The attached table shows the raw experimental data used for Figure 2 in the conference paper.

Digital supplementary information for the ESCAPE36 conference paper titled: High Performance HPs Using Tailored Refrigerants

Supplementary Material for Estimation of Thermodynamic Properties for Cellulosic Biomass-Derived Compounds: Application to Heat and Work Balances in Process Simulation that will be submitted to Escape36.

This document contains digital supplementary material for the article “An Extended Superstructure Formulation for Non-Isobaric Flowsheet Synthesis”, submitted to the peer-reviewed proceedings of the 36th European Symposium on Computer-Aided Process Engineering (ESCAPE 2026).

Supplementary material for: Generative AI for the optimal design of seawater desalination processes (ESCAPE 36, Sheffield, June 2026)

Supplemental information for the article "Multi-Scale Design for Clean Energy Systems: Industrial Electrification and Flexible Operation of Ammonia Synthesis", which has been submitted to 36th European Symposium on Computer Aided Process Engineering. The document includes parametric data and model information.

This is supplementary material for the paper "Generative AI in Process Design Instruction: A Survey of Students and Faculty" in Systems and Control Transactions. The supplementary material contains reference information for the paper. Specifically, it contains the survey questions used in the study, the raw data results of that survey, and a ChatGPT transcript of a session in which ChatGPT was used to synthesize a flowsheet of an ammonia synthesis process and perform an analysis of the conceptual design.

Nowadays, it is crucial to change daily habits to live in a more sustainable world. From an industrial point of view, the capture of CO2 is becoming more and more important in the chemical industry to reduce greenhouse gas emissions and its reuse can be an alternative to fossil resources. Another major challenge for future engineers is the significant increase in the use of renewable energy sources. In this perspective, a process allowing the synthesis of three different olefins from CO2 captured in industrial flue gases and using only wind energy is established. This process is separated into three major sections: water electrolysis, carbon dioxide reduction to produce methanol and methanol-to-olefins synthesis. The targeted production capacity is of 450 000 tonnes per year of olefins, which are considered to be ethylene, propylene and butylene. This process, which involves a complete flowsheet modelling is implemented with the Aspen Plus software. A heat integration is performed to i... [more]

While process simulations often are either very rigid and accurate or very flexible and unprecise, informed decision making can only be maintained by establishing a detailed process model as early as possible within the project lifecycle while keeping relevant aspects of the process flexible enough. In this work, we present the development of a framework based on a dynamic interface between AspenPlus® process simulations and Python, enabling enhanced flexibility and automation for process modeling and optimization. This integration leverages the powerful simulation capabilities of AspenPlus® with the versatility of Py-thon for data analysis and optimization, delivering significant improvements in workflow efficiency and process control. By utilizing the dynamic simulation data exchange with Python, extensive parameter studies can be conducted.
In this provided dataset, the necessary input data, as well as the output files for each parameter run are provided. Furthermore, a .runtime an... [more]

This study proposes a model-based approach utilizing a hybrid population balance model (PBM) to optimize temperature profiles for minimizing agglomeration and enhancing crystal growth. The PBM incorporates key mechanisms—nucleation, growth, dissolution, agglomeration, and deagglomeration—and is applied to the crystallization of an industrial active pharmaceutical ingredient (API), Compound K. Parameters were estimated through prior design of experiments (DoE) and refined via additional thermocycle experiments. In-silico DoE simulations demonstrate that the hybrid PBM outperforms traditional methods in assessing process performance under agglomeration-prone conditions. Results confirm that thermocycles effectively reduce agglomeration and promote bulk crystal formation, though their efficiency plateaus beyond a certain cycle number. This model-based approach provides a more robust strategy for agglomeration control compared to conventional methods, offering valuable insights for industr... [more]

Mechanistic population balance modeling (PBM) has advanced the design of pharmaceutical crystallization processes, enabling the production of active pharmaceutical ingredient (API) crystals with desired critical quality attributes (CQAs), such as purity and crystal size distribution. However, PBM development can sometimes be resource-intensive, requiring extensive design of experiments (DoE) and high-quality process data, making it impractical under fast-paced industrial development timelines. This study proposes a machine learning (ML)-based workflow for developing ‘fit-for-purpose’ digital twins of crystallization processes, leveraging industrially available DoE data to link operating conditions with CQAs. Validated on industrial data for a commercial API with complex crystallization challenges, the workflow efficiently identifies optimal operating conditions, demonstrating the potential of data-driven digital twins to accelerate the development of pharmaceutical processes.

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]