Reactive crystallization (RC) is a chemical process in which the reaction yields a crystalline product. It is used in various industries such as pharmaceutical manufacturing or water purification. In some cases, RC is the only feasible process pathway, such as the precipitation of certain ionic solids from solution. In other cases, a reaction can become a RC by changing the reaction environment to a solvent with low product-solubility. Despite the utility and prevalence of RC, it is not often emphasized in process design software. There are RC models that simulate the inner reactions and dynamics of a RC, but each has limiting assumptions, and are difficult to integrate with the rest of a process-line simulation. This modeling gap complicates RC process design and limits the exploration of the possible benefits to using RC as well as the ability to optimize a system that relies on it. To fill this gap, we built an open-source, customizable model that can be integrated with other unit o... [more]

The increased use of pharmaceuticals globally over the past two decades has contributed to an increase in unused pharmaceuticals and a corresponding surge in pharmaceutical waste. Thus, there is an impetus for the development of processes for the recovery of the active pharmaceutical ingredients (APIs) from these unused drugs. This study introduces a decision framework for solvent selection to enable the recovery of APIs using a general separation train where cooling crystallization is the final step. The framework is designed to base solvent selection not just on the solubilities of the formulation contents but also considers the overall recovery that can be achieved in the process. In addition, the environmental sustainability of the framework is analyzed using the process mass intensity metric (PMI). The effectiveness of this framework is demonstrated by using paracetamol (PA) as a model API in a formulation consisting of five of the excipients commonly found in PA formulations. The... [more]

This work presents a kinetic model of drug substance synthesis considering slug flow characteristics in Stevens oxidation. The developed model is also applied to determine the feasible range of the process parameters. Flow experiments were conducted to obtain kinetic data, varying the inner diameter, temperature, and residence time. A kinetic model was developed for the change in concentrations of the starting material, products, and catalysis. In the kinetic model, slug flow was considered by including a volumetric mass transfer coefficient during this flow. In the initial experiments, early-stage kinetic data were insufficient, conducting additional experiments at shorter residence times. Furthermore, the initial model could not reproduce the residual of the starting material, introducing the oxidant consumption that inhibits the starting material consumption and improving the initial model. The improved model could reproduce experimental results and demonstrated that, as the inner d... [more]

This work presents a hybrid-modeling approach to monoclonal antibody (mAb) production processes design using automated bioreactor equipment. Experimental data covering a reasonable yet broad range of cultivation conditions was collected by the equipment. Using the data, a model applicable to a wide range of cultivation conditions was developed. In the modeling, a data-driven model was applied to describe complicated/unknown phenomena that could not be captured by previously proposed mechanistic models. In the hybrid model, while maintaining the mass balance of the mechanistic model, coefficients of the equations were estimated with random forest regression. Overall, the model could describe the dynamic concentration profiles of product mAb and quality-relevant impurities depending on the media/glucose feeding conditions. The model was then applied to determine an optimal condition that maximized product mAb concentration and satisfied the impurity constraints. The work can further supp... [more]

The European Union is transitioning towards a circular and low-carbon economy, emphasizing renewable biological resources. This study explores the production of high-value compounds like chitosan from fungal biomass and presents a potential design for a sustainable biorefinery process, contributing to the diversification and optimisation of biomass feedstock utilisation. The process simulation includes dedicated sub-models for each unit operation, based on laboratory data and integrated into a comprehensive process flow sheet using COCO-COFE. The productivity of the simulated plant results in 2 500 tons of triglyceride oils and 1 800 tons of chitosan that can be produced from 15 000 tons of Aspergillus niger. On-site acetic acid production meets 45% of the total plant's demand, significantly reducing the amount of additional acetic acid to be purchased as raw material. Additionally, large-scale enzyme consumption and the substantial heat demand for biomass processing are key economic a... [more]

The Integrated Project in the Master of Chemical Engineering and Materials Science at the University of Liège (ULiège) aims to consolidate technical knowledge and promote the acquisition of soft skills by integrating various chemical engineering disciplines. The project focus on the design of an industrial process and is divided into five parts: individual work on mass balances and literature reviews, detailed modeling of thermodynamics and key unit operations, sensitivity studies, process integration, and report to a general audience. Key learning outcomes include developing critical thinking, addressing complex multidisciplinary topics, and understanding the role of science and technology in society. Students enhance their soft skills in project management, teamwork, and effective communication in English. Regular interactions with industry and academic experts, along with support from the ULiège Soft Skills Team, ensure comprehensive development. Evaluation includes both technical a... [more]

Carnegie Mellon University (CMU) has a strong tradition and expertise in Chemical Process Systems Engineering. This short article comments on the CMU PSE-related courses and describes in more detail our approach to teaching Chemical Process Design. We discuss (i) our emphasis on proposing processes related to energy and sustainability and (ii) some of the challenges that are currently faced when teaching this course.

This work explores several examples of how the thermodynamic concept of exergy can be used in the chemical engineering classroom. Examples include using exergy to determine thermodynamic and monetary value of utilities, to identify better heat exchanger network designs, to aid in work-heat integration applications such as heat pumps and organic Rankine cycles, to scope out realistic energy integration cases, and to assess how well chemical potential is being used and managed. The examples are presented in one connected context that makes it easy to see how exergy analyses can be useful across many aspects of chemical and energy industry supply chains.

UCL Chemical Engineering ensures graduates are digitally literate by integrating computational tools like gPROMS, Aspen Plus, and GAMS into the undergraduate curriculum. Students in the first year of undergraduate program use GAMS to solve simple simulation and optimization problems and gPROMS for solving ordinary differential equations (ODEs) in reactor design problems. In the second year, students start using Aspen Plus to simulate more complex chemical process units, interpret and discuss results obtained and justify any differences observed between experimental data and computational results. They use GAMS to simulate and optimize a process flowsheet with considerations of the implications of proper initialization procedures and strategies for obtaining optimal parameters and gPROMS for advanced reactor and separator problems. The computational knowledge acquired in the first two years prepares students for the third-year capstone design project where they use the various tools in... [more]

Industries with low-temperature heat demand, such as laundry and syrup sectors, heavily rely on natural gas-fired boilers, posing challenges to achieving net-zero emissions by 2050. Like hard-to-abate sectors, they must explore energy transition strategies, including heat recovery, fuel substitution, or carbon capture, to reduce CO2 emissions. This paper evaluates the potential of energy transition in these sectors through case studies, using a mixed integer linear programming (MILP) approach. The analysis focuses on three key performance indicators (KPIs): specific energy consumption, CO2 reduction, and variable costs. By 2050, the adoption of heat pumps and waste valorization emerge as the most promising solutions for the syrup and laundry sectors. Specifically, the use of heat pumps reduces energy demand by at least 50%, while on-site biofuel production can fully replace natural gas consumption, thus eliminating dependency on external energy sources. The analysis highlights the impo... [more]

Methanol and ammonia are key energy carriers in a decarbonized society. This study assesses their use in power generation via two pathways: direct utilization as green fuels in fuel cells or as hydrogen carriers. Using these chemicals as hydrogen carriers achieves higher efficiencies (around 40%) due to the maturity of hydrogen fuel cells, resulting in electricity costs around 700 €/MWh compared to 1200 €/MWh for direct utilization. While hydrogen offers lower electricity production costs, efficiency advancements in methanol and ammonia fuel cells could enhance their competitiveness. Additionally, for scenarios involving transportation and power generation, methanol and ammonia prove economically viable, particularly for distances exceeding 3000 km. Consequently, both are crucial for addressing hydrogen-related challenges in the new renewable energy systems.

Energy storage is essential for transitioning to a renewable system based on renewable sources. To meet this challenge, Power-to-X technologies are attracting more attention. This work explores converting the excess of electric energy obtained from wind or solar sources into hydrogen and then into methane leveraging existing natural gas infrastructure for easier storage and transport. The process involves two stages: Firstly, the methane production step using Power-to-X technologies during excess renewable energy periods and, secondly, the electricity generation step during high demand with CO2 capture for reuse in methane synthesis, forming a closed carbon loop. In this way the Power-to-X process is integrated with repurposed combined cycle power plants (CCPPs) creating a Power-to-methane-to-power system. Two approaches are evaluated: oxy-combustion, which simplifies process CO2 purification and air combustion, which needs a more complex CO2 purification, such as amine absorption or P... [more]

Reduction of greenhouse gas emissions is an important environmental element to actively combat the global warming and climate change. In view of reducing the CO2 concentration from the atmosphere, the Direct Air Capture (DAC) options are promising technologies in delivering negative carbon emissions. The integration of renewable-powered DAC systems with the CO2 utilization technologies can deliver both negative carbon emissions as well as reduced energy and economic penalties of overall decarbonized processes. This work evaluates the innovative energy- and cost-efficient potassium - calcium looping cycle as promising direct air capture technology integrated with various CO2 catalytic transformations into basic chemicals / energy carriers (e.g., synthetic natural gas, methanol etc.). The integrated system will be powered by renewable energy (in terms of both heat and electricity requirements). The investigated DAC concept is set to capture 1 Mt/y CO2 with about 75 % carbon capture rate.... [more]

Climate change, driven by increasing CO2 emissions, necessitates innovative mitigation strategies, particularly for hard-to-abate industries. Carbon Capture and Utilization technologies offer promising solutions by capturing CO2 from industrial flue gases and converting it into value-added products. Among capture methods, membrane separation stands out for its compact design, energy efficiency, and scalability. Following capture, CO2 can be converted into chemicals like formic acid using electrocatalytic processes, enabling energy storage from renewable sources. This study proposes the design of an industrial demonstrator for a CO2 recycling plant targeting hard-to-abate sectors such as textile and cement industries. The system integrates polymeric membranes for CO2 capture and a 100 cm² electrochemical reactor for CO2 electroreduction into formic acid. Experimental data from both stages are used to develop predictive models based on artificial neural networks (ANN), optimizing system... [more]

The electrification of chemical processes and carbon capture and utilisation represent two promising approaches to improve efficiency and decrease carbon emissions of the process industry. The development of electrolyzers has gathered momentum in the last decades, allowing for the possible introduction of renewable electrons into carbon dioxide-based chemicals manufacture. While the performance of the electrolyzers is subject to improvements driven by the experimental community, the generation of waste heat is unavoidable due to the electrical resistances and process inefficiencies within the electrochemical cells. The possibility of re-using this waste heat has not been investigated within the realm of carbon dioxide electrolyzers. Here we show the potential of upgrading this waste heat by means of a heat pump, for its utilisation in the downstream processing of formic acid obtained from carbon dioxide electroreduction. We found that the waste heat represents roughly 62% of the power... [more]

Compartmental models are widely used to simplify the analysis of complex fluid dynamics systems, yet subjective compartment definitions and computational constraints often limit their applicability. The CompArt algorithm introduces an AI-driven framework that automates compartmentalization in Computational Fluid Dynamics (CFD) simulations, optimizing both accuracy and efficiency. By leveraging unsupervised clustering techniques such as Agglomerative Clustering, CompArt identifies coherent flow regions based on velocity and turbulent kinetic energy dissipation rate, ensuring a data-driven, physically consistent segmentation. The methodology integrates a connectivity-based clustering strategy, where compartments are dynamically optimized using the Silhouette score and adjacency matrix. This approach enables the reduction of high-resolution 3D CFD simulations into a network of interconnected sub-systems, significantly lowering computational costs while preserving system heterogeneity. The... [more]

Process engineers in the Chemical and Oil & Gas industries extensively use process simulation for the design, development, analysis, and optimization of complex systems. This study investigates the integration of Artificial Intelligence (AI) with AVEVATM Process Simulation (APS), a next-generation commercial simulation tool. We propose a framework for a custom chatbot application designed to assist engineers in developing and troubleshooting simulations. This chatbot application utilizes a custom-trained model to transform engineer prompts into standardized queries, facilitating access to essential information from APS. The chatbot extracts critical data regarding solvers and thermodynamic models directly from APS to help engineers develop and troubleshoot process simulations. Furthermore, we compare the performance of our custom model against OpenAI technology. Our findings indicate that this integration significantly enhances the usability of process simulation tools, promoting more... [more]

Prospective life-cycle design of emerging technologies is important in discussions of decarbonization and resource circulation strategies. This study demonstrates the role of computer-aided process engineering in reflecting technology information with appropriate granularity and accuracy using air conditioning as a case study. Process simulations involving heat exchangers (indoor/outdoor units), compressors, and expansion valves were developed to model air conditioners to quantify changes in performance and heat exchanger size as existing and alternative refrigerants are introduced. The process simulation results were incorporated into a material flow analysis and life cycle assessment to quantify the change in life cycle greenhouse gas (GHG) emissions through 2050 for each refrigerant installed. The results show that operational emissions dominate the life cycle GHG emissions of air conditioners, that decarbonization of electricity can significantly reduce life cycle GHG emissions, wi... [more]

The growing demand for sustainable alternatives to petroleum-based products drives the development of biomanufacturing using agriculture-based sugars. However, agricultural sugar production faces significant challenges due to limited production capacity and potential negative environmental impacts. This research examines chemical sugar synthesis as an alternative, assessing its environmental impact with conventional agricultural production methods through life cycle assessment. As formaldehyde serves as a primary substrate in chemical synthesis, four production cases were evaluated—comprising two pathways (conventional methods and CO2 capture and utilization (CCU) technologies), each implemented with either fossil fuels or renewable energy sources. The analysis revealed that semi-batch reactors in chemical synthesis substantially reduce environmental impacts compared to batch reactors. Chemical sugar synthesis demonstrated marked advantages in reducing eutrophication, land use change,... [more]

One of the most successful continuous high-performance liquid chromatography (HPLC) processes for drug manufacturing is the Simulated Moving Bed (SMB). SMB is a multi-column, continuous, chromatographic process that can handle much higher throughputs than regular batch chromatographic processes. The process is initially transient, but eventually arrives at a cyclic steady state, which makes optimization very challenging, even more so when superstructure optimization is involved. To simplify the optimization problem, many researchers fixed the SMB structure, optimizing only the continuous variables, so they cannot be considered superstructure optimization. In this work, an SMB superstructure that can simultaneously optimize column structure and operation is proposed. The results showed that the superstructure proposed is reliable, and it is more efficient compared to current optimization approaches if the optimal column structure has to be identified.

Process design deals with the problem of finding the best process set-up, subject to a set of constraints defining the design space (DSp). This selection is guided primarily by economic considerations. Flexibility may also play an important factor in process design, since it embodies “how far” from the design space’s bounds are the candidate optimal designs, which in some cases may lead to off-spec products. This work proposes a novel approach for flexibility assessment. In design problems where the design space is constrained by a set of affine bounds, flexibility may be expressed either as the minimum or the maximum distance with respect to the feasible (design) space bounds. For any point in the DSp, the minimum distance provides a good indicator on the minimum flexibility, as the direction that represents the highest risk of violating the constraints. An analogous conclusion can be drawn between the maximum distance and maximum flexibility. These distances can be computed exactly v... [more]

In this work, we introduce a quantum-enhanced reinforcement learning (RL) framework for process design synthesis. RL-driven methods for generating process designs have gained momentum due to their ability to intelligently identify optimal configurations without requiring pre-defined superstructures or flowsheet configurations. This eliminates reliance on prior expert knowledge, offering a comprehensive and robust design strategy. However, navigating the vast combinatorial design space poses computational challenges. To address this, a novel approach integrating RL with quantum machine learning (QML) is proposed. QML leverages theoretical advantages over classical methods to accelerate searches in large spaces. Built upon our prior work, the approach begins with a maximum set of available unit operations, represented in a flowsheet structure using an input-output stream matrix as RL observations. A Deep Q-Network (DQN) algorithm trains a parameterized quantum circuit (PQC) in place of a... [more]

In process systems engineering (PSE), obtaining accurate process models for optimization can be expensive and time-consuming. Black-box Bayesian Optimization (BO) with Gaussian process (GP) surrogates offers a promising approach. However, full black-box optimization neglects valuable prior knowledge, which could otherwise improve the optimization process. This work explores methods of integrating prior knowledge in the form of low-fidelity data into BO by evaluating these methods on synthetic multi-fidelity test functions. Our results highlight possibilities for improved convergence of the BO optimization. However, our work further highlights potential pitfalls of these multi-fidelity models, such as bias, convergence to local optima, and overfitting on low-fidelity data. Hence, leveraging low-fidelity data in multi-fidelity models can improve BO convergence, but there are instances where the algorithms are more susceptible to failure.

Hydrogen isotope separation is a critical component of the fusion fuel cycle, particularly for achieving the desired purity levels of deuterium and tritium while minimising tritium inventory. This study investigates the cryogenic distillation of hydrogen isotopes, with a focus on the effects of isotopic equilibrium reactions at reduced temperatures and different system configurations. A one-column architecture was analysed to evaluate the impact of feed and side stream equilibrator temperatures and flowrates on separation performance and tritium inventory. Additionally, a two-column architecture was studied, incorporating multiple isotopic equilibrators in interconnecting streams, to further reduce unwanted heteronuclear isotopologues and improve system efficiency. Comparative analysis of the proposed configurations highlights significant operational advantages of optimising equilibrator temperatures, including reduced tritium contamination and inventory. Results indicate that reducing... [more]

Anthropogenic carbon-dioxide emissions, exceeding 51 billion tons annually, are a major driver of global climate impacts. Aqueous amine scrubbing offers an effective carbon-capture solution, but the energy-intensive thermal regeneration step of the process significantly increases costs, limiting large-scale adoption. To address these challenges, computational optimization of process and molecular design is promising but often too resource-intensive, emphasizing the need for efficient surrogate models. Specifically, we develop a surrogate model based on an artificial neural network (ANN) that is employed to replace rigorous phase-equilibrium computations performed with the SAFT-? Mie group contribution method within a steady-state aqueous amine carbon-capture process model. Our ANN is trained on 32,768 vapour–liquid equilibrium data points of a quaternary mixture of water, monoethanolamine, carbon dioxide, and nitrogen over industrially relevant temperature, pressure, and composition ra... [more]