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... [more]

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.

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 CA... [more]

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, wa... [more]

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 signi... [more]

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-co... [more]

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.

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.

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.

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 v... [more]

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 ev... [more]

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... [more]

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.

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... [more]

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... [more]

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.

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... [more]

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.

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 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. Thi... [more]

The hydrolysis of cellulose using ionic liquid (IL) has been extensively studied but there is limited understanding of the removal of IL from the biomass hydrolysate. Finding a suitable method for the recovery and reuse of IL is one of the biggest challenges before its large-scale application. Selecting an appropriate combined recovery process is very important. This study proposed a facile ion-exchange combined method for the recovery of IL from the modeled cellulose hydrolysate mixture containing sugars as well as γ-valerolactone (GVL) via an adsorption−desorption mechanism using sulfonic acid cation-exchange (Amberlyst 15 (H)) resin. The results showed that the resin could adsorb more than 94% of 1-ethyl-3-methylimidazolium chloride [Emim]Cl IL at ambient conditions within a contact time of 20 min. The other coexisting constituents like glucose and GVL have no significant effect on the adsorption efficiency of IL. The adsorption of IL on Amberlyst 15 (H) resin was observed to be pse... [more]

This article aims to optimise the structure of a press machine to enhance its stability and accuracy, as well as reduce the frame deformation during processing. The outer supporting frame of the JH31-250 press machine (Zhejiang Weili Forging Machinery Co., Ltd, Shaoxing, China) is used as a typical sample for exploring optimisation. Commercial software is utilised to conduct a finite element analysis on the three-dimensional model of the press machine frame. A topological optimisation algorithm using the solid isotropic microstructures with penalisation (SIMP) method is then applied to improve the structure of the press frame. The size of the topological structure is further refined with the response surface method and particle swarm optimisation method to ensure it is more relevant to engineering application. The analysis results indicate that the initial frame’s deformation under the static conditions is 0.4229 mm, and after optimisation, the deformed structural displacement is 0.254... [more]

Hydraulic hoses are part of most hydraulic systems, from industrial hydraulics with open loop hydraulic systems to mobile hydraulics with closed loop hydraulic systems. The design parameters of hydraulic hoses may influence the duty cycle dynamics of these systems. One of the factors that influence the behavior of a hydraulic hose under pressure loading is the steel braid angle with respect to the hydraulic hose axis. This work aims to determine the effect of the hydraulic hose braid angle on the change in its geometry. The next objective is to determine the forces that occur at the hose ends under pressure loading. The stresses occur when fluid pressure is applied to the inner wall of the hydraulic hose. Consequently, these stresses are transferred to the hose ends through the steel braid or spiral. The phenomenon of the neutral braid angle provides a balance between the stresses generated inside the hydraulic hose. Therefore, hydraulic hose manufacturers try to produce hydraulic hose... [more]

The aim of this work is to present in a systematic way a novel general methodology to develop the design equations (heat and mass balances) for networks of ideal reactors, that is, Plug-Flow Reactors (PFRs) and Continuous Stirred Tank Reactors (CSTRs). In particular, after introducing the general definition of conversion to be used for reactor networks, several case studies of interest in chemical engineering are presented as topic-examples of application: (i) adiabatic-stage reactors with recycle, (ii) adiabatic-stage reactors with split, (iii) adiabatic-stage reactors intercooled by reactants and (iv) adiabatic-stage reactors with interstage distributed feed. More generally, the presented methodology can also be applied to develop the design equations for complex networks of interconnected reactors, not restricted to those considered in the present work. The motivation behind the present study lies in the fact that, to the best of our knowledge, a systematic development of the design... [more]

This study aims to elucidate the mechanism by which the ultrasonic loading of metal affects the extraction of small molecular phase substances (low molecular compounds) in tar-rich coal. Tar-rich coal samples were collected from the Huangling mining area in the southeastern Ordos Basin, China. The coal, the leaching solution of the coal, the extraction products, and the extraction residual coal samples with different metal ions loaded by ultrasound were analyzed using field emission scanning electron microscopy, pH detection, gas chromatography−mass spectrometry, a Fourier transform infrared spectrometer, and an X-ray diffractometer. The obtained results indicated that the ultrasonic loading of coal samples with different metal ions (Mn2+, Co2+, Cu2+, Fe2+, and Ni2+) promoted the extraction of small molecular phase substances in coal and increased the proportion of extracted aliphatic hydrocarbons, alkylbenzene, naphthalene, phenanthrene, and other compounds. The extraction rate of Mn2... [more]