Conference Agenda

UCL Chemical Engineering employs a wide range of teaching strategies to ensure that graduates are digitally literate and have the required knowledge of how to use relevant computational tools (Tsatse and Sorensen, 2023). The curriculum consists of several modules which have a significant computational element, either as part of individual assignments, or as part of group work (Tsatse and Sorensen, 2021). These modules utilize various computational tools and software, including but not limited to gPROMS, AspenPlus, and GAMS.

Starting from Year 1, students use GAMS to solve simple problems such as mass balances and gPROMS for simple reactor problems including lumped models and distributed models. In Year 2, students start using AspenPlus to a) simulate more complex chemical units, b) interpret the behavior and results observed and c) discuss and justify any differences observed between the experimental data and computational results. In addition, they learn how to use gPROMS to model single distillation column tray and how to solve more complex reaction engineering problem, whilst they need to consider the implications of proper initialization procedures, the challenges of incorporating recycle streams, heat integration etc., which gradually expand the students’ knowledge of how Process Systems Engineering (PSE) relates to their studies. Furthermore, in addition to the traditional taught modules, the program includes a number of problem-based activities, a few of which are typically related to Process Systems Engineering such as Year 2 IEP’s Scenarios (Tsatse and Sorensen, 2021). This is an excellent opportunity for them to apply their knowledge from the taught modules, but also apply their own ideas for the Scenario deliverables.

Moving to Year 3 and their capstone design project, students have acquired the background knowledge to address the deliverables given for the design of a process plant. These deliverables include developing a rigorous model of a complete chemical process using information from literature. They investigate the heat integration possibilities for energy minimization. Moreover, the students work on the detailed design of a specific unit within the plant (reactor or separator) and investigate the optimum conditions, internals and sizing using a comprehensive parametric study.

This work outlines the rationale and strategies for delivering modules with significant computational requirements, and how they are coordinated across the curriculum to prepare students for the third year design project and future professional challenges. It demonstrates how complex process systems engineering (PSE) concepts are introduced through various modules, with a focus on supporting student learning, addressing resource challenges, and incorporating feedback for continuous improvement. The approach ensures that students not only grasp PSE tools but also develop critical engineering thinking, enabling them to excel in these challenges and often exceed expectations.

References

Tsatse, A. and Sorensen, E. (2021) Reflections on the development of scenario and problem-based chemical engineering projects. Computer Aided Chemical Engineering 50, 2033-2038

Tsatse, A. and Sorensen, E. (2023). Teaching strategies for the effective use of computational tools within Chemical Engineering curriculum. Computer Aided Chemical Engineering 52, 3501-3506

Extended Abstract

Active learning is accepted by most educators as the teaching paradigm that has the best potential to yield improved learning outcomes in classroom settings (Bloom, 1968; Crouch and Mazur, 2001). To adopt active learning, some class time needs to be allocated for students to experiment with the application of the newly acquired knowledge, giving them time to make mistakes, correct their errors, try again, and repeat this process as necessary. This cyclic activity is a variant of Kolb’s (1984) ideas about the cognitive processes involved in learning. One way to allocate time would be to adopt the flipped class paradigm (Lewin and Barzilai, 2022 and 2023).

To be effective, this form of learning relies on the availability of sufficient open-ended problem sets to enable students to provide students with a rich source of open-ended problems. These should encompass a range of difficulty: from introductory level to “final exam level” and beyond. To that end, this paper presents a sample of problems with a common theme close to the author’s heart (“the best way to a man’s heart is through his stomach”), mostly intended to be utilized in a course on numerical methods, with one extra problem designed for a course on “good old” process control. So, with “food, glorious food” as a theme, our four example problems are:

1. Optimal formulation of Willy Wonka’s new chocolate bar. This is an introductory LP problem, which gives students practice in translating a verbal problem description into a mathematical formulation.
2. Optimal scheduling for the “Matrix Pizza,” a bakery providing quality pizzas to a college campus in mid-West USA. This is a more advanced MILP problem including alternative scenarios that need to be accounted for in the optimal scheduling solution.
3. Optimal frying time for “fried ice cream.” This is a transient heat transfer problem that is defined as an IVPDE that needs to be solved numerically.
4. Control system design for Uncle Kane’s continuous pancake batter machine. This is a SISO control problem presented as a set of alternative operating modes, each with its own uncertain model description. The student needs to select the operating mode and design a suitable PI controller that meets required specifications.

References

Bloom, B. S. (1984). “The 2-sigma problem: The search for methods of instruction as effective as one-to-one tutoring.” Educational Researcher, 13(6): 4-16.

Crouch, C. H. and E. Mazur (2001). “Peer instruction: Ten years of experience and results,” American Journal of Physics, 69 (9): 970-977.

Kolb, D. (1984). Experimental Learning as the Science of Learning and Development. Prentice Hall, Englewood Cliffs, NJ.

Lewin, D. R. and A. Barzilai (2022). “The Flip Side of Teaching Process Design and Process Control to Chemical Engineering Undergraduates – and Completely Online to Boot,” Education for Chemical Engineers, 39, 44-57.

Lewin, D. R. and A. Barzilai (2023). “A Hybrid-Flipped Course in Numerical Methods for Chemical Engineers,” Comput. Chem. Eng., 172, 108167.

This past October 16^th we organized the 1st Workshop on Modelling in Chemical Engineering Education in the Netherlands with the goal to identify the current situation of modelling training in the academic Chemical Engineering (ChemE) programs in the Netherlands. The workshop was co-organized by PSE-NL (https://pse-nl.com/) and the ChemE department at Delft University of Technology (TUDelft).

The workshop followed up on an inquiry sent to the different stakeholders: lecturers in Dutch ChemE programs, directors of education of bachelor and master programs, student associations, and industrial practitioners. Respondents shared their experiences with the different ChemE program cycles, their views on the role of modelling as an evolving technology in the future of ChemE academic programs, and modelling knowledge expectations for ChemE students entering the workforce. The workshop was intended to be an explorative journey across the scales and application domains of ChemE modelling, from molecular models to supply chain management.

During the workshop, we identified issues, gaps and opportunities for enhanced teaching and practicing of modelling in Dutch academic Chemical Engineering programs. The stakeholders shared their experiences and expectations, and lectures analysed the challenges of teaching modelling at different scales. All input was used to prepare a wish list of shared desired improvements on modelling education. A list of actions to address these challenges will be the target of a follow-up workshop.

Process control should be one of the most exciting chemical engineering undergraduate courses! This presentation describes transforming "Chemical Process Control" into "Data Analytics, Optimization, and Control" at the University of Notre Dame (second-semester core course in the third undergraduate year). In six hands-on experiments, students practice data-centric modeling and analysis using the Arduino-based Temperature Control Lab (TCLab) hardware. Novel innovations in course content include (1) state-space modeling, (2) optimization using Pyomo, (3) uncertainty quantification including nonlinear regression and design of experiments, (4) and digital twins.

The semester learning goals are:

1. Develop mathematical models for dynamical systems from data and first principles using modern statistical methods;

2. Predict dynamical system performance using numerical methods;

3. Analyze, implement, tune, and debug feedback controllers using the hands-on laboratory;

4. Formulate and solve optimization problems for decision-making;

5. Demonstrate mastery of at least two of the above skills in an open-ended group project.

The semester topics are organized into three parts, as described below.

Part 1: Data-Centric Modeling of Dynamical Systems

Classical process control focuses on frequency-domain analysis. While the frequency domain perspective provides beautiful insights into certain aspects of controls (e.g., time delays and responses to periodic inputs), it requires dedicating significant time teaching Laplace transformations. Instead, we emphasize state-space modeling, which naturally complements the (partial) differential (algebraic) equation models taught in transport, kinetics, and thermodynamics. As prerequisites, our students have completed five mathematics courses (Calculus I, II, III, linear algebra and linear ODEs, differential equations) and a numerical methods and data analysis course. Using the TCLab, we build upon this foundation using numerical analysis to perform step tests and nonlinear regression to estimate ODE model parameters. Assessments include:

• Homework 1 reviews Python programming and statistical/computational methods.

• Lab 1 fits a first-order linear model to step-test data from the TCLab.

• Lab 3 compares the quality of fit for one and two-component linear models.

Part 2: Feedback Control

Next, we introduce feedback control, motivated by various applications. Using the TCLab, we implement and compare control strategies to maintain time-varying temperature setpoints to temper dark chocolate. We emphasize model-based design, developing dynamic models for the TCLab and control system, and examining how changing the control gains impacts the eigenvalues. Assessments include:

• Lab 2 explores relay (on/off) control.

• Lab 4 explores proportional-integral (PI) control.

Part 3: Computational Optimization

Finally, we introduce computational optimization in Pyomo using production planning and formulation optimization problems such as gasoline blending. These problems provide a foundation for dynamic optimization problems with the TCLab, including optimal control, state estimation, and parameter estimation. Assessments in Part 3 include:

• Homework 2 introduces optimization modeling in Pyomo, emphasizing business analytics problems.

• Lab 5 explores Pyomo-based open-loop optimization, state estimation, and parameter estimation for the TCLab.

• Lab 6 implements closed-loop model predictive control (MPC) and compares performance to relay, PI, and open-loop optimal controls.

Lectures conclude with an exam (~week 11). During the last four weeks, students focus on open-ended team projects.

Material for this course is available online (https://ndcbe.github.io/controls/Readme.html). Prof. Jeffery Kantor (1954-2023) led many innovations in this course.

The integration of mixed reality (MR) and virtual reality (VR) technologies into chemical engineering education offers promising avenues for enhancing student engagement, intuition, and comprehension of practical concepts. However, there is an existing risk that MR and VR might supplant the content of chemical engineering courses. These technologies should serve as augmentative tools that enhance, rather than replace, existing teaching methodologies. Thus, they need to be adapted with a human-in-the-loop concept and grounded in social learning theory principles.

Current implementations of MR/VR are often isolated within single topics and lack interoperability with the broader curriculum, resulting in partial educational experiences with ill-defined learning designs that fail to leverage the interconnected nature of chemical engineering disciplines. This approach impedes the development of a cohesive understanding of topics ranging from chemical reactor design to heat transfer and limits the potential for integrated learning.

The effective incorporation of MR/VR necessitates the seamless integration of existing educational materials, including presentations, lecture videos, and textual resources. By embedding and blending MR/VR experiences within the variety of existing pedagogical frameworks, chemical engineering educators can create enriched learning environments that cater to diverse cognitive preferences without discarding proven instructional methodologies.

In our view, a significant challenge lies in bridging the gap between a “low-effort” integration of MR and VR technologies and well-established teaching practices. Often, these MR and VR technologies are introduced as engaging but superficial, low-value-added demonstrations that lack alignment with specific learning outcomes and present unbalanced cost-benefit implications for educational institutions. To transcend these limitations, MR and VR must be deliberately deployed to facilitate deep conceptual understanding and practical skill development, rather than serving as mere visual spectacles. The educator’s role is more important than ever as a “human-in-the-loop,” bridging the gap so that learning outcomes can be effectively achieved.

We propose an integrated framework for designing a learning approach that combines MR/VR technologies with flipped classroom models in chemical engineering education. This framework emphasizes pre-class exposure to traditional content, enabling students to acquire foundational knowledge through established resources. Pre-, post-, and in-class sessions leverage MR/VR to provide immersive, interactive experiences that reinforce and contextualize theoretical concepts. Extending this approach throughout the undergraduate or master's curriculum facilitates a consolidated practice, which in turn meets current student expectations. This holistic educational strategy may align better with industry expectations, preparing students to navigate the multifaceted demands of the professional market.

The integration of MR/VR technologies within a flipped classroom paradigm can enhance learning outcomes by providing experiential learning opportunities that complement traditional content delivery without any partial withdrawal. The educator’s role is more crucial than ever due to the need to integrate and blend new tools in the chemical engineering course mix with a clear learning design that aligns with learning outcomes. By focusing on the alignment of these technologies with curricular objectives, we can cultivate chemical engineering professionals equipped with both the theoretical acumen and practical skills necessary to excel in a technologically advancing field such as chemical engineering.

The Integrated Project for the 2024-2025 academic year in the Master of Chemical Engineering and Materials Science at the University of Liège aims to consolidate technical knowledge and promote the acquisition of soft skills by integrating and linking chemical engineering disciplines that are usually taught separately. The key learning outcomes include making connections between different chemical engineering classes, consolidating technical knowledge, developing critical thinking, addressing complex and multidisciplinary topics in the chemical and process industry, and increasing awareness of the role of science and technology in society.

The project focuses on developing technical skills such as project management, meeting deadlines, working in large groups, and communicating effectively in English, both in written and oral forms. The final deliverable is a 15-page article and a presentation.

For the 2024-2025 academic year, the project topic is the synthesis of Vinyl Chloride Monomer (VCM). The project is divided into several parts, each concluding with a final report and presentation:

1. Part 1: Individual work on mass balances and literature reviews, followed by group consolidation of results and project planning. This phase includes presenting the mass balance, literature review results, and initial process basics.
2. Part 2: Detailed models for thermodynamics, process techno-economics, kinetics, reactors, separation, and unit operations. Students work in groups and sub-groups to study the chemical system and critical elements in detail.
3. Part 3: Sensitivity studies to assess key process parameters and evaluate their impact on unit operation results. Students challenge assumptions and discuss model validation.
4. Part 4: Integration of the process into one model, building a global flowsheet, optimizing its topology, applying optimization and heat integration techniques, and studying process techno-economics and life cycle assessment.
5. Part 5: Extended literature review and creation of a report for a general audience. Students update the literature review, identify key performance indicators, challenge and validate process assumptions, identify alternative manufacturing pathways and product alternatives, and communicate their findings to a broader audience.

Throughout the project, students will have regular interactions with industry and academic experts, participate in plenary meetings and feedback sessions, and use shared drives for communication and document sharing. The ULiège Soft Skills Team will provide support for group management and soft skills development.

Evaluation of the project includes both technical and soft skills assessments. Each student will receive an individual grade based on group performance and individual contributions. The technical group grade is based on reports and the final presentation, while the technical individual grade is based on Part 1 reports and written assessments. Soft skills evaluation includes group and individual levels, with self and peer-assessment.

This integrated project has been running for more than 5 years at the University of Liège, and an annual feedback meeting takes place at the end of the project. The general feeling expressed by students is that even if this project requires a lot of effort, it is very rich and instructive and is seen as an excellent preparation for their future career.