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.

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.

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 mee... [more]

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... [more]

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.

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 de... [more]

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 usef... [more]

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 configuratio... [more]

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... [more]

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... [more]

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 demons... [more]

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... [more]

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 d... [more]

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... [more]

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 minimu... [more]

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... [more]

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.

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 p... [more]

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 phy... [more]

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 gene... [more]

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... [more]

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... [more]

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) validat... [more]

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... [more]

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 ov... [more]