Contains 335 original peer-reviewed research articles presented at the 36th European Symposium on Computer Aided Process Engineering (ESCAPE 36) in Sheffield, UK. Subject categories include CAPE in Circular Economy, CAPE in Clean Energy Systems, CAPEing with Uncertain Futures, Pharmaceutical & Biotechnological Systems, Modelling & Simulation, Concepts, Methods & Tools, Process Design, Scheduling & Optimisation, Process Control & Operation, Education, and Knowledge Transfer & Entrepreneurship.

Digital competency is now core to chemical engineering practice, yet the extent and coherence of digitalisation skills provision across undergraduate curricula remain uneven. This study maps qualitatively and qualitatively digital learning outcomes across undergraduate chemical engineering programmes at the University of Sheffield, against a digital skills framework (data analysis, process simulation, process automation & control, reproducible workflows, programming, data governance). In recent years, digital skills education within chemical engineering education has advanced considerably, driven by the broader industrial shift toward Industry 4.0 and reinforced by the global challenges. Academic institutions have begun to integrate digitalisation-related content more deliberately within syllabus, in alignment with degree programme accreditation requirements and industry needs. Beyond introductory spreadsheet manipulation and basic programming, many courses are now embedding more advan... [more]

The accelerating social, environmental, and economic challenges of the twenty-first century call The growing complexity of sustainability challenges calls for educational approaches that integrate technical analysis with multi-stakeholder decision-making. Industrial ecology (IE) provides a relevant framework by combining systems thinking, resource flow analysis, and socio-environmental considerations. However, it is still predominantly taught through traditional lecture-based methods, limiting students' ability to engage with real-world complexity. This paper proposes and evaluates an experiential pedagogical framework based on industrial ecology, combining stakeholder role-play, industrial symbiosis scenario design, and multi-criteria decision analysis (MCDA). Implemented in a semester-long course, the framework enables students to collaboratively design and evaluate resource-exchange networks while representing different stakeholder perspectives. Results show significant improvements... [more]

The rapid shift toward Industry 4.0 and data driven manufacturing has prompted universities to reshape chemical engineering programmes, yet the scope and coherence of "Digital Chemical Engineering" (DCE) within UK curricula remain unclear. This study qualitatively maps DCE provision across five IChemE accredited MEng degrees to identify which digital skills are taught, how they progress across programme stages, how skills are distributed between core and elective content or taught versus applied learning, and how well provision aligns with accrediting frameworks. The analysis is structured around eight domains: (D1) programming and computation; (D2) data literacy and statistics; (D3) process modelling and simulation; (D4) optimisation and process systems engineering; (D5) control, automation and instrumentation; (D6) AI, machine learning and digital twins; (D7) software engineering practices; and (D8) data governance, ethics and cybersecurity. Results show institution dependent digital... [more]

A survey was conducted of 103 students and lecturers who had recently participated in chemical engineering design courses concerning their opinions on the use of Generative Artificial Intelligence (Gen-AI) in their capstone design education. Participants were at universities in Europe, the Middle East, North America, and South America, from at least eight different language groups. The survey found little difference in responses between students and lecturers, except for uptake, in which students reported higher rates of familiarity and adoption of Gen-AI tools than instructors. Both groups were net-positive generally on the use of Gen-AI in the classroom, reporting relatively high confidence in the ability to assess results, the general positive benefits of using Gen-AI in their chemical process design education, and the likelihood of using them in the future. However, participants reported that their trust in the results of Gen-AI tools was relatively low.

The Imperial College Integrated Design Project reframes the chemical engineering capstone as a structured educational journey that develops professional competence rather than simply delivering a final technical report. The programme is grounded in four pedagogical pillars-authenticity, integration, impact, and reflection-which align with the graduate attributes required by the Institution of Chemical Engineers. Authenticity is achieved through open-ended problems drawn from industrial partners and emerging research needs; integration connects knowledge from across the curriculum into a coherent systems perspective; impact emphasises user-centred, sustainable solutions; and reflection cultivates metacognitive awareness of decision making and learning from failure. A mentored-autonomy model supports student teams through weekly checkpoints, skills workshops, and access to disciplinary experts. Assessment deliberately balances artefact quality with evidence of process, rewarding reasonin... [more]

As Industry 5.0 (I.D. 5.0) reshapes the engineering education landscape, Higher Education Institutes (HEIs) have evolved to integrate Generative Artificial Intelligence (GenAI) via strategic curriculum revamps to meet Education 5.0 (E.D. 5.0) competencies. EN.540.202 (Introduction to Chemical & Biological Process Analysis) is the first core course at Johns Hopkins University and was revamped in Fall 2025 to create more rigorous course content and the conscious creation of new weekly graded problem sets, which did not rely on prior course content/textbook-based solved examples. Problem sets were fed as Effective Prompt Engineering (EPE) inspired prompts to ChatGPT, and AI-elicited responses were compared. AI was able to perform fundamental calculations, offer detailed explanations, unit conversions/checks, proactive information (outside the problem scope), and graphical information. Key challenges and pitfalls observed were terminology misinterpretation, lack of visual representation, d... [more]

With the ongoing advances in generative artificial intelligence (GenAI), the initial skepticism surrounding its tools is gradually diminishing. In fact, tools such as ChatGPT, Copilot and similar, are often used in everyday tasks, both in our personal lives and in educational contexts. Educators may use them for content creation, grading exams, or automating repetitive tasks. Students resort to them to better understand a topic, get feedback on an assignment and brainstorm ideas. Research has shown that, if used correctly, these tools can spur and support both teaching and learning. However, these continuous advancements and the increasing number of available tools also require more research to benchmark all these models and, if possible, provide quantifiable indications of which tool is better to use for which specific subtopic. As such, we created FermBench, a dataset of fermentation knowledge, which can be used to benchmark various large language models (LLMs). The models selected f... [more]

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.

These are the slides presented at the ESCAPE 35 conference on Monday July 7, 2025, in the talk with the same name. They briefly introduce the concept of exergy with a basic overview, and provide seven easy examples that professors can use in their courses. The topics include heating systems, pinch analysis, energy efficiency, energy integration, steam generation, utilities, heat pumps, organic Rankine cycles, direct air capture of CO2, and CO2 compression and sequestration. See the linked conference paper for more information.

In this paper, we present our recent advances and achievements in automatic control course in the engineering study of cybernetics at the Faculty of Chemical and Food Technology STU in Bratislava. We describe the course elements and procedures used to improve teaching, learning, and administration experience. We discuss on-line learning management system, various teaching aids like e-books with/without solutions to practice examples, computer generated questions, video lectures, choice of computation and simulation tools.
The course is provided in the presence form of study for about 20 students, but it relies on on-line tools and methods. Starting from this academic year, flipped design of the course was designed. We describe our experience in the preparation of such a change and some initial feedback from the students.
The course concentrates on input/output linear approximation of processes in chemical and food technology and discusses poles/zeros, process dynamics, frequency and... [more]

The field of Process Systems Engineering (PSE) is undergoing a renaissance through the integration of artificial intelligence (AI) and machine learning (ML). This transformation is driven by the vast availability of industrial data and advanced computing power, enabling the practical application of sophisticated ML models. These models enhance PSE capabilities in design, control, optimization, and safety. The progress of ML and ever-present data collection address previously intractable problems, particularly in system integration and life-cycle modeling. ML-powered predictive algorithms are augmenting traditional control systems, showing potential in supply chain optimization and increasing operational resilience. Additionally, ML-driven fault prediction and diagnostics are enhancing process safety systems, allowing for predictive maintenance and minimizing risks of accidents. A case study of the collaboration between the University of West Attica and Helleniq Energy through the MSc p... [more]

This paper examines the current state of green and digital integration in traditional chemical engineering education, focusing on how artificial intelligence (AI) can enhance learning. A review of curricula shows that sustainability principles, such as green chemistry, circular economy, and resource efficiency, are often confined to electives rather than core courses. Likewise, digital skills are introduced at a basic level, with limited exposure to AI, especially machine learning, and advanced process optimization. The paper emphasizes the need for a structured approach to integrating sustainability and digitalization into core subjects, supported by interdisciplinary learning. It also explores AI’s role in transforming education, particularly in predictive modeling, process optimization, and adaptive learning. The study provides recommendations for redesigning the traditional chemical engineering curriculum to strengthen green and digital transformation.

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.

This work gives a detailed description of the models, methods, and equipment used in a bachelor’s degree lab course. The connections between simulation results and real-world data are highlighted and tools for making the models useful for process design tasks are portrayed. The models cover the production chain for fuel-grade bioethanol, starting from the fermentation of sugar with yeast. In only one semester (14 weeks with 180 minutes per week) the students achieve to produce high-purity ethanol. Some exemplary results of the process designs and their comparison to the realized intermediate and final products are given together with production cost data.

The integration of mixed reality (MR) and virtual reality (VR) into Chemical, Biochemical, and Biomolecular Engineering (CBB) education presents an opportunity to address one of today’s most pressing pedagogical challenges: sustaining student attention and engagement. Traditional “magistral” approaches often tend to limit the adoption of interactive methodologies. By contrast, MR/VR technologies can heighten immersion and practical intuition, capturing learner focus more effectively than conventional lectures. Yet, if deployed as superficial, isolated demonstrations, these tools may fail to support deep conceptual understanding and risk supplanting core course content. This work proposes a flipped-classroom model that deliberately embeds MR/VR exercises throughout the typical CBB curriculum. The methodology emphasizes a human-in-the-loop concept, whereby the educator strategically orchestrates virtual simulations and real-world problem-solving, reinforcing theoretical concepts through... [more]

Process control can be one of the most exciting and engaging chemical engineering undergraduate courses! This paper describes our experience transforming Chemical Process Control into Data Analytics, Optimization, and Control at the University of Notre Dame (second semester required course in the junior year). Our modern course is built around six hands-on experiments in which students practice data-centric modeling and analysis using the Arduino-based Temperature Control Lab (TCLab) hardware. We argue that state-space dynamic modeling and optimization are more critical for educating modern chemical engineers than topics such as frequency domain analysis and controller synthesis emphasized in many classical undergraduate control courses. All the course material is available online at https://ndcbe.github.io/controls.

Prompt engineering – improving the command given to a large language model (LLM) – is becoming increasingly useful in order to maximize the performance of the model and therefore the quality of the output. However, in certain instances, the user is not able to enrich the prompt with additional and personalized details, such as the preferred tone and length of generated response. Therefore, it is useful to create models that learn these preferences and implement them directly in the prompt. Current state-of-the-art inductive logic programming (ILP) systems can play an important role in the development and advancement of digitalization strategies. For example, they can be used to learn personal preferences of users without sacrificing human interpretability of the learned outcomes. These systems have recently witnessed the development of data efficient, robust, and human interpretable algorithms and systems for learning predictive models from data and background knowledge. In this paper,... [more]

Current progress in digitalization has led to a wide interest in learning more from available data. Advanced data analytics can be achieved through commercially available software; however, learning to program allows for more flexibility and, ultimately, more freedom in the potentially tailor-suited investigation. Among other programming languages, Python is one of the most requested, in industry and research alike. To intensify the earlier efforts and create both a pedagogical framework to teach programming to (bio)chemical engineers, and provide students with the opportunity to ask questions, we explore the integration of sPyCE and FermentAI into BioVL, a virtual laboratory for teaching (bio)processes, previously implemented by the authors. sPyCE is an open-source series of Python courses tailored to (bio)chemical engineers, FermentAI is a chatbot trained to answer questions about fermentation processes. The main goal of this work is to enable students to (i) learn (bio)processes and... [more]

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]

In this paper, we present our recent advances and achievements in automatic control course in the engineering study of cybernetics at the Faculty of Chemical and Food Technology STU in Bratislava. We describe the course elements and procedures used to improve teaching, learning, and administration experience. We discuss on-line learning management system, various teaching aids like e-books with/without solutions to practice examples, computer generated questions, video lectures, choice of computation and simulation tools. The course is provided in the presence form of study for about 20 students, but it relies on on-line tools and methods. Starting from this academic year, flipped design of the course was designed. We describe our experience in the preparation of such a change and some initial feedback from the students. The course concentrates on input/output linear approximation of processes in chemical and food technology and discusses poles/zeros, process dynamics, frequency and t... [more]

Contains 423 original peer-reviewed research articles presented at the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35). Subject categories include Modelling and Simulation, Sustainable Product Development and Process Design, Large Scale Design and Planning/Scheduling, Model Based Optimisation and Advanced Control, Concepts, Methods and Tools, Digitalization and AI, CAPEing with Societal Challenges, CAPE Education and Knowledge, PSE4Food and Biochemical, and PSE4BioMedical and (Bio)Pharma.

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)