The paradigm shift into an era of Industry 4.0, also referred to as the fourth Industrial Revolution, has emphasized the need for intelligent networking between process equipment and industrial processes themselves. This has brought on an age of research and framework development for smart manufacturing in the name of Industry 4.0 [1]. While the physical and digital advancements towards smart manufacturing integration are substantial the inclusion of engineers themselves amongst this shift is often less considered [2]. There are educational efforts in Europe to create and implement smart manufacturing curriculum for non-traditional or adult learners already integrated in the workforce, but attention is also needed on a next generation smart manufacturing curriculum for pre-career students [3]. We, the teaching team of CHE 554: Smart Manufacturing at Purdue University, developed and implemented a curriculum geared towards the training of undergraduate, graduate, and non-traditional stud... [more]

Optimal and autonomous daily use of new technologies isn’t a reality for everyone. In a societal context driven by sociotechnical transitions [1] many people lack access to digital equipment and skills, preventing their participation in digital social life, including energy services. Our exploratory and phenomenological research, guided by European Union directives [2], explores the social appropriation [3] of new technologies during the deployment of smart meters in Wallonia. The study investigates social behaviour of audiences with support during smart meter installation and identifies barriers to technology appropriation. In an exclusively qualitative approach, the field surveys aim to determine to what extent individual participatory forms [4][5] and collective forms [8][9] of support, through active pedagogies like experiential learning [6][7], can include digitally vulnerable users. The central role of field professionals as interfaces [10] is also highlighted within the service... [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]

Digital twins and digital shadows are frequently used terms by industry and academia to describe data-centric models that accurately depict a physical system intended for process monitoring and control. Processes restricted by a low degree of automation rely greatly on operator competencies in key decision-making; a digital shadow can here assist as a guidance tool [1-4]. This work presents a practical implementation of a digital shadow to support operators running a pilot scale-packed batch distillation column at the Technical University of Denmark (DTU) primarily used in education and teaching activities [5]. This operation is selected due to inherent unsteady process dynamics that are controlled by a set of manual valves, which the student operator must continuously balance to meet purity constraints without disrupting the operation. This realisation employ a modular software architecture, separated into four distinct modules compiled into Docker images and independently deployed. T... [more]

Three different implementations of the flipped class paradigm were used to teach Chemical Engineering students at Imperial College London (ICL) in the 2023-24 academic year: (1) The 3rd year elective course Introduction to Numerical Methods (INM) taught in its entirety in flipped format (the “good”); (2) The 2nd year core course on Process Dynamics and Control (PDC), with the first half of the course on process dynamics taught in traditional lecture format, and the second half on process control taught in flipped format (the “bad”); and (3) a one-week workshop on heat integration, taught as part of a 3rd year core course on Process Design (PD), taught in flipped format (the “surprising”). This paper describes these three implementations in detail and presents and analyzes the responses from student surveys intended to ascertain students’ perceptions about the level of their satisfaction with the flipped class approach and the degree to which they achieved mastery of the taught material... [more]

Active learning is widely recognized as an effective teaching approach that can improve classroom outcomes. This is enabled by providing the time for students to apply new knowledge, make mistakes, correct them, and repeat the process until mastery is achieved. One way to implement active learning is through the flipped classroom paradigm. However, to be effective, active learning depends on providing students with a variety of open-ended problems, ranging in difficulty from introductory to advanced levels. This paper presents four food-themed problems for use in numerical methods and process control courses: 1. Formulating Willy Wonka’s new chocolate bar: An introductory linear programming problem focused on translating verbal descriptions into mathematical models. 2. Optimal production for the Matrix Pizza company: A more advanced mixed-integer linear programming problem involving multiple scheduling scenarios. 3. Optimal frying time for fried ice cream production: A transient heat t... [more]

The production of hydrogen from solar energy has surged in popularity in recent years, driven by global initiatives to combat climate change. The Greater Middle East (GME) region, with its favorable geographical position, offers considerable potential for solar-based hydrogen generation. This study combines Geographic Information System (GIS) spatial analysis and the Analytical Hierarchy Process (AHP) with data-driven optimization models to assess land suitability and hydrogen production potential within the region under various scenarios. Findings highlight that water availability is the primary limiting factor, followed closely by road accessibility in determining land suitability for hydrogen production. According to the AHP analysis, only 3.8% of the GME region is highly suitable for such initiatives. Projections suggest that by 2050, the region could achieve a total hydrogen production capacity of up to 1590 Mt/y, potentially avoiding around 4586 Mt of CO2 emissions if all highly... [more]

A one-dimensional homogeneous reactor model for a cocurrent flow nonadiabatic catalytic membrane reactor operating water gas shift reaction (WGSR) is developed. The model is used to predict the performance of the reactor and estimate the optimal hydrogen flux profiles required to maximize the CO conversion, and control the temperature rise due to the exothermicity. Under the optimized condition, the secured optimal hydrogen flux is found to be a bang-bang type suggesting constructing reactors of different hydrogen permeabilities. To control the reactor temperature, the activity of the reaction side is diluted by distributing axially certain fractions of inert solid, i.e. 0.35, 0.45 and 0.50. The total volume fraction of the inert solid required to maintain the temperature at 320oC (593.15 K) is 0.50 and the profile is obtained to be a singular-arc type with an observed maximum activity at the reactor inlet.

Sugarcane bioenergy is a reality in Brazil, comprising the production of ethanol and bioelectricity. Sugarcane bioenergy can reduce greenhouse gas (GHG) emissions as compared to fossil fuels. However, there are concerns about the possible implications caused by the expansion of sugarcane production, the displacement of mainly pastureland, but also other croplands, and the potential for indirect land use changes. A promising strategy to enlarge sugarcane bioenergy in Brazil without compromising the cattle industry is to integrate both activities, converting extensive livestock production systems into more intensive ones. The objective of this study is to model and evaluate the socioeconomic impacts and land use change considering the expansion of ethanol production in two scenarios. The first scenario, referred to Business as Usual (BAU), comprises of sugarcane bioenergy and extensive livestock production, without any integration between the two. The second scenario, Integrated Sugarcan... [more]

Sustainable aviation fuels (SAFs) can be pivotal, gradually replacing fossil kerosene and lowering carbon emissions without changing the existing infrastructure. One of the pathways to produce SAFs is through the Fischer-Tropsch synthesis (FTS) process. The present work proposes an integrated process of sustainable aviation fuel production from biogas through a reforming process, Fischer-Tropsch (FT), and a solid oxide electrolysis (SOEC) process. Aspen Plus v14 is used to build an integrated kinetic process model for biogas reforming, FTS and hydrocracking. The technical evaluation is assessed with several key performance indicators, such as carbon efficiency and process efficiency. In addition, two scenarios are investigated in this study for H2 supply from SOEC before and after reforming. The output products consist of kerosene and diesel since the tail gas and naphtha are recycled to the reformer to maximize SAF production. The simulation results show that the carbon efficiency of... [more]

Rare earth elements (REE) and many other critical minerals are necessary for the manufacturing of modern everyday technologies, including microchips, batteries and electric motors. Recovery of these materials typically involves aqueous systems which can be modeled as chemical equilibrium problems. One common method for solving these problems is the law of mass action approach (LMA), where a system of non-linear equations involving the equilibrium constants is solved. However, despite being theoretically simple, these problems are in practice very difficult to solve. Currently, the use of iterative heuristics based on saturation indices to decide on which species and reactions to include in the calculations is the state of the art to arrive at a solution. Here, we present an optimization-based alternative to solve chemical equilibria problems involving precipitation/dissolution reactions without the need for such heuristics. Our approach is first validated against the LMA software MINTE... [more]

In 2022, Uruguay launched its green hydrogen roadmap, due to its renewable energy potential, water availability, and favorable logistics to position itself as an exporter of green hydrogen and derivatives. The energy source for water electrolysis is a key factor in both the final cost and the environmental impact of hydrogen production. In this context, this study evaluates the environmental impact of hydrogen production via water electrolysis, powered by 150 MW of new power sources (solar, wind, and hybrid farms) in Uruguay, all connected to the national grid. A life cycle assessment (LCA) was conducted using OpenLCA software and the Ecoinvent database, with 1 kg of hydrogen as the functional unit and the system boundaries included power generation and the electrolysis system. The analysis focused on global warming potential (GWP), considering different scenarios for the grid energy mix and the inclusion or exclusion of surplus energy as carbon credits. The wind plant is the option th... [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]

In recent years, several strategies have been developed to reduce the carbon dioxide (CO2) released into the atmosphere. Carbon Capture, Storage and Utilisation (CCUS) is one of the proposed solutions. However, CCUS systems are expensive to install and operate. Furthermore, most studies in the literature have focused on CO2 utilisation and storage separately, without accounting for the effects of other CO2 emission management strategies. To address this gap, a Mixed Integer Linear Programming (MILP) framework for a supply chain network is developed in this work, incorporating CO2 storage, utilisation, permit trading, and carbon emission taxation. Multi-criteria decision analysis (MCDA) techniques are implemented to select CO2-based products for CO2 utilisation. The MILP framework is set to achieve the maximum environmental and economic performance using a Multi-Objective Optimisation (MOO) approach. This involves using the e-constraint method as a solution procedure to minimise the tot... [more]