins utilize mathematical models to provide added precision to decision-making. However, physics-informed models are challenging to build, calibrate, and validate with existing data science methods. Model-based design of experiments (MBDoE) is a popular framework for optimizing data collection to maximize parameter precision in mathematical models and digital twins. In this work, we apply MBDoE, facilitated by the open-source package Pyomo.DoE, to train and validate mathematical models for batch crystallization. We quantitatively examined the estimability of the model parameters for experiments with different cooling rates. This analysis provides a quantitative explanation for the heuristic of using multiple experiments at different cooling rates.

Machine learning (ML) has become a powerful tool to analyze complex relationships between multiple variables and to unravel valuable information from big datasets. However, an open research question lies in how ML can accelerate the design and optimization of processes in the early experimental development stages with limited data. In this work, we investigate the ML-aided process design of a microwave reactor for ammonia production with exceedingly little experimental data. We propose an integrated approach of synthetic minority oversampling technique (SMOTE) regression combined with neural networks to quantitatively design and optimize the microwave reactor. To address the limited data challenge, SMOTE is applied to generate synthetic data based on experimental data at different reaction conditions. Neural network has been demonstrated to effectively capture the nonlinear relationships between input features and target outputs. The softplus activation function is used for a smoother prediction compared to the Rectified Linear Unit activation function. Ammonia concentration is predicted using pressure, temperature, feed flow rate, and feed composition ratio as input variables. For point-wise prediction based on discrete operating conditions, the proposed SMOTE integrated neural network approach outperforms with 96.1% accuracy compared to neural networks (without SMOTE), support vector regression, and linear regression. The multi-variate prediction trends are also validated which are critical for design optimization.

Modern industrial processes are continuously monitored by a large number of sensors. Despite having access to large volumes of historical and online sensor data, industrial practitioners still face challenges in the era of Industry 4.0 in effectively utilizing them to perform online process monitoring and fast fault detection and diagnosis. To target these challenges, in this work, we present a novel framework named “FARM” for Fast, Accurate, and Robust online process Monitoring. FARM is a holistic monitoring framework that integrates (a) advanced multivariate statistical process control (SPC) for fast anomaly detection of nonparametric, heterogeneous data streams, and (b) modified support vector machine (SVM) for accurate and robust fault classification. Unlike existing general-purpose process monitoring frameworks, FARM’s unique hierarchical architecture decomposes process monitoring into two fault detection and diagnosis, each of which is conducted by targeted algorithms. Here, we test and validate the performance of our FARM monitoring framework on Tennessee Eastman Process (TEP) benchmark dataset. We show that SPC achieves faster fault detection speed at a lower false alarm rate compared to state-of-the-art benchmark fault detection methods. In terms of fault classification diagnosis, we show that our modified SVM algorithm successfully classifies 17 out of 20 of the fault scenarios present in the TEP dataset. Compared with the results of standard SVM trained directly on the original dataset, our modified SVM improves the fault classification accuracy significantly.

This paper presents the development of algorithms for mass-energy constrained neural network (MECNN) models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy steady-state/transient data used for optimal model training violate the same. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks are used as candidate architectures. The proposed approaches for solving both the inverse and forward problems are validated considering both steady-state and dynamic data in presence of various noise characteristics. The proposed network structures and algorithms are applied to the development of data-driven models of a nonlinear non-isothermal reactor that involves an exothermic reaction making it significantly challenging to exactly satisfy the mass and energy conservation laws of the system only by using the available input and output boundary conditions.

A hybrid model for the fluid bed granulation operation was built. The deterministic component focuses on the mass and energy balances representing the water ingress and egress from the powder bed. The empirical one does on granule growth. Estimability techniques were used to determine which parameters to regress from the available data. A partial least squares approach was used to better understand the impact of the model parameters onto key model responses and sensitivity plots were made to aid operational decisions and support pharmaceutical development.

Cardiomyocytes (CMs), the contractile heart cells that can be derived from human induced pluripotent stem cells (hiPSCs). These hiPSC derived CMs can be used for cardiovascular disease drug testing and regeneration therapies, and they have therapeutic potential. Currently, hiPSC-CM differentiation cannot yet be controlled to yield specific heart cell subtypes consistently. Designing differentiation processes to consistently direct differentiation to specific heart cells is important to realize the full therapeutic potential of hiPSC-CMs. A model that accurately represents the dynamic changes in cell populations from hiPSCs to CMs over the differentiation timeline is a first step towards designing processes for directing differentiation. This paper introduces a microsimulation model for studying temporal changes in the hiPSC-to-early CM differentiation. The differentiation process for each cell in the microsimulation model is represented by a Markov chain model (MCM). The MCM includes cell subtypes representing key developmental stages in hiPSC differentiation to early CMs. These stages include pluripotent stem cells, early primitive streak, late primitive streak, mesodermal progenitors, early cardiac progenitors, late cardiac progenitors, and early CMs. The time taken by a cell to transit from one state to the next state is assumed to be exponentially distributed. The transition probabilities of the Markov chain process and the mean duration parameter of the exponential distribution were estimated using Bayesian optimization. The results predicted by the MCM agree with the data.

Modeling the non-linear dynamics of a system from measurement data accurately is an open challenge. Over the past few years, various tools such as SINDy and DySMHO have emerged as approaches to distill dynamics from data. However, challenges persist in accurately capturing dynamics of a system especially when the physical knowledge about the system is unknown. A promising solution is to use a hybrid paradigm, that combines mechanistic and black-box models to leverage their respective strengths. In this study, we combine a hybrid modeling paradigm with sparse regression, to develop and identify models simultaneously. Two methods are explored, considering varying complexities, data quality, and availability and by comparing different case studies. In the first approach, we integrate SINDy-discovered models with neural ODE structures, to model unknown physics. In the second approach, we employ Multifidelity Surrogate Models (MFSMs) to construct composite models comprised of SINDy-discovered models and error-correction models.

Continuous manufacturing in pharmaceutical industries has shown great promise to achieve process intensification. To better understand and justify such changes to the current status quo, a technoeconomic analysis of a continuous production must be conducted to serve as a predictive decision-making tool for manufacturers. This paper uses PharmaPy, a custom-made Python-based library developed for pharmaceutical flowsheet analysis, to simulate an annual production cycle for a given active pharmaceutical ingredient (API) of varying production volumes for a batch crystallization system and a continuous mixed suspension, mixed product removal (MSMPR) crystallizer. After each system is optimized, the generalized cost drivers, categorized as capital expenses (CAPEX) or operational expenses (OPEX), are compared. Then, a technoeconomic and sustainability cost analysis is done with the process mass intensity (PMI) as a green metric. The results indicate that while the batch system does have an overall lower cost and better PMI metric at smaller manufacturing scales in comparison with the continuous system, the latter system showed more potential for scaling-up for larger production volumes.

Copolymers are commonplace in various industries. Nevertheless, fine-tuning their properties bears significant cost and effort. Hence, an ability to predict polymer properties a priori can significantly reduce costs and shorten the need for extensive experimentation. Given that the physical and chemical characteristics of copolymers are correlated with molecular arrangement and chain topology, understanding the reactivity ratios of monomers—which determine the copolymer composition and sequence distribution of monomers in a chain—is important in accelerating research and cutting R&D costs. In this study, the prediction accuracy of two Artificial Neural Network (ANN) approaches, namely, Multi-layer Perceptron (MLP) and Graph Attention Network (GAT), are compared. The results highlight the potency and accuracy of the intrinsically interpretable ML approaches in predicting the molecular structures of copolymers. Our data indicates that even a well-regularized MLP cannot predict the reactivity ratio of copolymers as accurately as GAT. This is attributed to the compatibility of GAT with the data structure of molecules, which are graph-representative.

The integrated design and control (IDC) framework is becoming increasingly important for systematic design of flexible manufacturing and energy systems. Recent advances in computing and derivative-free optimization have enabled more tractable solution methods for complex IDC problems that involve, e.g., multi-period dynamics, the presence of high-variance and non-stationarity probabilistic uncertainties, and mixed-integer control/scheduling decisions. Parallelly, developments in techno-ecological synergy (TES) have allowed co-design of industrial and environmental systems that have been shown to lead to win-win solutions in terms of the economy, ecological, and societal benefits. In this work, we propose to combine the IDC and TES frameworks to more accurately capture the real-time interactions between process systems and the surrounding natural resources (e.g., forests, watersheds). Specifically, we take advantage of (multi-scale) model predictive control to close the loop on a realistic high-fidelity simulation of the overall TES system. Since this closed-loop simulation is computationally expensive, we propose to solve the resulting design problem using a data-efficient constrained Bayesian optimization method. We demonstrate that the new perspective offered by the proposed TES-IDC framework leads to robust win-win solutions that can more effectively handle uncertainty in future disturbances compared to technology-only solutions on a chloralkali manufacturing unit built in an urban forest.

Pharmaceutically elegant tablets are an expectation from pharmacists, health care providers and consumers for solid oral dosage forms. The presence of non-aesthetically pleasing defects in solid oral dosage forms can result in complaints back to the manufacturer and potentially non-compliance with medicines. The purpose of this study was to simulate and analyze the design of a tablet core and the aqueous film-coating process, to gain a better understanding of tablet defect generation, and to help eliminate the defects from the finished product. This evaluation employs Discrete Element Method (DEM) using the software product Altair® EDEM™ to understand the potential mechanisms that are causing the defects, based on the forces tablets experience in the coating operation, along with the number of tablet-to-tablet interactions that occur during the duration of the process. Defects observed during the scale up of the coating process to a commercial production scale confirmed the DEM results where physical damage was observed more on the edges of the tablets than the face of the tablets. Also based on the number of tablet-to-tablet interactions, operating the coating process under thermodynamically wetter processing conditions can result in elevated levels of picking and sticking defects being observed based on the specific tablet design evaluated. The results of these efforts allowed the manufacturing and development team to evaluate improvement opportunities not only in tablet design but also to re-evaluate the thermodynamic design space of the coating operation and the mechanical set up of the coating equipment.

In this work, we present a follow-up work of reinforcement learning (RL)-driven process design using the Institute for Design of Advanced Energy Systems Process Systems Engineering (IDAES-PSE) Framework. Herein, process designs are generated as stream inlet-outlet matrices and optimized using the IDAES platform, the objective function value of which is the reward to RL agent. Deep Q-Network is employed as the RL agent including a series of convolutional neural network layers and fully connected layers to compute the actions of adding or removing any stream connections, thus creating a new process design. The process design is then informed back to the RL agent to refine its learning. The iteration continues until the maximum number of steps is reached with feasible process designs generated. To further expedite the RL search of the design space which can comprise the selection of any candidate unit(s) with arbitrary stream connections, we investigate the role of RL reward function and their impacts on exploring more complicated versus intensified process configurations. A sub-space search strategy is also developed to branch the combinatorial design space to accelerate the discovery of feasible process design solutions particularly when a large pool of candidate process units is selected by the user. The potential of the enhanced RL-assisted process design strategy is showcased via a hydrodealkylation example.

Water-gas shift membrane reactors (WGS-MRs) offer a pathway to affordable blue H2 generation/purification from gasified feedstock or reformed fuels. To exploit their cost benefits for blue hydrogen production, WGS-MRs’ performance needs to be optimized, which includes navigating the multidimensional design space (e.g., temperature, feed pressures, space velocity, membrane permeance and selectivity, catalytic performance). This work describes an equation-oriented modeling framework for WGS-MRs in the Pyomo ecosystem, with an emphasis on model scaling and multi-start initialization strategies to facilitate reliable convergence with nonlinear optimization solvers. We demonstrate, through sensitivity analysis, that our model converges rapidly (< 1 CPU second on a laptop computer) under a wide range of operating parameters (e.g., feed pressures of 1-3 MPa, reactor temperatures of 624-824 K, sweep-to-feed ratios of 0-0.5, and steam/carbon ratios of 1-5). Ongoing work includes (1) validation and calibration of the WGS-MR model using benchtop laboratory data and (2) design, intensification, and optimization of blue H2 processes using the WGS-MR model.

Direct air capture (DAC) of carbon dioxide is a promising technology to enable climate change mitigation. The liquid solvent DAC (LSDAC) process is one of the leading technologies being piloted. However, LSDAC uses a high-temperature regeneration process which requires a lot of thermal energy. Although current LSDAC designs incorporate pre-heat cyclones and a heat recovery steam generator to enable heat recovery, these do not maximize the use of the heat in the products of calcination. In this paper, a linear optimization model is developed to minimize energy cost in a LSDAC that is powered by renewable energy and natural gas. First, the material flow network is modified to include a heat exchanger (HX) and water supply to a proton exchange membrane (PEM) electrolyser. Mass and energy balance constraints are then developed to include the water flow as well as the energy balance at the PEM and the HX. Results show that about 911 tonnes of hydrogen could be produced over 336 hours of operation using a 136MW PEM. Further analysis reveals that hydrogen production is only prioritized if the value is higher than the cost of natural gas.

Chemical looping ammonia synthesis (CLAS) is a new ammonia synthesis method capable of efficiently synthesizing ammonia at atmospheric pressure. The low-pressure operation of CLAS systems could decrease the capital and operational costs of ammonia synthesis. Despite its early developmental stage, the use of standard process engineering equipment in CLAS makes it possible to reasonably assess its economic potential. In this study, we evaluated the technoeconomic potential of CLAS systems in comparison to a Haber-Bosch (HB) synthesis process in the context of green ammonia production. CLAS is more compatible with the separate nitrogen and hydrogen feedstocks used in green ammonia production, and cost savings from CLAS could improve the economic viability of green ammonia production. Ammonia synthesis loops were modeled in Aspen Plus and the levelized cost of ammonia (LCOA) of each system was calculated. Three CLAS systems; two high temperature and one low-temperature chemical loop, were compared to a conventional HB system of equivalent size. This study found that CLAS can reduce the synthesis cost by 90% and that the low temperature CLAS as more economically viable than the high temperature CLAS. The need for an external heater in the high temperature CLAS diminished any cost savings that would have been realized due to the low-pressure operation. This work highlights the potential of CLAS to reduce ammonia synthesis costs and emphasizes the need for further development of low-temperature CLAS systems.

The growing electrification of buildings and vehicles, while a natural step towards achieving global decarbonization, poses some challenges for the electric grid in terms of power consumption. One way of addressing them is by deploying onsite, behind-the-meter resources (BTMR), such as battery energy storage and solar PV generation. The optimal design of these systems, however, is a demanding task that depends on the integration of multiple complex subsystems. In this work, the optimal integrated design and dispatch of BTMR systems for retail buildings with electric vehicle fast charging stations is addressed. A framework is proposed, combining high-fidelity simulation (of buildings, electric vehicle fast charging stations, and BTMR), predictive control strategies with closed-loop implementation, and a derivative-free design method that explores parallelization and high-performance computing. Focus is given to the design layer, highlighting the effect of parallelization on the choice of the method, computational effort, and types of results. A case study of a big-box grocery store with an EV fast charging station is presented, and its optimal BTMR system is identified in terms of equipment sizes, costs (capital, utility, lifecycle, and levelized) and resiliency against outages, demonstrating great potential for real-world applications.

The chemical industry is actively pursuing energy transition and decarbonization through renewables and other decarbonization initiatives. However, navigating this transition is challenging due to uncertainties in capital investments, electricity costs, and carbon taxes. Adapting to decarbonization standards while preserving existing valuable infrastructure presents a dilemma. Early transitions may lead to inefficiencies, while delays increase the carbon footprint. This research proposes a framework to find an optimal retrofit decarbonization strategy for existing oil refineries. We start with a generic process flowsheet representing the refinery’s current configuration and operations, and consider various decarbonization alternatives. Through superstructure optimization, we identify the most cost-effective retrofit strategy over the next three decades to achieve decarbonization goals. We develop a Mixed-Integer Linear Programming (MILP) model, integrating simplified process equations and logical constraints to identify the most economical retrofit decarbonization strategy. The paper presents numerical results from the MILP model. Furthermore, the trends exhibited by the outcomes across various scenarios considering distinct electricity costs and carbon tax levels are presented. These results provide valuable insights into the economic feasibility of retrofit electrification strategies for decision-makers in the chemical industry.

Most integrated energy system (IES) optimization frameworks employ the price-taker approximation, which ignores important interactions with the market and can result in overestimated economic values. In this work, we propose a machine learning surrogate-assisted optimization framework to quantify IES/market interactions and thus go beyond price-taker. We use time series clustering to generate representative IES operation profiles for the optimization problem and use machine learning surrogate models to predict the IES/market interaction. We quantify the accuracy of the time series clustering and surrogate models in a case study to optimally retrofit a nuclear power plant with a polymer electrolyte membrane electrolyzer to co-produce electricity and hydrogen.

Biogas is a promising energy source for sustainable hydrogen production due to its high concentration of CH4. However, determining the optimal process configuration is challenging due to the uncertainty of the fed biogas composition and the sensitivity of the operating conditions. This necessitates early-stage evaluation of the biomass-to-hydrogen process’s performance, considering economics, energy efficiency, and environmental impacts. A data-driven model was introduced for early-stage assessment of hydrogen production from biogas without whole process simulation and optimization. The model was developed based on various biogas compositions and generated parameters for mass and energy balance. A database of unit processes was created using simulation models. Sensitivity analysis was performed under four techno-economic and environmental evaluation criteria: Unit Production Cost (UPC), Energy Efficiency (EEF), Net CO2 equivalent Emission (NCE), and Maximum H2 Production (MHP). The early-stage evaluation of the biogas-to-hydrogen process can guide the establishment of biogas utilization strategies and propose effective biogas enhancement process development solutions to respond to market disturbances.

This study focuses on optimizing solid oxide electrolysis cell (SOEC) systems for efficient and durable long-term hydrogen (H2) production. While the elevated operating temperatures of SOECs offer advantages in terms of efficiency, they also lead to chemical degradation, which shortens cell lifespan. To address this challenge, dynamic degradation models are coupled with a steady-state, two-dimensional, non-isothermal SOEC model and steady-state auxiliary balance of plant equipment models, within the IDAES modeling and optimization framework. A quasi-steady state approach is presented to reduce model size and computational complexity. Long-term dynamic simulations at constant H2 production rate illustrate the thermal effects of chemical degradation. Dynamic optimization is used to minimize the lifetime cost of H2 production, accounting for SOEC replacement, operating, and energy expenses. Several optimized operating profiles are compared by calculating the Levelized Cost of Hydrogen (LCOH).

The decarbonization of the society has a very high effect on the power grids as especially the energy generation will be almost completely shifted to CO2-neutral sources such as wind and solar. This implies significant design changes to the power grids and power systems, which lie between the electricity producers and consumers. In this paper, we discuss both the generation and consumer side, including the grid changes and required data exchange to support the transition.

The energy transition is driven both by the motivation to decarbonize as well as the decrease in cost of low carbon technology. Net-carbon neutrality over the lifetime of technology use can neither be quantitatively assessed nor realized without accounting for the flows of carbon comprehensively from cradle to grave. Sources of emission are disparate with contributions from resource procurement, process establishment and function, and material refining. The synergies between the constituent value chains are especially apparent in the mobility transition which involves (i) power generation, storage and dispatch, (ii) synthesis of polymeric materials, (iii) manufacturing of vehicles and establishment of infrastructure. Decision-making frameworks that can coordinate these aspects and provide cooperative sustainable solutions are needed. To this end, we present a multiscale modeling and optimization framework for the simultaneous resolution of the material and energy value chains. A case study focusing on the transition of mobility technology towards electric vehicles in Texas is presented. The key contributions of the proposed framework are (i) integrated network design and operational scheduling, (ii) the tracking of disparate emissions, (ii) simultaneous modeling of the material and energy supply chains, (iv) implementation on energiapy, a python package for the multiscale modeling and optimization of energy systems.

This study presents a comprehensive approach to optimizing hydrogen supply chain network (HSCN), focusing initially on Texas, with potential scalability to national and global regions. Utilizing mixed-integer nonlinear programming (MINLP), the research decomposes into two distinct modeling stages: broad supply chain modeling and detailed hub-specific analysis. The first stage identifies optimal hydrogen hub locations, considering county-level hydrogen demand, renewable energy availability, and grid capacity. It determines the number and placement of hubs, county participation within these hubs, and the optimal sites for hydrogen production plants. The second stage delves into each selected hub, analyzing energy mixes under variable solar, wind, and grid profiles, sizing specific production and storage facilities, and scheduling to match energy availability. Iterative refinement incorporates detailed insights back into the broader model, updating costs and configurations to converge upon an optimal supply chain design. This design encapsulates macro-level network configurations, including centralization versus decentralization strategies, transportation cost analysis, and carbon footprint assessment, as well as micro-level operational specifics like renewable energy contributions, facility scale, and energy portfolio management. The methodology’s robustness allows for strategic insights into hydrogen production facility siting, aligning with local energy resources and supply chain economics. This adaptable, multi-scale approach contributes to informed decision-making in the evolution of sustainable hydrogen-based energy systems, offering a roadmap for policy reforms and strategic supply chain development in diverse energy landscapes.

This research is dedicated to designing and economically evaluating the green ammonia supply chain, considering the fluctuating nature of renewable energy sources and energy demand across both hourly and seasonal variations. It also explores the impact of economies of scale and the delays associated with long-distance shipping to meet energy demands in a timely manner. These considerations require the formulation of a Mixed-Integer Nonlinear Programming model, further complicated by the necessity for a two-stage stochastic programming approach. We introduce a hierarchical optimization framework that utilizes a decomposition method to differentiate between one-time design decisions and subsequent operational choices. At the upper level, potential design solutions are identified through the Bayesian Optimization and Hyperband algorithm, which effectively navigates the non-linear challenges posed by economies of scale. The lower level then addresses a Mixed-Integer Linear Programming problem to independently assess the feasibility of each scenario. Our empirical analysis includes case studies of three potential international routes for transporting green ammonia to Korea. We contrasted our methodology with a hypothetical scenario that presupposes a constant supply of power and a stable demand for energy. Additionally, techno-economic analyses were conducted to evaluate the implications of the minimum operational limits for electrolyzers.

A novel process system which integrates an isopropanol-based chemical heat pump with a post-combustion carbon capture unit was proposed, designed, and analyzed. The system uses low-quality waste heat (~80°C) produced through the CO2 adsorption step of a carbon capture process and upgrades that heat to a higher temperature (~150°C) using the chemical heat pump. The chemical heat pump is powered mostly by the waste heat and requires only a small amount of electricity. The higher temperature heat produced can be used in the desorption stage of the CO2 capture process, displacing a portion of the existing fossil energy required. The energy and exergy performance characteristics of the chemical heat pump were computed using the results of a steady state simulation in a systems analysis. Using exergy cost correlations, the profitability of the chemical heat pump concept was estimated. It was found that for this particular configuration, the fossil energy load of desorption could be reduced by roughly 2.7% with very little parasitic electric load.

Modern power grids coordinate electricity production and consumption via multi-scale wholesale energy markets. Historically, levelized cost metrics were the de facto standard for techno-economic analyses of energy systems and comparison of technology options. However, these metrics neglect the complexity of energy infrastructure including the time-varying value of electricity. An emerging alternative is multi-period optimization, which considers the locational marginal price of electricity as input data (parameters). In this work, we present a general interface for multi-period optimization with time-varying energy prices to facilitate rapid analysis and comparison of potential energy systems models. The PriceTakerModel class is written in the IDAES®-PSE platform and allows users to generate a multi-period, price-taker model instance, as well as automatically generate common operational constraints for their model, such as start-up and shutdown. We show this interface successfully generates multi-period price-taker models, facilitates model discrimination, and aids in analyzing various technologies for deployment in unique energy markets.

As the United States continues efforts to decarbonize the power and transportation sectors, significant challenges associated with the reliance of clean energy technologies on rare earth elements (REEs) will have to be overcome. One potential approach for increasing the supply of these elements is to extract REEs from end-of-life (EOL) hard disk drives (HDDs). HDDs contain neodymium and praseodymium, which are among the most important REEs for the clean energy transition, as they are crucial to producing the permanent magnets needed for wind turbines and electric vehicles. Here, we propose a superstructure-based approach to find the optimal pathway for recovering REEs from EOL HDDs. The superstructure was optimized by maximizing the net present value (NPV) over 15 years. Projected prices for commercial rare earth oxides and the projected amount of EOL HDDs in the U.S. were estimated and used in the model. These projections were used to establish the base case optimal result, assuming that the plant recycles 60% of personal computers EOL HDDs in the U.S. each year. The model was then expanded to consider the recycling of EOL HDDs generated before the beginning of plant production. Next, a sensitivity analysis was conducted to evaluate the impact of different parameters on the venture’s profitability and the optimal processing pathway. Combined, these results offer both valuable insights into the economic viability of REE recycling extraction and a method for performing similar analyses in the future.

Solid oxide cells (SOCs) are a promising dual-mode technology that generates hydrogen through high-temperature water electrolysis and generates power through a fuel cell reaction that consumes hydrogen. Reversible operation of SOCs requires a transition between these two modes for hydrogen production setpoints as the demand and price of electricity fluctuate. Moreover, a well-functioning control system is important to avoid cell degradation during mode-switching operation. In this work, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional integral (PI) control strategies, while ramping between the modes of hydrogen and power production. While both control methods provide similar performance in many metrics, NMPC significantly reduces cell thermal gradients and curvatures (mixed spatial temporal partial derivatives) during mode switching. A dynamic process flowsheet of the reversible SOC system was developed in the open-source, equation-based IDAES modeling framework. Our IDAES dynamic simulation results show that NMPC can ramp the SOC system between hydrogen and power production targets within short mode switching times. Moreover, NMPC can comply with operating limits in the SOC system more effectively than PI, and only NMPC can directly enforce user-specified limits for mixed spatial temporal partial derivatives of temperature. This allows for management of the trade-off between operating efficiency and cell degradation, which is dependent on these temperature curvatures.

Increasing wind and solar electricity generation in power systems increases temporal variability in electricity prices which incentivizes the development of flexible processes for electricity generation and electricity-based fuels/chemicals production. Here, we develop a computational framework for the integrated design and optimization of multi-product processes interacting with the grid under time-varying electricity prices. Our analysis focuses on the case study of nuclear-based hydrogen (H2) and electricity generation, involving nuclear power plants (NPP) producing high temperature heat and electricity coupled with a high temperature steam electrolyzers (HTSE) for H2 production. The ability to co-produce H2 along with nuclear is widely seen as critical to improving the economics of nuclear energy technologies. To that end, our model focuses on evaluating the least-cost design and operations of the NPP-HTSE system while accounting for: a) power consumption variation with current density for the HTSE and the associated capital and operating cost trade-off, b) heat integration between NPP and HTSE and c) temporal variability in electricity prices and their impact on plant operations to meet a baseload hydrogen demand. Instead of formulating a monolithic optimization model, which would be computationally expensive, we propose a decomposition approach that reformulates the original problem into three sub-problems solved in an iterative manner to find near-optimal solutions. Through a numerical case study, we demonstrate the potential synergies of NPP and HTSE integration under alternative electricity price scenarios. This synergy is measured via the metric of relative breakeven H2 selling price that accounts for the opportunity cost of reduced electricity sales from H2 co-production.

With growing concern over the effects of green-house gas emissions, there has been an increase in emission-reducing policies by governments around the world, with over 70 countries having set net-zero emission goals by 2050-2060. These are ambitious goals that will require large investments into the expansion of renewable and low-carbon technologies. The decisions about which technologies should be invested in can be difficult to make since they are based on information about the future, which is uncertain. When considering emerging technologies, a source of uncertainty to consider is how the costs will develop over time. Learning curves are used to model the decrease in cost as the total installed capacity of a technology increases. However, the extent to which the cost decreases is uncertain. To address the uncertainty present in multiple aspects of the energy sector, multistage stochastic programming is employed considering both exogenous and endogenous uncertainties. It is observed in scenarios when costs of emerging technologies decrease to competitive prices, decisions to invest in these technologies should be made earlier to allow for the decrease in costs to be taken advantage of in the future. Noticeably, a wider variety of energy and biofuel technologies are invested in when uncertainty is included. Interestingly, it is also seen that there are lower carbon emissions when uncertainty is considered.

Thermal separation processes, such as distillation, play a pivotal role in the chemical and petrochemical sectors, constituting a substantial portion of the industrial energy consumption. Consequently, owing to their huge application scales, these processes contribute significantly to greenhouse gas (GHG) emissions. Decarbonizing distillation units could mitigate carbon emissions substantially. Heat Pumps (HP), that recycle lower quality heat from the condenser to the reboiler by electric work present a unique opportunity to electrify distillation systems. In this research we try to answer the following question in the context of multi-component distillation – Do HPs actually reduce the effective fuel consumption or just merely shift the fuel demand from chemical industry to the power plant? If they do, what strategies consume minimum energy? To address these inquiries, we construct various simplified surrogate and shortcut models designed to effectively encapsulate the fundamental physics of the system. These models are integrated into a superstructure-based Mixed-Integer Nonlinear Programming (MINLP) formulation, which is amenable to global optimization algorithms aimed at minimizing the effective fuel consumption of the system. Moreover, through the examination of a toy 4-component alcohol separation example, we demonstrate how HPs can notably reduce carbon emissions, even when the consumed electricity is generated by burning fossil fuels.

As a low-carbon fuel, feedstock, and energy source, hydrogen is expected to play a vital role in the decarbonization of high-temperature process heat during the pyroprocessing steps of clinker production in cement manufacturing. However, to accurately assess its potential for reducing CO2 emissions and the associated costs in clinker production applications, a techno-economic analysis and a study of facility-level CO2 emissions are necessary. Assuming that up to 20% hydrogen can be blended in clinker fuel mix without significant changes in equipment configuration, this study evaluates the potential reduction in CO2 emissions (scopes 1 and 2) and cost implications when replacing current carbon-intensive fuels with hydrogen. Using the direct energy substitution method, we developed an Excel-based model of clinker production, considering different hydrogen–blend scenarios. Hydrogen from steam methane reformer (gray) and renewable-based electrolysis (green) are considered as sources of hydrogen fuel for blend scenarios of 5%–20%. Metrics such as the cost of cement production, facility-level CO2 emissions, and cost of CO2 avoided were computed. Results show that for hydrogen blends (gray or green) between 5% and 20%, the cost of cement increases by 0.6% to 16%, with only a 0.4% to 6% reduction in CO2 emissions. When the cost of CO2 avoided was computed, the extra cost required to reduce CO2 emissions is $229 to $358/ metric ton CO2. In summary, although green hydrogen shows promise as a low-carbon fuel, its adoption for decarbonizing clinker production is currently impeded by costs.

Hydrogen, as a clean and versatile energy carrier, holds immense promise for addressing the world’s growing energy and environmental challenges. However, hydrogen-based energy systems face challenges related to efficient storage methods, energy-intensive production, refueling processes, and overall cost-effectiveness. To solve this problem, a superstructure was developed that integrates overall technologies related to hydrogen energy transportation. This study synthesizes process pathways for hydrogen energy transportation method including energy carrier production, storage, and refueling, based on the developed superstructure. The techno-economic analysis was conducted to evaluate the performance of each transportation pathway and compare it with conventional fossil fuel transportation system. Process performance criteria, including unit production cost (UPC), energy efficiency (EEF), and net CO2 equivalent emissions (NCE), serve as indicators for process performance. By comparing technological pathways, we can propose the most economically and environmentally optimal energy refueling route. Additionally, sensitivity analyses were performed on various external factors, identifying influential variables in the decision-making process for hydrogen production, storage, and refueling strategies, while also elucidating technological limitations.

Post-combustion carbon capture technologies have the potential to contribute significantly to achieving the environmental goals of reducing CO2 emissions in the short term. However, these technologies are energy and cost-intensive, and the variability of flue gas represents important challenges. The optimal design and optimization of such systems are critical to reaching the net zero and net negative goals, in this context, the use of computer-aided process design can be very effective in overcoming these issues. In this study, we explore the implementation of carbon capture technologies within an industrial complex, by considering the pooling of CO2 streams. We present an optimization formulation to design carbon capture plants with the goal of enhancing efficiency and minimizing the capture costs. Capital and operating costs are represented via surrogate models (SMs) that are trained using rigorous process models in Aspen Plus, each data point is obtained by solving an optimization problem in Aspen Plus equation-oriented approach. Since selecting the functional form of the surrogate model is crucial for the solution performance; we study different SM approaches (i.e., ALAMO, kriging, radial basis function, polynomials, and artificial neural networks) and analyze their impact on solver performance. Numerical results show the computational advantage of using ALAMO while highlighting the increased complexity of using ANN and kriging to formulate optimization problems. Regarding the pooling of CO2 streams, the optimal designs for the network are not trivial, thus showing the importance of addressing the problem systematically.

An important measure to achieve global reduction in CO2 emissions is CO2 capture, transport, and storage. The deployment of CO2 capture requires the development of a shared CO2 transport infrastructure, where CO2 can be transported with different transport modes. Furthermore, the cost of CO2 transport can be subject to significant economies of scale effects with respect to the amount of CO2 transported, also mentioned as clustering effects. Therefore, optimizing the shared infrastructure of multiple CO2 sources can lead to significant reductions in infrastructure costs. This paper presents a novel formulation of the clustered CO2 transport network. The Markov Decision Process formulation defined here allows for more detailed modeling of non-linear, discrete transport costs and increased geographical resolution. The clustering effects are modeled through cooperative multi-agent interactions. A multi-agent, reinforcement learning-based algorithm is proposed to optimize the shared transportation network, with examples illustrating the results of the method.

As a promising electricity storage system, Liquid Air Energy Storage (LAES) has the main advantage of being geographically unconstrained. LAES has a considerable potential in energy efficiency improvement by utilizing compression heat and integrating with other systems. In this work, the Stirling Engine (SE) is introduced to improve the energy efficiency of the LAES system. Three LAES-SE systems are modelled in Aspen HYSYS and optimized by the Particle Swarm Optimization (PSO) algorithm. The studied systems include (i) the LAES system with 3 compressors and 3 expanders (3C+3E) using an SE to recover the compression heat, (ii) the 3C+3E LAES system with LNG regasification and SE, and (iii) the 3C+3E LAES system with solar energy and SE. The optimization results show that the Round-Trip Efficiencies (RTEs) of the LAES-SE system and the LNG-LAES-SE systems are 68.2% and 73.7%, which are 3.2% and 8.7% points higher than the basic 3C+3E LAES-ORC system with an RTE of 65.0%. For the Solar-LAES-SE system, a revised RTE and the economic performance with solar energy input are optimized. The traditional RTE for the Solar-LAES-SE system, which only accounts for power produced and consumed in the discharging and charging sections, is 189% and 173% respectively, when optimized with respect to energy and economic performances. The revised RTE accounts for the integrated external sources, avoiding the confusing result that the RTE becomes larger than 100%. The energy and economic performances of the Solar-LAES-SE system are proved to be the best compared with the Solar-LAES-ORC and Solar energy directly heated-LAES systems.

We study the impact of switching from combustion heating to electric heating in processes comprising high temperature reaction/separation sequences, where the heat supporting the reaction(s) is substantially provided by combusting a reaction byproduct (fuel gas). A canonical process structure is de?ned. It is shown that the conventional combustion- based process presents signi?cant interactions. An asymptotic analysis is utilized to investigate and compare the dynamic responses of the conventional and electric process configurations. It is demonstrated that the dynamic behavior of the two processes exhibits two timescales, with the faster corresponding to the evolution of the temperatures of the units with high heat duty, and the slow time scale capturing the variables involved in the material balance. A simpli?ed ethylene cracking process example is used to demonstrate these findings.

Biogas is often considered as a source of renewable energy, for heat and power production. However, biogas has greater promise as a source of concentrated CO2 in addition to methane, making it a rich supply of carbon and hydrogen for the generation of fuel and chemicals. In this work, we use the concept of attainable region in the enthalpy-Gibbs free energy space to identify opportunities for effective biogas valorization that maximizes the conversion of CO2. The AR concept allows us to study a chemical process without knowing the exact reaction mechanism that the species in the process use. Deriving Material Balance equations that relate a reactive process’s output species to its input species is sufficient to identify process limits and explore opportunities to optimize its performance in terms of material, energy, and work. The conversion of biogas to valuable products is currently done in two steps; the high temperature and endothermic reformer step, followed by the low temperature exothermic synthesis step. We demonstrate, using Aspen Simulation, that energy integration, both heat and work, between the two steps is crucial to achieving a substantial amount of CO2 conversion. We also show how a heat pump configuration can be utilized to integrate energy between the reformer and synthesis steps against the temperature gradient by integrating external renewable energy.

The transition to net-zero greenhouse gas emissions requires a rapid redesign of energy systems. However, the redesign may shift environmental impacts to other categories than climate change. To assess the sustainability of the resulting impacts, the planetary boundaries framework provides absolute limits for environmental sustainability. This study uses the planetary boundaries framework to assess net-zero sector-coupled energy system designs for absolute environmental sustainability. Considering Germany as a case study, we extend the common focus on climate change in sustainable energy system design to seven additional Earth-system processes crucial for maintaining conditions favorable to human well-being. Our assessment reveals that transitioning to net-zero greenhouse gas emissions reduces many environmental impacts but is not equivalent to sustainability, as all net-zero designs transgress at least one planetary boundary. However, the environmental impacts vary substantially between net-zero designs, highlighting that design choices exist to address transgressions of planetary boundaries.

In view of achieving the decarbonization target, green hydrogen is commonly regarded as the alternative capable of reducing the share of fossil fuels. Despite its wide application as a chemical on industrial scale, hydrogen utilization as an energy vector still suffers from unfavorable economics, mainly due to its high cost of production, storage and transportation. To overcome the last two of these issues, different hydrogen carriers have been proposed. Hydrogen storage and transportation through these carriers involve: 1. the carrier hydrogenation, exploiting green hydrogen produced at the loading terminal, where renewable sources are easily accessible, 2. the storage and transportation of the hydrogenated species and 3. its subsequent dehydrogenation at the unloading terminal, to favour H2 release. Although there is a number of studies in literature on the economic feasibility of hydrogen transport through different H2 vectors, very few of them delve into the technical evaluation of the hydrogen value chain. From the process design point of view, the hydrogenation and dehydrogenation stages are of paramount importance, considering that they are the cost drivers of the whole system. This work aims to address this gap by presenting a systematic methodology to technically analyse different hydrogen vectors. For the sake of example, ammonia and dibenzyltoluene are considered. Weaknesses of the overall value chain are pointed out, to understand where to focus research efforts for future process intensification.

The placement of wind turbines is a crucial design element in wind farms, given the energy losses resulting from the wake effect. Despite numerous studies addressing the Wind Farm Layout Optimization (WFLO) problem, considering multiple directions to determine wind turbine spacing and layout remains limited. However, relying solely on one predominant direction may lead to overestimating energy production, and loss of energy generation. This work introduces a novel mathematical programming optimization framework to solve the WFLO problem, emphasizing the wind energy’s nonlinear characteristics and wake effect losses. Comparisons with traditional layout approaches demonstrate the importance of optimizing wind farm layouts during the design phase. By providing valuable insights into the renewable energy sector, this research aims to guide future wind farm projects towards layouts that balance economic considerations with maximizing energy production.

The synthesis of energy systems necessitates simultaneous optimization of both design and operation across all components within the energy system. In real-world applications, this synthesis poses a mixed-integer nonlinear programming (MINLP) problem, considering nonlinear behaviours such as investment cost curves and part-load performance. The complexity increases further when seasonal energy storage is involved, as it requires temporal coupling of the full time series. Although numerous solution approaches exist to solve the synthesis problems simplified by linearization, methods for solving a full-scale problem are currently missing. In this work, we introduce a rigorous method, RiNSES4, to manage the nonlinear aspects of energy system synthesis, particularly focusing on long-term time-coupling constraints. RiNSES4 calculates the upper and lower bounds of the initial synthesis problem in two separate branches. The proposed method yields feasible solutions through upper bounds, while evaluating the solution quality via lower bounds. The solution quality is iteratively enhanced by increasing the resolution for calculating upper bounds and tightening the relaxations for computing lower bounds. Both branches work simultaneously and independently, with their outcomes compared after each iteration within each branch. The iterations continue until a predefined optimality gap is reached. We apply RiNSES4 to design a photovoltaic and battery energy system, considering the seasonality of both energy supply and demand sides. In comparison with a state-of-the-art commercial solver, RiNSES4 enables to solve the MINLP synthesis problem with great temporal detail and shows high potential.

This study delves into the development and examination of various mathematical models for conventional steam-methane reforming (SMR) reactors, establishing a foundational basis for an electrified SMR reactor design. Distinct mathematical models with different scales and dimensions are derived. A basic 1D-fluid, 0D-catalyst (1D-0D) pseudo-homogeneous model is validated with plant data, and progressively advanced to a 2D-0D model considering radial transfer, then further extended to a rigorous 2D-1D model considering transfer phenomena between catalyst particle and fluid. Simulation cases are conducted under uniform design parameters, heat source and operation conditions. Comparative analyses focus on several key performance aspects, including temperature, reaction rate distribution, and outlet characteristics such as temperature, pressure, flow rate, composition and CH4 conversion. The models effectively describe the industrial SMR reactor behavior. Influences of scale and dimension of mathematical model on reactor performance are highlighted. The rigorous 2D-1D model is identified as the most suitable model for adapting to electrified reactor configurations due to its precise capture of transfer phenomena and detailed illustration of both fluid and catalyst behaviors.

Initial design stages are inherently complex and often lack comprehensive information, posing challenges in evaluating sustainability metrics. Machine Learning (ML) emerges as a valuable solution to address these challenges. ML algorithms, particularly effective in predicting environmental impacts of new chemicals with limited data, enable more informed decisions in sustainable design. This study focuses on employing ML for predicting the environmental impacts related to human health, ecosystem quality, climate change, and resource utilization to aid in early-stage environmental impact assessment of chemical processes. The effectiveness of the ML algorithm, eXtreme Gradient Boosting (XGBoost) tested using a dataset of 350 points, divided into training, testing, and validation sets. The study also includes a practical application of the model in a cradle-to-cradle LCA of N-Methylpyrrolidone (NMP), demonstrating its utility in sustainable chemical process design. This approach signifies a significant advancement in the early stages of process design, highlighting the potential of ML in enhancing environmental sustainability in the chemical industry.

Circular systems design is an emerging approach for promoting sustainable development. Despite its perceived advantages, the characterization of circular systems remains loosely defined and ambiguous. This work proposes a network optimization framework that evaluates three objective functions related to economic and environmental domains and employs a Pareto analysis to illuminate the trade-offs between objectives. The US polyethylene terephthalate (PET) value chain is selected as a case study and represented via a superstructure containing various recycling pathways. The superstructure optimization problems are modeled as a mixed integer linear program (MILP) and linear programs (LPs), implemented in Pyomo, and solved with CPLEX for a one-year assessment horizon. Solutions to the circular economy models are then compared to the corresponding solutions of linear economy models. Preliminary results show that the optimal circular network is advantageous over the optimal linear network for all objectives subject to the current market supply of raw materials and the total cost of production. However, when considering the present chemical processing infrastructure of the US economy and unrestricted biomass feedstock availability, a linear economy is favorable as an outcome of low operating cost and carbon sequestration.

Recent advances in process design for solvent-based, post-combustion capture (PCC) processes, such as the Piperazine/Advanced Flash Stripper (PZ/AFS) process, have led to a reduction in the energy required for capture. Even though PCC processes are progressively improving in Technology Readiness Levels (TRL), with a few commercial installations, incorporating carbon capture adds cost to any operation. Hence, cost reduction will be instrumental for proliferation. The aim of this work is to improve process economics through optimization and to identify the parameters in our economic model that have the greatest impact on total cost to build and operate these systems. To that end, we investigated changes to the optimal solution and the corresponding cost of capture considering changes in the price of utilities and solvent. We found that changes in solvent price had the most effect on the cost of capture. However, re-optimizing the designs in the event of price changes did not lead to significant improvements in the case of piperazine, cooling water and electricity, whereas re-optimizing for changes in steam prices lead to yearly saving of 3.8%. These findings show that the design choices obtained at the nominal optimal solution are insensitive to utility price changes except for the case of steam and that there is a need for altered designs for locations where the steam prices are different.

The overarching goal of limiting the increase in global temperature to = 2.0° C likely requires both decarbonization and defossilization efforts. Direct air capture (DAC) and CO2 electrolysis stand out as promising technologies for capturing and utilizing atmospheric CO2. In this effort, we explore the details of designing and operating an integrated DAC-electrolysis process by examining some key parameters for economic feasibility. We evaluate the gross profit and net income to find the most appropriate capacity factor, average electricity price, syngas sale price, and CO2 taxes. Additionally, we study an optimistic scenario of CO2 electrolysis and perform a sensitivity analysis of the CO2 capture price to elucidate the impact of design decisions on the economic feasibility. Our findings underscore the necessity of design improvements of the CO2 electrolysis and DAC processes to achieve reasonable capacity factor and average electricity price limits. Notably, CO2 taxes and tax credits in the order of $400 per t-CO2 or greater are essential for the economic viability of the optimistic DAC-electrolysis route, especially at competitive syngas sale prices. This study serves as a foundation for further work on designing appropriate power system models that integrate well with the presented air-to-syngas route.

Sustainable and economically viable plastic recycling methodologies are vital for addressing the increasing environmental consequences of single-use plastics. In this study, we evaluate the plastic waste management value for the state of Georgia, US and investigate the potential of introducing novel depolymerization methods within the network. An equation-based formulation is developed to identify the optimum supply-chain design given the geographic location of existing facilities. Chemical recycling technologies that have received increasing attention are evaluated as candidate technologies to be integrated within the network. The optimum supply-chain design is selected based on environmental and economic objectives. The designed network of pathways uses a mix of different technologies (chemical and mechanical recycling) in a way that are both economically environmentally sound.

2,3 butanediol (BDO) has garnered recent interest due to the high titer concentrations that can be obtained through biochemical routes and its potential for efficient conversion into long-chain hydrocarbons. BDO separation, however, is challenging given its low volatility and high affinity towards water. In this study, two BDO separation pathways were compared, single distillation and combined simulated moving bed (SMB) adsorption with distillation. The separations were incorporated into a 2018 biorefinery design developed by the National Renewable Energy Laboratory (NREL) to produce renewable fuels from corn stover, with BDO as an intermediate and adipic acid as the co-product. The comparison was performed on the basis of sustainability, using lifecycle greenhouse gas (GHG) emissions as the metric. It was found that using a single distillation column gives GHG emissions of 48 gCO2e/MJ for the renewable fuel. This is lower than 93 gCO2e/MJ for petroleum fuel but is higher compared to the SMB-based process which achieves 21 gCO2e/MJ. Additionally, the minimum fuel selling price (MFSP) of each pathway was computed. Single distillation gave a minimum MFSP of $2.54/GGE (gallon of gasoline equivalent) of fuel, while SMB reached $2.45/GGE. The SMB’s MFSP is lower than the Department of Energy’s (DOE) target of $2.50/GGE, demonstrating this pathway is both an economic and sustainable alternative and a sound separation candidate that can enable the viability of the entire biorefinery. The effect of BDO fermentation titer was also considered through a sensitivity analysis.

In this paper, we study the design optimization of methanol production with the goal of minimizing methanol production cost. One challenge of methanol production via carbon dioxide (CO2) hydrogenation is the reduction of operating costs. The simulation of methanol production is implemented within the Aspen HYSYS simulator. The feeds are pure hydrogen and captured CO2. The process simulation involves a single reactor and incorporates recycling at a ratio of 0.995. The methanol production cost is determined using an economic analysis. The cost includes capital and operating costs, which are determined through the equations and data from the capital equipment-costing program. The decision variables are the pressure and temperature of the reactor contents. The optimization problem is solved using a derivative-free algorithm, pyBOUND, a Python-based black-box model optimization algorithm that uses random forests (RFs) and multivariate adaptive regression splines (MARS). The predicted minimum methanol production cost by pyBOUND is $1396.56 per tonne of methanol, which corresponds to the pressure of 68.82 bar and temperature of 192.23°C while the actual cost is $1393.95 per tonne of methanol at these conditions. The cost breakdown of methanol production is 75% hydrogen price, 11% utility cost, 8% capital cost, 5% carbon dioxide price, and 1% operating cost.

One of the main challenges to support life in space is the development of sustainable, circular processes that reduce the high cost of resupply missions. Space biomanufacturing is an emerging paradigm that aims to reduce the need for resources, enabling on-demand manufacture of products. The cost of installing biomanufacturing systems in space depends on the cost of transporting the system components, which is directly proportional to their mass/weight. From this perspective, the system mass is a critical factor that dictates process design, and this has important implications in how we can approach such design. For instance, mass constraints require circular use of resources and tight process integration (to minimize resupply) and restricts the type of resources and equipment needed. In this work, we evaluate the lactic acid bioproduction design using Escherichia coli, Saccharomyces cerevisiae, and Pichia pastoris. We use the Equivalent System Mass (ESM) metric as a key design measure. ESM allows the quantification of different physical properties of the system in a common mass basis. Our analysis reveals that 97.7 kg/year of lactic acid can be produced using Saccharomyces cerevisiae in a 10 L stainless steel fermenter. Furthermore, considering that stainless steel is the design material and quantifying the mass of 1 g/cm2 of shielding material, the total system mass was 19 kg. This modeling framework also identified the critical system elements responsible for the highest system mass and launch cost. Overall, our analysis reveals how focusing on system mass can bring new design perspectives that can aid the design of traditional manufacturing systems.

Aiming to mitigate the environmental impact derived from fossil fuels, we propose an integrated carbon capture-biomass gasification process is proposed to produce low-carbon hydrogen as an alternative energy carrier. The process begins with the pre-treatment of empty fruit bunches (EFB), involving grinding, drying, torrefaction, and pelletization. The resulting EFB pellet is then fed into a dual gasifier, followed by a catalytic cracking of tar and water gas shift reaction to produce syngas, aiming to increase its H2 to CO ratio. Subsequently, we explore two alternatives (DEPG and MEA) for syngas upgrading by removing CO2. Finally, a PSA system is modeled to obtain H2 at 99.9% purity. The pre-treatment stage densifies the biomass from an initial composition (%C 46.47, %H 6.22, %O 42.25) to (%C 54.10, %H 6.09, %O 28.67). The dual gasifier operates at 800°C, using steam as a gasifying agent. The resulting syngas has a volume concentration (%CO 20.0, %CO2 28.2, %H2 42.2, %CH4 5.9). Next stages of the process focus on removing the CO2 and increased H2 through catalytic reactions from the syngas. Thus, the DEPG carbon capture process can decrease the CO2 concentration to 2.9%, increasing the hydrogen to 95.6% in volume. In contrast, the MEA process reduces the concentration of CO2 to 5.2% and increases the concentration of H2 to 93.1%. Moreover, we estimate a levelized costs of hydrogen (LCOH) and carbon capture cost for each method (DEPG and MEA) (LCOC) and CO2 avoided (LCCA). LCOH: 3.05 USD/kg H2, LCOC: 92 and 59 USD/t CO2 and 183 and 119 USD/t CO2, for DEPG and MEA respectively.

Plastics are widely used for their affordability and versatility across many consumer and industrial applications. However, the end-of-life (EoL) management stage can often lead to releasing hazardous chemical additives and degradation products into the environment. The increasing demand for plastics is expected to increase the frequency of material releases throughout the plastic EoL management activities, creating a challenge for policymakers, including ensuring proper material segregation and disposal management and increasing recycling efficiency and material reuse. This research designed a Python-based EoL plastic management tool to support decision-makers in analyzing the holistic impacts of potential plastic waste management policies. The constructed tool was developed to reduce the complexity of material flow analysis calculations, estimating material releases, and environmental impacts. The utility of the tool was tested through the hypothetical nationwide adoption of an extended producer responsibility (EPR) program. The decision-making capability of the tool can facilitate the prediction of long-term outcomes, offering technical knowledge and insight for policymakers seeking to mitigate the environmental and health impacts of plastic pollution.

To mitigate the effects of catastrophic failure while maintaining resource and production efficiencies, energy systems need to be designed for resilience and sustainability. Conventional approaches such as redundancies through backup processes or inventory stockpiles demand high capital investment and resource allocation. In addition, responding to unexpected “black swan” events requires that systems have the agility to transform and adapt rapidly. To develop targeted solutions that protect the system efficiently, the supply chain network needs to be considered as an integrated multi-scale system incorporating every component from individual process units all the way to the whole network. This approach can be readily integrated with analogous multiscale approaches for sustainability, safety, and intensification. In this work, we bring together classical supply chain resilience with process systems engineering to leverage the multi-scale nature of energy systems for developing resilience enhancement strategies that are resource-efficient. In particular, we adapt qualitative risk analysis methods to uncover critical system components and major vulnerabilities to guide resource allocation decisions. To account for these vulnerabilities, we explore the feasible region of operation around each node of the supply chain. An optimization formulation is devised to generate multiscale alternative. The approach is demonstrated through a case study involving the production of biofuels, demonstrating the range of adaptation strategies possible when process-level strategies are incorporated into overall supply chain design.

We study the design and optimization of a multicomponent seawater desalination process with zero liquid discharge (ZLD). The designed process is highly integrated with multiple sub processing units that include humidification-dehumidification, Lithium Bromide absorption chiller, multi-effect evaporators, mechanical vapor compression, and crystallization. Aspen Plus software with E-NRTL and SOLIDS thermodynamic packages are used for modeling and simulation of desalination and crystallization units, respectively. In addition to this, we use data-driven optimization to find the best operating condition (i.e., the temperature of the last effect evaporator) that minimizes the overall energy consumption of the designed plant with an output constraint imposed on the mass fraction of salts going to the ZLD system should be greater than 20 wt.% to achieve the ZLD goal. We use a local sample-based data-driven optimizer, Nonlinear Optimization with the Mesh Adaptive Direct Search (NOMAD) algorithm, to perform constrained simulation-based optimization. The results show that at the optimized temperature (71.58 °C), our design produces 1777 kg/hr drinking water with an energy consumption of 536 kW in comparison to 580 kW of energy consumption for the same plant output in the base case design (not optimized). Thus, data-driven optimization of the evaporator temperature improves the overall energy consumption by 7.5% and achieves higher desalination efficiency. Further, the integration of the crystallizer unit into the overall desalination process allows us to produce about 43 kg/h of NaCl and achieve ZLD.

Critical minerals are essential to the future of clean energy, especially energy storage, electric vehicles, and advanced electronics. In this paper, we argue that process systems engineering (PSE) paradigms provide essential frameworks for enhancing the sustainability and efficiency of critical mineral processing pathways. As a concrete example, we review challenges and opportunities across material-to-infrastructure scales for process intensification (PI) with membranes. Within critical mineral processing, there is a need to reduce environmental impact, especially concerning chemical reagent usage. Feed concentrations and product demand variability require flexible, intensified processes. Further, unique feedstocks require unique processes (i.e., no one-size-fits-all recycling or refining system exists). Membrane materials span a vast design space that allows significant optimization. Therefore, there is a need to rapidly identify the best opportunities for membrane implementation, thus informing materials optimization with process and infrastructure scale performance targets. Finally, scale-up must be accelerated and de-risked across the materials-to-process levels to fully realize the opportunity presented by membranes, thereby fostering the development of a circular economy for critical minerals. Tackling these challenges requires integrating efforts across diverse disciplines. We advocate for a holistic molecular-to-systems perspective for fully realizing PI with membranes to address sustainability challenges in critical mineral processing. The opportunities for PI with membranes are excellent applications for emerging research in machine learning, data science, automation, and optimization.

Some of the most highly trusted and ubiquitous process simulators have solution methods that are incompatible with algorithms designed for equation-oriented optimization. The natively unconstrained Efficient Global Optimization (EGO) algorithm approximates a black-box simulation with kriging surrogate models to convert the simulation results into a reduced-order model more suitable for optimization. This work evaluates several established constraint-handling approaches for EGO to compare their accuracy, computational efficiency, and reliability using an example simulation of an amine post-combustion carbon capture process. While each approach returned a feasible operating point in the number of iterations provided, none of them effectively converged to a solution, exploring the search space without effectively exploiting promising regions. Using the product of expected improvement and probability of feasibility as next point selection criteria resulted in the best solution value and reliability. Constraining probability of feasibility while solving for the next sample point was the least likely to solve, but the solutions found were most likely to be feasible operating points.

Utilizing sustainable energy sources is crucial for expanding the range of solutions available to meet the growing energy demand and reducing reliance on environmentally damaging and depleting conventional fuels. Biosolids, a type of biomass, are generated as secondary effluent during wastewater treatment process in municipal and industrial sites. These solids possess the potential to serve as a sustainable energy source due to their richness of carbon. For an extended period, biosolids have been landfilled, even though it can be considered a wasteful use of a precious resource and a possible mean for contamination to the food supply chain. This has served as an extra impetus to investigate the potential for harnessing the capabilities of these substances. While many research studies have looked at different ways to put biomass waste to use, very little has been written on biosolids, especially those derived from industrial sources. This research assesses the feasibility of transforming GTL derived biosolids into value-added commodities that can serve as raw materials in chemical manufacturing or be employed energy generation. The study primarily examines widely recognized thermal conversion processes, pyrolysis and gasification. An evaluation is carried out to analyze the economic, technological, and environmental aspects of the treatment methods utilizing these technologies. The aim is to demonstrate the potential of GTL biosolids conversion and to determine associated costs and environmental impacts. The ASPEN simulation tool is utilized to model thermal treatment pathways, allowing for the generation of economic and environmental estimations for each route.

Modern mechanical recycling infrastructure for plastic is capable of processing only a small subset of waste plastics, reinforcing the need for parallel disposal methods such as landfilling and incineration. Emerging pyrolysis-based chemical technologies can “upcycle” plastic waste into high-value polymer and chemical products and process a broader range of waste plastics. In this work, we study the economic and environmental benefits of deploying an upcycling infrastructure in the continental United States for producing low-density polyethylene (LDPE) and polypropylene (PP) from post-consumer mixed plastic waste. Our analysis aims to determine the market size that the infrastructure can create, the degree of circularity that it can achieve, the prices for waste and derived products it can propagate, and the environmental benefits of diverting plastic waste from landfill and incineration facilities it can produce. We apply a computational framework that integrates techno-economic analysis, life cycle assessment, and value chain optimization. Our results demonstrate that the infrastructure generates an economy of nearly 20 billion USD and positive prices for plastic waste, opening opportunities for compensation to residents who provide plastic waste. Our analysis also indicates that the infrastructure can achieve a plastic-to-plastic degree of circularity of 34% and remains viable under various external factors (including technology efficiencies, capital investment budgets, and polymer market values). Finally, we present significant environmental benefits of upcycling over alternative landfill and incineration waste disposal methods, and comment on ongoing work expanding our modeling methodology to other chemical upcycling pathway case studies, including hydroformylation of specific plastics to chemicals.

There is a need to drastically reduce greenhouse gas emissions. While significant progress has been made in electrifying transport, heavy duty transportation and aviation are not likely to be capable of electrification in the near term, spurring significant research into biofuels. When coupled with carbon capture and storage, biofuels can achieve net-negative greenhouse gas emissions via many different conversion technologies such as fermentation, pyrolysis, or gasification to produce ethanol, gasoline, diesel, or jet fuel. However, each pathway has a different efficiency, capital and operating costs, and potential for carbon capture, making the optimal pathway dependent on policy and spatial factors. We use the Integrated Markal-EFOM System model applied to the USA, adding a rich suite of biofuel and carbon capture technologies, region-specific CO2 transportation and injection costs, and government incentives from the Inflation Reduction Act. We find that under current government incentives, biofuels and carbon capture from biorefineries are primarily focused in the Midwest and South of the USA, but play a relatively small role in the overall USA transportation sector even in 2055. However, increased government incentives, biomass availability, or oil price could lead to increased biofuel production and reduced transportation emissions.

Increases in population and improvements in living standards have significantly increased the demand for animal products worldwide. However, modern livestock agriculture exerts significant pressure on the environment due to high material and energy requirements. These systems also generate significant amounts of waste that can cause severe environmental damage when not handled properly. Thus, if we wish to enable farmers to meet this increased demand in a sustainable way, technology pathways must be developed to convert livestock agriculture into a more circular economy. With this end in mind, we propose a novel framework (which we call ReNuAl) for the recovery of nutrients from livestock waste. ReNuAl integrates existing technologies with a novel biotechnology approach that uses cyanobacteria (CB) as a multi-functional component for nutrient capture and balancing, purifying biogas, and capturing carbon. The CB can be applied to crops, reducing the need for synthetic fertilizers like diammonium phosphate. Using manure profiles obtained from dairy farms in the Upper Yahara region of Wisconsin, we construct a case study to analyze the environmental and economic impacts of ReNuAl. Our results illustrate that the minimum selling price (MSP) of CB fertilizer produced from deploying ReNuAl at a 1000 animal unit (AU) farm is significantly higher than the cost of synthetic fertilizers. We also observe that ReNuAl can return environmental benefits in areas such as climate change and nutrient runoff when compared to current practices. As a result, we see that consideration of environmental incentives can significantly increase the economic viability of the process.

This study investigates the feasibility of valorizing pulp and pulp sludge (PPS) into methane through anaerobic digestion (AD) with a focus on techno-economic analysis (TEA). Three scenarios are evaluated: (A) the base case, (B) sludge AD with alkaline pretreatment using green liquor dregs (GLD), and (C) co-digestion with nitrogen-rich feedstocks. The evaluation is applied to a common PPS, consisting of 70% primary sludge (PS) from the primary clarifier and 30% secondary sludge (SS) from biological treatments from a kraft mill. Theoretical methane potential (TMP) is determined using the Buswell equation. The study highlights the significance of co-digestion with nitrogen-rich feedstocks in enhancing the economic viability of the AD process for PPS, providing valuable insights for sustainable waste management and resource recovery in the pulp and paper industries.

Biorefineries can reduce carbon dioxide emissions while serving the global chemical demand market. Governments are also using carbon pricing policies, such as carbon taxes, cap-and-trade models, and carbon caps, as a strategy to reduce emissions. The use of biomass feedstocks in conjunction with carbon capture usage and storage technologies are mitigation strategies for global warming. Businesses can invest in these technologies to accommodate the adoption of these policies. Rapid action is necessary to halt global warming, which results in aggressive policies. In this work, a multi-period process design and planning problem is developed for the design and capacity expansion of biorefineries. The three carbon pricing policies are integrated into the model and parameters are selected according to the aggressive scenario denoted by the Paris Agreement. The results show that the cap-and-trade policy achieves a higher net present value evaluation over the carbon tax model across all pareto points due to the flexibility of the allowances in the cap-and-trade policy. The carbon cap model substantial investments are required in carbon capture technologies to adhere to the emissions constraints.

This paper introduces a computational framework for selecting green solvents to separate multilayer plastic films, particularly those challenging to recycle through mechanical means. The framework prioritizes the selective dissolution of polymers while considering solvent toxicity. Initial screening relies on temperature-solubility dependence, utilizing octanol-water partition coefficients (LogP) to identify non-toxic solvents (LogP = 3). Additionally, guidelines from GlaxoSmithKline (GSK), Registration, Evaluation, Authorization, and Restriction of Chemical Regulation (REACH), and the US Environmental Protection Agency (EPA) are employed to screen for green solvents. Molecular-scale models predict temperature-dependent solubilities and LogP values for polymers and solvents. The framework is applied to identify green solvents for separating a multilayer plastic film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). The case study demonstrates the framework’s effectiveness in identifying environmentally friendly solvents and balancing trade-offs between solvent toxicity and solubility. Furthermore, the framework informs process design by screening for suitable green solvents in selective dissolution processes, potentially leading to the development of more sustainable dissolution processes and the identification of easily recyclable polymer blends in multilayer plastic films.

Chemical looping Reforming (CLR) is seen as a promising technology for blue hydrogen production. With proper control, CLR in fixed bed reactors has demonstrated the capability to generate blue hydrogen and nitrogen from a single reactor. To enhance efficiency and H2 purity in the product stream, integration of a CLR reactor with a heat recovery system and a Shift reactor is essential. This study explores the design and control of an integrated CLR-Shift reactors system. The integrated system yields a product stream with 75% H2 mole fraction during the Reforming step of CLR, and a nitrogen with high purity (98%) during the Oxidation step. In the best-case scenario, the integrated system produces H2 and N2 at a molar ratio of 1.26 with H2 production efficiency of 80.1%.

Designing a digital twin will be crucial in developing automation-based future manufacturing systems. The design of digital twins involves data-driven modelling of individual manufacturing units and interactions between the various entities. The goals of future manufacturing units such as zero waste at the plant scale can be formulated as a model-based optimal control problem by identifying the necessary state, control inputs, and manipulated variables. The fundamental assumption of any model-based control scheme is the availability of a “reasonable model”, and hence, assessing the goodness of the model in terms of stability and sensitivity around the optimal parameter value becomes imperative. This work analyses the data-driven model of an acetaminophen production plant obtained from SINDy, a nonlinear system identification algorithm using sparse identification techniques. Initially, we linearize the system around optimal parameter values and use local stability analysis to assess the stability of the identified model. Further, we use what is known as a conditional sloppiness analysis to identify the sensitivity of the parameters around the optimal parameter values to non-infinitesimal perturbations. The conditional sloppiness analysis will reveal the geometry of the parameter space around the optimal parameter values. This analysis eventually gives valuable information on the robustness of the predictions to the changes in the parameter values. We also identify sensitive and insensitive parameter direction. Finally, we show using numerical simulations that the linearized SINDy model is not good enough for control system design. The pole-placement controller is not robust, and with high probability, the control system becomes unstable to very minimum parameter uncertainty in the gain matrix.

Achieving worldwide sustainable development is a practical challenge that demands an efficient management of resources across their entire value chains. This practical task requires the optimal selection of pathways for extracting, processing, and transporting resources to meet the demands in different geographic regions at minimal economic cost and environmental impact. This work addresses the challenge by proposing a systematic framework for designing resource-processing networks that can be applied to resource management problems. The framework considers the integration and resource exchange within and across multiple processing clusters. It allows for the life cycle assessment of the environmental and economic impacts of the defined value chains, and design accordingly the different processing and transport systems from extraction to final use. The proposed representation and optimization model are demonstrated in a case study to assess the impact of energy transition under decarbonization constraints on long-distance energy supply chains. The objective is to identify optimal cluster designs and interconnecting transportation networks for decarbonized energy supply between energy exporters and importers.

In a world grappling with mounting plastic waste, the pursuit of sustainable plastic waste management has become pivotal in aligning with Circular Economy (CE) goals, with a strong emphasis on resource conservation, product durability, and carbon footprint reduction. The strategic implementation of recycling methods serves as a stepping stone for transitioning from linear to circular models. This work delves into plastic waste recycling technologies, specifically focusing on open and closed-loop approaches, providing a comprehensive evaluation anchored on economic, environmental, and circularity criteria. Different recycling techniques are thoroughly examined, with particular attention given to chemical recycling methods such as pyrolysis and gasification. This work introduces a comprehensive screening model driven by a new proposed circularity metric validated through a case study to assess these recycling pathways. The results reveal the substantial potential of chemical recycling technologies compared to conventional incineration for energy recovery. Pyrolysis refinery and methanol production from plastic waste demonstrate triple and double the profitability of incineration while significantly enhancing the overall contribution of CE. This work emphasizes the imperative of a sustainable approach to plastic waste management by balancing different metrics considerations.

The food-energy-water nexus (FEWN) has been receiving increasing interest in the open literature as a framework to address the widening gap between natural resource availability and demand, towards more sustainable and cost-competitive solutions. The FEWN aims at holistically integrating the three interconnected subsystems of food, energy and water, into a single representative network. However, such an integration poses formidable challenges due to the complexity and multi-scale nature of the three subsystems and their respective interconnections. Additionally, the significant input data uncertainty and variability, such as energy prices and demands, or the evaluation of emerging technologies, contribute to the system’s inherent complexity. In this work, we revisit the FEWN problem in an attempt to elucidate and address in a systematic way issues related to its multi-scale complexity, uncertainty and variability. In particular, we provide a classification of the sources of data and technology uncertainty from historic data, forecasting and process parameters, and propose ways to quantify their impact on the integrated system analysis. To effectively tame the FEWN’s multi-scale complexity, we distinguish between the introduced error of approximation and optimization of employed surrogate models. In turn, it is possible to characterize their impact on optimal FEWN decision-making based on the quantification of the introduced errors at all levels. Thus, we present strategies to systematically characterize FEWN process systems modeling and optimization. Ultimately, this facilitates translating obtained solutions into actionable knowledge by quantifying the level of confidence one can have in the derived process model and optimal results.

This paper considers the optimal incorporation of renewable ammonia production facilities into existing supply chain networks which import ammonia from conventional producers while accounting for uncertainty in this conventional ammonia price. We model the supply chain transition problem as a two-stage stochastic optimization problem which is formulated as a Mixed Integer Linear Programming problem. We apply the proposed approach to a case study on Minnesota’s ammonia supply chain. We find that accounting for conventional price uncertainty leads to earlier incorporation of in-state renewable production sites in the supply chain network and a reduction in the quantity and cost of conventional ammonia imported over the supply chain transition horizon. These results show that local renewable ammonia production can act as a hedge against the volatility of the conventional ammonia market.

Multilayer plastic films are widely used in packaging applications because of their unique properties. These materials combine several layers of different polymers to protect food and pharmaceuticals from external factors such as oxygen, water, temperature, and light. Unfortunately, this design complexity also hinders the use of traditional recycling methods, such as mechanical recycling. Solvent-based separation processes are a promising alternative to recover high-quality pure polymers from multilayer film waste. One such process is the Solvent-Targeted Recovery and Precipitation (STRAPTM) process, which uses sequential solvent washes to selectively dissolve and separate the constituent components of multilayer films. The STRAPTM process design (separation sequence, solvents, operating conditions) changes significantly depending on the design of the multilayer film (the number of layers and types of polymers). Quantifying the economic and environmental benefits of alternative process designs is essential to provide insights into sustainable recycling and film (product) design. In this work, we present a fast computational framework that integrates molecular-scale models, process modeling, techno-economic and life-cycle analysis to evaluate STRAPTM designs. The computational framework is general and can be used for complex multilayer films or multicomponent plastic waste streams. We apply the proposed framework to a multilayer film commonly used in industrial food packaging. We identify process design configurations with the lowest economic and environmental impact. Our analysis reveals trends that can help guide process and product design.

Plastic packaging plays a fundamental role in the food industry, avoiding food waste and facilitating food access. The increasing plastic production and the lack of appropriate plastic waste management technologies represent a threat to the environmental and human welfare. Therefore, there is an urgent need to identify sustainable packaging solutions. Circular economy (CE) promotes reducing waste and increasing recycling practices to achieve sustainability. In this work, we propose a CE framework based on multi-objective optimization, considering both economic and environmental impacts, to identify optimal packaging designs and waste management technologies. Using mixed-integer linear programming (MILP), techno-economic analysis (TEA), and life cycle assessment (LCA), this work aims to build the first steps in packaging design, informing about the best packaging alternatives and the optimal technology or technologies to process packaging waste. For the economic analysis, we consider the minimum increase in price (MIP) when adding recycling to the cost of each packaging solution, while for the environmental analysis, the greenhouse gas emissions impact was considered. A case study on ground coffee packaging is used to illustrate the proposed framework. The results demonstrate that the multilayer bag option is the most convenient when considering both the chosen economic and environmental impacts.

The oil and gas energy sector uses billions of gallons of water for hydraulic fracturing each year to extract oil and gas. The water injected into the ground for fracturing along with naturally occurring formation water from the oil wells surfaces back in the form of produced water. Produced water can contain high concentrations of total dissolved solids and is unfit for reuse outside the oil and gas industry without desalination. In semi-arid shale plays, produced water desalination for beneficial reuse could play a crucial role in alleviating water shortages and addressing extreme drought conditions. In this paper we co-optimize the design and operation of desalination technologies along with operational decisions across produced water networks. A multi-period produced water network model with simplified split-fraction-based desalination nodes is developed. Rigorous steady-state desalination mathematical models based on mechanical vapor recompression are developed and embedded at the desalination sites in the network model. An optimal common design is ensured across all periods using global capacity constraints. The solution approach is demonstrated for multi-period planning problems on networks from the PARETO open-source library. Model formulation and challenges associated with scalability are discussed.

Inspired by Nature, we propose that synergies between biorefinery and mineral refinery can be exploited so that at least a part of the carbon is captured before being released to the atmosphere. In doing so, carbon is captured not only from CO2 but also from biomass and developing more such processes may be the cornerstone for controlling CO2 emissions. A comparison of circular economy in traditional biorefineries and bio-mineral refineries is done by using general chemical formulas and it is shown that the bio-mineral refinery captures carbon. In this work, we have shown that Serpentine may be used to partially neutralise biomass pyrolysis oil. The extracted oil may be used as feedstock to produce downstream chemicals and further studies are required to produce the same.

In response to the global imperative to address climate change, this research focuses on enhancing the transparency and efficiency of the Carbon Capture Utilization and Storage (CCUS) supply chain under carbon tax. We propose a decision-making framework that integrates the CCUS supply chain’s optimization model, emphasizing carbon tax policies, with a blockchain network. Smart contracts play a pivotal role in automating the exchange and utilization of carbon emissions, enhancing the digitalization of the CCUS supply chain from source to sink. This automation facilitates seamless matching of carbon sources with sinks, efficient transfer of emissions and funds besides record-keeping of transactions. Consequently, it improves the monitoring, reporting, and verification processes within the CCUS framework, thereby simplifying compliance with regulatory mandates for net emission reductions and carbon taxation policies. By eliminating reliance on third-party verifiers, our blockchain-based CCUS system reduces verification costs and ensures reliable tracking of emissions, mitigating the risk of carbon leakage. Policymakers and stakeholders gain valuable insights to optimize the CCUS network design, specifically considering the impact of carbon tax. This study represents an advancement in sustainable practices, providing a robust tool for decision-makers engaged in climate change mitigation efforts.

Critical minerals and rare earth elements play an important role in our climate change initiatives, particularly in applications related with energy storage. Here, we use discrete optimization approaches to design a process for the recovery of Lithium and Cobalt from battery recycling, through membrane separation. Our contribution involves proposing a Generalized Disjunctive Programming (GDP) model for the optimal design of a multistage diafiltration cascade for Li-Co separation. By solving the resulting nonconvex mixed-integer nonlinear program model to global optimality, we investigated scalability and solution quality variations with changes in the number of stages and elements per stage. Results demonstrate the computational tractability of the nonlinear GDP formulation for design of membrane separation processes while opening the door for decomposition strategies for multicomponent separation cascades. Future work aims to extend the GDP formulation to account for stage installation and explore various decomposition techniques to enhance solution efficiency.

Carbon capture is a promising option to mitigate CO2 emissions from existing coal-fired power plants, cement and steel industries, and petrochemical complexes. Among the available technologies, membrane-based carbon capture presents the lowest energy consumption, operating costs, and carbon footprint. In addition, membrane processes have important operational flexibility and response times. On the other hand, the major challenges to widespread application of this technology are related to reducing capital costs and improving membrane stability and durability. To upscale the technology into stacked flat sheet configurations, high-fidelity computational fluid dynamics (CFD) that describes the separation process accurately are required. High-fidelity simulations are effective in studying the complex transport phenomena in membrane systems. In addition, obtaining high CO2 recovery percentages and product purity requires a multi-stage membrane process, where the optimal network configuration of the membrane modules must be studied in a systematic way. In order to address the design problem at process scale, we formulate a superstructure for the membrane-based carbon capture, including up to three separation stages. In the formulation of the optimization problem, we include reduced models, based on rigorous CFD simulations of the membrane modules. Numerical results indicate that the optimal design includes three membrane stages, and the capture cost is 45.4 $/t-CO2.

In this work, we propose a Mixed Integer Nonlinear Programming (MINLP) model to determine the optimal sustainable design of a poly(hydroxyalkanoate)s (PHAs) production plant configuration and its heat exchanger network (HEN). The superstructure-based optimization model considers different carbon sources as raw material: glycerol (crude and purified), corn starch, cassava starch, sugarcane sucrose and sugarcane molasses. The PHA extraction section includes four alternatives: the use of enzymes, solvent, surfactant-NaOCl or surfactant-chelate. Model constraints include detailed capital cost for equipment, mass and energy balances, product specifications and operating bounds on process units. To assess the feasibility of the PHA plant, we considered the Sustainability Net Present Value (SNPV) as the objective function, a multi-criteria sustainability metric that considers economic, environmental and social pillars. The Net Present Value (NPV) was also calculated. SNPV metric provides useful insights on sustainable PHA production, as the optimal technological route results in the sugarcane-surfactant-chelate option, rather than the sugarcane-enzyme pathway which proves more economically profitable, but with higher environmental impacts. Moreover, inclusion of HEN design significantly improves the objective function value, mainly due to a 24% carbon footprint impact reduction.

Computer-aided molecular and process design (CAMPD) tries to find the best molecules together with their optimal process. If the optimization problem considers two or more components as degrees of freedom, the resulting mixture design is challenging for optimization. The quality of the solution strongly depends on the accuracy of the thermodynamic model used to predict the thermophysical properties required to determine the objective function and process constraints. Today, most molecular design methods employ thermodynamic models based on group counts, resulting in a loss of structural information of the molecule during the optimization. Here, we unlock CAMPD based on property prediction methods beyond first-order group-contribution methods by using molecule superstructures, a graph-based molecular representation of chemical families that preserves the full adjacency graph. Disjunctive programming is applied to optimize molecules from different chemical families simultaneously. The description of mixtures is enhanced with a recent parametrization of binary group/group interaction parameters. The design method is applied to determine the optimal working fluid mixture for an Organic Rankine cycle.

Ethylene is a crucial precursor for a diverse spectrum of products and services. As global production exceeds 150 million tons annually and is projected to surpass 255 million tons by 2035, the imperative for sustainable and efficient ethylene production becomes increasingly clear. Despite Externally Heated Crackers (EHCs) dominating ethylene production for over a century, they face intrinsic limitations that necessitate transformative solutions, including intense radial thermal gradients, high metal demand, and substantial CO2 emissions. This study employs a robust combination of Computational Fluid Dynamics (CFD) coupled with detailed chemical kinetics to rigorously assess selected configurations of Internally Heated Crackers (IHCs) against the leading EHC designs. Our findings reveal that IHCs exhibit the potential to enhance ethylene output by a factor of 1.66 when compared to EHCs of the same length, diameter, and surface temperature. These results herald a promising era for developing more efficient cracking reactor designs, poised to redefine the landscape of sustainable chemical manufacturing towards achieving Net-Zero emissions. Embracing innovative technologies like IHCs presents an opportunity for the chemical industry to make significant strides in reducing its environmental footprint while meeting the growing global demand for ethylene and its derivatives.

Sand produced along with well-production fluids accumulates in the surface facilities over time, taking valuable space, while the sand carried with the fluids damages downstream equipment. Thus, sand is separated from the fluid in the sand traps and separators and removed during periodic clean-ups. But at high sand productions, the probability of unscheduled facilities shutdowns increases. Such extreme production conditions can be handled by strategic planning and optimal design of the separator network to enable maximum sand separation at minimal equipment cost while ensuring the accumulation extent is within tolerable limits. This paper develops a mathematical model to optimize the separator network design to maximize sand separation while the sand accumulation extent and total equipment cost are minimal. The optimization model is formulated using multi-objective mixed-integer nonlinear programming (MINLP). The capabilities of the developed model to assist sand management in the separator network are demonstrated with a case study of optimizing the network for two wells producing sand particles of different sizes. A residence time distribution-based model is used to predict sand settling behavior. The developed Pareto Front shows the trade-off between the increase in total sand accumulation rate and total equipment cost for an increase in the fraction of sand settled.

Most studies in process systems engineering are applying incomplete methods when incorporating sustainability. Including sustainability is a laudable goal, and practitioners are encouraged to develop systems that promote economic, environmental, and social aspects. Ten methods that are often overlooked in performing sustainable process systems engineering are listed in this effort and discussed in detail. Practitioners are encouraged to create designs that are inherently safer, to be more complete in their identification of process chemicals used and released, to be complete in their definitions of supply chains, and to apply additional environmental impact categories. Other methods point to items that are factors in process systems engineering such as disruptive recycling, robust superstructures for optimizations, and employing complete sets of objectives. Finally, users should be aware that sustainability tools are available, which might have been outside of their awareness.

Reverse electrodialysis (RED) is a nascent renewable technology that generates clean, baseload electricity from salinity differences between two water streams, a renewable source known as salinity gradient energy (SGE). Full-scale RED progress calls for robust techno-economic and environmental assessments. Using generalized disjunctive programming (GDP) and life cycle assessment (LCA) principles, this work proposes cost-optimal and sustainable RED process designs involving different RED stack sizes and width-over-length ratios to guide the design and operation from the demonstration to full-scale phases. Results indicate that RED units will benefit from larger aspect ratios with a relative increase in net power of over 30% with 6 m2 membrane size. Commercial RED unit sizes (0.25–3 m2) require larger aspect ratios to reach an equal relative increase in net power but exhibit higher power densities. The GDP model devises profitable RED process designs for all the assessed aspect ratios in a foreseeable scenario for full-scale deployment, that is, the energy recovery from desalination concentrates mixed with reclaimed wastewater effluents. A RED system with 3 m2 RED units nine times wider than its length could earn a net present value of $2M at a competitive levelized cost of electricity of $111/MWh in the Spanish electricity market. On-site, RED-based electricity could abate roughly 7% of the greenhouse gas emissions from the desalination plant’s energy supply, given the low emissions contribution of RED supply share. These findings demonstrate that optimization-based eco-technoeconomic assessments are a vital ally in making RED a full-scale reality.

This paper presents optimal design for an energy-integrated biogas-fuel cell system for renewable electricity generation. The integrated process consists of two steps. The first step generates hydrogen from biogas via methane steam reforming (SMR), whereas the second step electrochemically converts this hydrogen into electricity using a solid oxide fuel cell (SOFC). These two steps are coupled via material and energy integration. Specifically, various design alternatives like anode and/or cathode gas recycling, biogas upgradation by CO2 removal, external versus direct internal reforming, and auxiliary power production through steam and/or micro gas turbine are explored to improve the overall efficiency and total annualized cost of the system. Specifically, a flowsheet superstructure is developed by incorporating all the available design alternatives. An optimal flowsheet with minimum total annualized cost is extracted from this superstructure using formal optimization techniques to meet the desired power target. Heat exchanger network superstructure is used to incorporate energy integration effectively. The proposed flowsheet and the corresponding optimal operating conditions are explained by analyzing the trade-offs associated with the corresponding design variables in terms of power production, capital expenditure, and utility consumption. For a power target of 300 kW, the proposed optimal energy-integrated process has a total annualized cost of $608,955/y with a net electrical efficiency of 67.1% and corresponds to electricity cost of $0.23/kWh.

The study of sustainable design has gained prominence in response to the growing emphasis on environmental and social impacts of critical infrastructure. Addressing the different dimensions inherent in sustainability issues necessitates the application of many-objective optimization techniques. In this work, an illustrative four-objective design system is formulated, wherein uncertainties lie within two different socially-oriented objectives. A stochastic community detection approach is proposed to identify robust groupings of objectives. The findings reveal that the modularity of the optimal solution surpasses that of the average graph, thus demonstrating the efficacy of the proposed approach. Furthermore, a comprehensive exploration of the Pareto frontiers for both the robust and single-scenario best groupings is undertaken, demonstrating that using the robust grouping results in little to no information loss about tradeoffs.

This work explores the economic and environmental opportunities for sustainable aviation fuel (SAF) in the Brazilian sugarcane industry. Brazil was one of the first countries to use biomass fuels for transportation and is currently the 2nd largest producer of the world’s bioethanol. Bioethanol produced from sugarcane can be upgraded to SAF via the American Society for Testing and Materials (ASTM)-certified pathway alcohol-to-jet (ATJ); however, at least two challenges exist for commercial implementation. First, technologies to produce bio-jet fuels cost more than their conventional fossil-based counterparts. Second, there is considerable uncertainty regarding returns on investment as the sugar and ethanol markets have been historically volatile. As such, we propose a new optimization model to inform risk-conscious investment decisions on SAF production capacity in sugarcane mills. Specifically, we propose a linear program (LP) to model an integrated sugarcane mill that can produce sugar, ethanol, or SAF. Then, using historical price data as scenarios, we determine optimal operation at different market scenarios. Based on the relationship between ethanol, sugar, and SAF prices, we show that the integrated sugarcane mill operates in four production regions. Furthermore, through sensitivity studies, we quantify the impact of SAF prices showing a premium SAF price of 2 $ L-1 results in 100% of scenarios favoring SAF production. These results allow us to guide SAF buyers or policymakers by showing the price point for SAF to become attractive for sugarcane mill integration.

Triacetic acid lactone (TAL) is a bio-privileged molecule with potential as a chemical precursor, traditionally synthesized from petroleum. Current trends are shifting towards the use of renewable biomass or CO2-derived feedstocks to enhance sustainability. However, comprehensive studies on the techno-economic viability and carbon life cycle of such methods are limited. This study assesses TAL production from conventional glucose and a novel approach co-feeding Yarrowia lipolytica (YL) with glucose and formic acid (FA), aiming for a more cost-effective and eco-friendly process. We confront the inherent challenges in this process by exploring different technology scenarios using kinetic bioprocess modeling underpinned by techno-economic analysis (TEA) and life cycle assessment (LCA) to identify the most cost-effective and sustainable routes to TAL production. A noteworthy component of our investigation centers around the prospect of recycling and utilizing the CO2 emitted from the YL bioreactor to eliminate greenhouse gas emissions inherent in aerobic fermentation processes. The study combines TEA and LCA to dissect the proposed TAL bio-production routes, evaluating the sustainability of the process and the implications of net-zero greenhouse gas emission manufacturing. We employed SuperPro Designer and Aspen software for process simulation and energy balance computations. The results underscore the benefits of CO2 recycling in TAL production, with an estimated minimum selling price (MSP) slightly increasing by 6.21-7.80% compared to traditional methods, but significantly undercutting the market price of $51000/mt-TAL and achieving net-negative CO2 emissions. This research illustrates a viable route to bio-production with net-zero emissions, providing a model for future bioprocessing and industrial practices.

Managing oil and gas produced water, characterized by hypersalinity and large volumes, presents significant challenges. This paper introduces an advanced optimization framework, PARETO, which offers a novel approach to strategic water management, emphasizing produced water (PW) treatment, quality tracking, quantification of emissions, and environmental justice. This work presents a case study showcasing different produced water management challenges. The PARETO framework demonstrated its effectiveness in optimizing water management strategies in line with environmental sustainability and regulatory compliance.

Equation-oriented (EO) modeling has the potential to enable the effective design and optimization of the operation of advanced energy systems. However, advanced modeling of energy systems results in a large number of variables and non-linear equations, and it can be difficult to search through these to identify the culprit(s) responsible for convergence issues. The Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES-IP) contains a tool to identify poorly scaled constraints and variables by searching for rows and columns of the Jacobian matrix with small L2-norms so they can be rescaled. A further singular value decomposition can be performed to identify degenerate sets of equations and remaining scaling issues. This work presents an EO model of a flowsheet developed for post-combustion carbon capture using a monoethanolamine (MEA) solvent system as a case study. The IDAES diagnostics tools were successfully applied to this flowsheet to identify problems to improve model robustness and enable the optimization of process design and operating conditions of a carbon capture system.

We present the implementation of combined junior course projects encompassing three core courses: reaction engineering, separations, and process simulation and design. The combined project aims to enhance the vertical integration of process design learning through all levels of the curriculum. We design the projects to utilize novel modular process technologies (e.g., membrane separation) and to emphasize new process design goals (e.g., sustainability, decarbonization). Two example projects, respectively on green methanol synthesis and ethylene oxide production, are showcased for project implementation. Feedback from junior and senior students is also presented to motivate the development of such joint project in CHE curriculum. We will also discuss the challenges we hope to address to maximize student learning from this unique project.

Equation-Oriented (EO) modeling techniques have been gaining popularity as an alternative for simulating and optimizing process systems due to their flexibility and ability to leverage state-of-the-art solvers inaccessible to many procedural modeling approaches. Despite these advantages, adopting EO modeling tools remains challenging due to the significant learning curve and effort required to build and solve models. Many techniques are available to help diagnose problems with EO process models and reduce the effort required to create and use them. However, these techniques still need to be integrated into EO modeling environments, and many modelers are unaware of sophisticated EO diagnostic tools. To survey the availability of model diagnostic tools and common workflows, the U.S. Department of Energy’s Institute for the Design of Advanced Energy Systems (IDAES) has conducted user experience interviews of users of the IDAES Integrated Platform (IDAES-IP) for process modeling. The interviews reveal a gap between the availability and utilization of model diagnostic tools driven primarily by a lack of awareness of and lack of standard interfaces among different tools. To address this gap, the IDAES team has developed a recommended workflow for integrating diagnostics into the model development process and an IDAES Model Diagnostics Toolbox that provides a standard interface for many of these best practices. This paper identifies barriers to the widespread adoption of diagnostic tools for EO models and reduces these barriers by providing a standard, user-friendly interface for many different tools.

Chemical engineering is a highly complex interconnected major. Just as chemical engineers have broken complex processes into unit operations, the chemical engineering curriculum has been broken up into courses. The organization of these courses vary among institutions and are based on years of prior teachings and research. Despite this, there have been calls to revaluate the curriculum from both industry and academia. We propose a graph-based representation of curricula in which topics are represented by nodes and topic dependencies are represented by directed edges forming a directed acyclic graph. This enables using graph theory measures and tools to provide formal ways of evaluating a curriculum. Additionally, the abstraction is readily understandable meaning conversations between instructors regarding the curriculum can occur within a department and even across institutions. This abstraction is explained with a simplified curriculum and applied to the undergraduate chemical engineering curriculum at University of Wisconsin-Madison. Highly and lowly connected topics are identified and approaches for grouping the topics into modules are discussed.

Some educators place an emphasis on the commonalities between engineering mathematics with process control, among others and this helps students see the bigger picture of what is being taught. Traditionally, some of the concepts such as diffusion and heat transfer are taught with a mathematical point of view. Now-a-days, Machine Learning (ML) has emerged as topic of greater interest to both educators and learners and new and disparate modules are sometimes introduced to teach the same. With the emergence of these new topics, some students (falsely) believe that ML is a new field that is somehow different and not linked to engineering mathematics. In this work, we show the link between the different topics from engineering mathematics, that are traditionally taught in UG education, with ML. We hope that educators and learners will appreciate the treatise and think differently, and we further hope that this will further increase the interest to improve ML models.

At Procter and Gamble, innovation is based on a framework of Irresistible Superiority [1] that utilizes five complementary components – Products, Packages, Communication, Retail Execution, and Value. Increasing-ly, the computational techniques heavily leveraged with-in the Process Systems Engineering community are play-ing a leading role in delivering these five vectors, and they become increasingly valuable as we aim to deliver products in more exploratory consumer spaces – where combining high-volume data, advanced modeling, and quantified uncertainty will allow us to discover and deliv-er better products faster than ever before…

The United States’ Inflation Reduction Act (IRA) of 2022 has established incentives to facilitate the energy transition. While these policies provide economic incen-tives that encourage investment and may reduce financial risk for the private sector on the supply side, transitioning to a lower carbon or net-zero economy by 2050 presents several challenges. These include designing flexible production systems that can interact with inter-mittent renewable energy resources, ensure process safety, redesigning existing energy infrastructure to support new energy carriers like hydrogen or ammonia, and making long-term investment decisions in an uncertain and evolving market… (ABSTRACT ABBREVIATED)

The overarching goal of process design (Figure 1) is to find technologically feasible, operable, economically attractive, safe and sustainable processing pathways and process configurations with specifications for the connectivity and design of unit operations that perform a set of tasks using selected functional materials (e.g., catalysts, solvents, sorbents, etc.) to convert a set of feed-stocks or raw materials into a set of products with desired quality at a scale that satisfies the demand. Process synthesis and integration can further screen, optimize and improve these pathways for given techno-econo-environmental targets or objectives. These objectives may include, but are not limited to, minimizing the overall investment and processing costs, minimizing the energy consumption, minimizing the emissions or wastes, maxim-zing the profit, and enhancing the safety, operability, controllability, flexibility, circularity, and sustainability, among others… (ABSTRACT ABBREVIATED)

Flexibility is a critical feature of any industrial system as it tells us about the range of conditions under which the system can effectively and safely operate. It is becoming increasingly important as we face greater volatilities in market conditions, diverse customer needs, more stringent safety and environmental regulations, the growing use of resources with varying availability such as renewable energy, and an increased likelihood of disruptions caused by, for example, extreme weather… (ABSTRACT ABBREVIATED)

Process design is a core component of chemical en-gineering education and either involves or is followed by an extensive design project in most schools. The design project is often considered a core activity in the educa-tion of future chemical engineers because it develops their skills in creative and critical thinking beyond the boundaries of their acquired knowledge, as well as training them in teamwork. Such skills are likely to be crucial to empower students to develop process technologies that respond to the relevant future challenges in process design. These future challenges include accommodating alternative raw materials and energy resources, addressing sustainability concerns, and arranging production schedules that are more flexible… (ABSTRACT ABBREVIATED)

All ABET-accredited engineering programs mandate a culminating major design experience based on knowledge and skills acquired in earlier course work and incorporating realistic appropriate engineering standards and multiple realistic constraints. Some chemical companies organize their Manufacturing Innovation Process into a sequence of stages which typically include Need Identification, Product Design, Basic and Detailed Chemistry, Process Design, Equipment Design, Plant Design, Detailed Engineering and Vendor Specifications, Component Acquisition, Plant Construction Planning and Execution, Operating Procedure Development, Plant Commissioning and Start-up, and Production Planning, Scheduling, and Operation. Each of these stages involve the solution of many “design” problems that could be the subject of the culminating undergraduate chemical engineering design experience… (ABSTRACT ABBREVIATED)

Chemical Process Industries must navigate a series of changes in their operations to comply with increasing sustainability targets. These changes may involve the use of electricity-based operations, the implementation of carbon capture strategies, and the use of biomass or end-of-life carbon-containing waste as feedstocks. De-carbonizing oil refineries is particularly challenging as they possess highly valuable infrastructure. Discarding this infrastructure before the end of its life to build entirely new electric and biomass-based operations does not seem to be an economical or even a sustainable solution. This presentation will cover recent work in my group related to the decarbonization of oil refineries, focusing on proposing solutions that could be integrated with existing plants… (ABSTRACT ABBREVIATED)

There is a global consensus that steps must be taken to mitigate the impact of anthropogenic climate change. The Paris Agreement on climate change has been ratified by 192 countries and the signatories have pledged to make changes to their patterns of energy and land use that achieve “carbon neutrality” or net-zero emissions of greenhouse gases (GHG) by approximately mid-century. In these countries, energy ministries, energy companies and utilities are evaluating alternative fuels and power sources that can deliver the heat and power required for a modern economy with reduced GHG emissions. While technically proven low-emissions alterna-tives exist for almost every application, most of these alternatives cost substantially more than the fuels or energy sources they replace. Consequently, most countries will use a combination of regulations, taxes and subsidies to distort the energy market in favor of the lower-emissions alternatives… (ABSTRACT ABBREVIATED)

Achieving sustainability in the energy sector re-quires an economically viable path with a balanced tran-sition that does not aggravate environmental and socio-logical problems associated with current fossil-based power production. Increasing the grid penetration of intermittent renewables to realize a sustainable energy future without consideration of the balanced transition may result in devastating economic and societal impacts [1]. As we press for the minimization of renewable power curtailment, current fossil-based technology struggles to meet demand under extreme transient and part-load conditions. This results in dramatic reduction of efficiency and a corresponding increase in emissions of not only carbon, but far more devastating pollutants… (ABSTRACT ABBREVIATED)

The future of computer-aided process design hinges on continued recruitment, training, and retention of the next generations of engineers. Many elementary and secondary school programs focused on engineering have made substantial impacts in informing children about careers in science, technology, engineering, and mathematics (STEM). A report by the National Academies established three general principles for pre-college engineering education, the first of which is that elementary and secondary engineering education should emphasize engineering design. Curricula focused on teaching the engineering design process have been developed for K-12 students and educators… (ABSTRACT ABBREVIATED)