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]

Advancing student understanding of process design requires a balanced integration of theoretical knowledge with real-world industrial applications. This study introduces system design thinking-based learning through an engineering clinic approach that bridges the gap between classroom concepts and chemical engineering practice. Using an industrial multiproduct oil pipeline operation as a case study, students are exposed to real-world industrial systems, identify bottlenecks in a process, draw similarities between systems at different scales, and implement control strategies to address a practical industrial problem. In this study, we highlight the collaborative efforts between faculty, students, and industry partners to provide experiential learning in process design, modeling, and control to address the challenge of minimizing product loss during flushing operations in multiproduct petroleum pipeline systems.

The implementation of processes that use renewable energy requires that a techno-economic analysis be performed beforehand to determine its economic and technical feasibility. A techno-economic analysis was performed proposed wind farms in Trinidad and Tobago using the System Advisor Model simulation software. Metrics included the annual energy production in kWh, capacity factor, net present value in US$ and internal rate of return. From the above, the number of households that can be powered each month by the farms were calculated. The results showed that rotor diameter, which defines the swept area has a significant impact on annual energy production as a 33 m difference translated into a 27.3 GWh and 22.9 GWh difference in output. The results are promising and show that the oil and natural gas-based economy can be diversified.

As high-throughput experimentation (HTE) becomes a cornerstone of modern materials research, undergraduate and postgraduate curricula increasingly require students to possess Python programming skills to operate automated liquid-handling robots, such as the Opentrons OT-2 and Flex. However, the high cost of this hardware often necessitates shared equipment use during hands-on lab sessions, creating a significant pedagogical barrier: students lack the individual time required to iteratively test and debug their protocols on physical robotic platforms for automated material synthesis. Furthermore, we observe that the scarcity of robotic platforms creates an imbalance in group dynamics, where students with more coding experience often lead protocol development, while those with less experience remain disengaged. To address these challenges, we developed an interactive simulator that translates Python protocols into 2D animations on personal laptops. Using gold nanoparticle (AuNP) synthesi... [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]

Electrolysis systems must be constructed from multiple stack units. This modular system of stack units (modular electrolysis system) requires systematic optimization of its composition. This optimization depends on numerous, and sometimes conflicting, criteria. While such criteria have already been evaluated for modular plants in the process industry, they must be re-assessed in the context of modular electrolysis systems. To address this challenge, an expert survey was conducted within two research networks (H2Giga and DECHEMA e.V. Research Network) and the VDMA P2X4A.The evaluation followed the two-stage process: After ranking the categories costs, flexibility, process engineering and time-to-process according to their overall importance a ranking of individual criteria within each category was conducted. The survey reveals a clear prioritisation: costs are in first place with 35.9%, followed by flexibility (25.4%), process technology (23.2%) time to process (15.4%). This ranking pro... [more]

Computational tools are widely used in solving engineering problems and are now embedded within chemical engineering education. At the UCL Department of Chemical Engineering, students are taught gPROMS ModelBuilder in modules requiring coding; however, many choose alternative tools such as MATLAB, Python, or Polymath for coursework and capstone design project reactor design. This study investigates the reasons behind these preferences using a survey of fourth-year students who had completed their third-year design project. The results show that perceived ease of use, availability of external resources, and ease of debugging could strongly influence tool selection. The findings highlight the importance of accessibility, community support, and perceived relevance in shaping sustained student engagement with computational tools.

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]

Data extraction and processing constitute the cornerstone of quantitative management and operational analysis in industrial process plants. However, most manufacturing facilities currently lack efficient data extraction systems, relying instead on engineers to manually write and execute database queries, which is time-consuming, error-prone, and inflexible when handling diverse data formats or large-scale equipment networks. To address these limitations, this work presents a novel Large Language Model (LLM)-based intelligent framework designed for data extraction and basic data of industrial equipment. The system integrates natural language understanding capabilities with process database schemas, enabling users to perform complex data queries and analyses through natural language prompts. Specifically, it can perform data mining, time-dependent analyses, equipment comparisons, and cross-period performance evaluations when process information is provided. By integrating the process con... [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]

Workflow automation platforms connect applications and services to automate data transfer and multistep processes. Although widely used in engineering research and institutional administration, including engineering institutions, they are rarely integrated into undergraduate engineering curricula, and their educational adoption introduces ethical, technical, and pedagogical risks. This paper proposes a practical framework for developing, deploying, and assessing workflow-automation-enabled learning tools coupled with generative AI, with explicit attention to institutional constraints and learning outcomes. As a conceptual case study, we present it in a second-year chemical engineering course (Heat and Mass Transfer) to support learning of heat conduction. The platform includes instructor-approved assets such as content slides, solved problems, pre-prompts, and a validated question database, through an automation pipeline that issues structured API calls to a generative AI system and re... [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]

Physics-Informed Neural Networks (PINNs) offer a promising approach for integrating first-principles modeling with data-driven methods, especially in dynamic thermal systems. This study introduces a hybrid PINN framework for a one-dimensional heating rod governed by heat transfer equations. Unlike traditional PINNs that rely on time-dependent automatic differentiation, this approach employs numerical derivatives to bypass gradient saturation and enhance robustness. The proposed model demonstrates accurate extrapolation and generalization with limited training data and is effectively used as a surrogate in a Model Predictive Control (MPC) framework for rod-tip temperature regulation. Additionally, a plan is outlined to apply physics-informed dimensionality reduction and model order reduction to improve computational efficiency and enable real-time application. The findings affirm PINNs' potential as control-oriented reduced models for thermal processes.

Reliable process operation under uncertainty remains a fundamental challenge in chemical and pharmaceutical manufacturing. Variability arising from feed fluctuations, material properties, external disturbances, and uncertain model parameters can significantly impact feasibility and performance when operating ranges are designed under nominal assumptions. Design Space Identification (DSI) addresses these limitations by defining a Probabilistic Design Space (PDS), within which process constraints are satisfied with a prescribed confidence level. However, identifying and extracting robust and practically usable nominal operating ranges (NORs) from complex, high-dimensional, and nonconvex PDSs remains computationally demanding. This work proposes a novel reinforcement learning (RL)-based framework for automated identification of the largest robust hyper-rectangular NOR fully contained within a given PDS. The design centering problem is formulated as a sequential decision-making task and so... [more]

Modern semiconductor manufacturing requires extreme precision as yield margins narrow in the "More-than-Moore" era. While physics-based models (PBMs) provide high-fidelity insights into plasma etching, their computational intensity-often requiring hours per simulation-renders them impractical for direct iterative optimization. This work demonstrates a hybrid framework that utilizes data-driven surrogate models to enable rapid, cost-effective process optimization. A 2D axisymmetric fluid model of an inductively coupled O2 plasma (ICP) reactor was developed to generate a training dataset for two neural architectures: a Multi-Layer Perceptron (MLP) and a Kolmogorov-Arnold Network (KAN). These surrogates predict radial etching rates across a wide operating window of power, pressure, gas flow, and bias voltage. By replacing the expensive PBM with these high-speed surrogates, derivative-free optimization algorithms (Nelder-Mead and Powell) successfully identified a profit-maximizing operatin... [more]

As industries pursue more sustainable and flexible manufacturing strategies, batch processes continue to play a vital role across a wide range of applications. Batch operations offer the ability to handle diverse feedstocks and accommodate varying product specifications. These processes are broadly used in sectors such as pharmaceuticals, specialty chemicals, food production, and bioprocesses, where precise control over reaction conditions and product quality is essential. However, maintaining optimal conditions in a batch process can be challenging due to the minimal opportunities for mid-batch interference. This work focuses on a real industrial batch process that frequently sees batches with poor yields resulting in large financial losses. Despite utilizing a mid-infrared spectrometer analyzing the batch medium in real-time, the reduced product accumulation observed in faulty batches is not evident until over a third of the batch time has passed, by which point the batch is not econ... [more]

Plastic injection molding is a widely used polymer-processing method. As the requirements for processing accuracy have become increasingly stringent, defect analysis in plastic injection molding is necessary to improve the product yield. Causal discovery has recently gained attention for defect analysis in many processes. Because injection molding is a spatial process involving the distribution of physical quantities, spatial autocorrelation should be considered. Although the linear non-Gaussian acyclic model (LiNGAM) is a well-known causal discovery method, it cannot properly model spatial autocorrelation. In this study, a new causal discovery method for a spatially autocorrelated dependent variable, referred to as the Causal Structure Search for the Spatial Autoregressive Model (CASSPAR), is proposed. It models the causal relationships among the observed points without prior knowledge of the spatial structure. The proposed method represents the causal relationships among the observed... [more]

Carbon dioxide transport is a critical component of the carbon capture and sequestration (CCS) supply chain. Given the substantial energy requirements and dispersed locations of CCS facilities, optimizing pipeline operations is critical to minimize costs. Although CO2 in dense phase is typically favored for long-distance transport, gaseous phase transport is also a possibility for shorter distances and volumes. This study models a gaseous CO2 pipeline system. Since CO2 gas pipelines provide the benefit of line packing, owing to gas compressibility, this work leverages it to maximize throughput in the presence of disturbances. Pipeline pressures within each segment are perceived as an inventory (i.e. form of storage) and a model predictive control (MPC) formulation for optimal inventory management is implemented to maximize throughput. This study applies the formulation to pipelines arranged in series and parallel. It effectively maximizes throughput and optimally drains pipeline pressu... [more]

Multi-stack proton exchange membrane (PEM) water electrolysis systems are increasingly deployed to improve the scalability and flexibility of green hydrogen production. However, sharing balance-of-plant equipment introduces coupling between stacks, and differences in stack performance increase the complexity of plantwide operation. In particular, non-identical efficiencies and safety constraints, such as hydrogen-to-oxygen (HTO) ratio limits, can render single-stack or equal-power-sharing control strategies suboptimal. In this work, the steady-state optimal operating regime of a two-stack PEM electrolysis system is characterized using a plantwide optimization approach and active constraint mapping over a range of system power loads. Performance differences between the stacks are represented through variations in Faraday efficiency to emulate simplified degradation. For identical stacks, the system behaves similarly to a single large electrolyzer, where equal power distribution is optim... [more]

Recurrent neural nets (RNNs) are now commonly used for the surrogate modeling of process systems, leading to better control and faster real-time optimization. However, when trained with small training data sets, most experiments show that RNNs exhibit poor generalization abilities outside the range of the training data space. Nonetheless, recent advances in deep learning research have shown that certain characteristics of the loss landscape of trained models, such as the flatness around the local minimum, tend to relate to generalization ability. This paper investigates this phenomenon for the case of RNN surrogates of the well-known Three Tanks case study, which is representative of many continuous processes. We trained a total of 200 LSTMs (long short-term memory networks) differing in initialization, architecture, and training dynamics on the same data of 500 samples. The number of model parameters ranges from 238 to 11, 353. We estimated the loss curvature of each trained model usi... [more]

Offshore natural gas compression systems are characterized by strong hydraulic coupling, nonlinear behaviour, and strict safety constraints, particularly in high-CO2 production environments. Conventional decentralized PID control with anti-surge protection ensures reliable local regulation but often leads to poor plant-wide coordination and persistent offsets when multiple compression trains, recycle loops, and separation units interact dynamically. Although multivariable control strategies such as model predictive control can address these issues, their industrial application remains limited by modeling effort, computational demand, and robustness concerns. This work presents a hybrid supervisory control framework in which reinforcement learning (RL) augments an existing PI-based architecture for an offshore gas compression system with membrane-based CO2 separation. A Proximal Policy Optimization (PPO) agent is trained on a dynamic digital-twin model of export, CO2, injection, and byp... [more]

Predictive maintenance is a promising approach to increase safety and productivity in chemical plants. One notoriously difficult problem in predictive maintenance are hard to predetermine, non-deterministic changes such as drifts. The term "drift" can be found with different definitions in this context. Therefore, it is defined here as changes in variables and parameters that occur orders of magnitude slower than the nominal process dynamics and are not directly measurable. Previous research resulted in a hybrid method that detects and diagnoses drifts from two sources: process and equipment. This method combines model-based and statistical approaches and additional information from the equipment, such as measurement gain or power consumption, is envisioned to reduce uncertainty about the drift cause [1]. First case studies revealed significant problems regarding economically viable integration of additional information. These problems arise due to the amount of information in scenario... [more]

In this work, we propose a methodology for monitoring the performance of model predictive controllers (MPCs). A sequence of binary classification machine learning models, organized in cascade, called Cascade Machine Learning Models (CMLM), is evaluated to give a diagnosis of the control conditions. The proposed methodology was assessed using two case studies: a benchmark problem (the van de Vusse reactor under nonlinear MPC, NMPC) and a simulated industrial debutanizer column under commercial MPC. The ML models evaluated were the Random Forest and the Multilayer Perceptron. The results show that the proposed approach outperforms both a single multiclass model and traditional MPC performance monitoring methodologies, while remaining adaptable and scalable to larger applications.

This research presents a novel framework for the surrogate-based dynamic optimization of control schemes within chemical separation and purification processes such as the biorefinery downstream processing. The current study investigated the downstream of an enzymatic bioreactor responsible for the synthesis of 5-hydroxymethylfurfural value-added derivatives, focusing on the critical balance between operational costs and productivity. Two high-fidelity long short-term memory neural network-based surrogate models were developed to predict energy consumption and economic gain, both achieving a coefficient of determination (R2) exceeding 0.97. These models were subsequently integrated into a multi-objective optimization architecture to address an operating efficiency testing scenario characterized by stepwise inflow parameter changes. By exploring the resulting Pareto front, an optimal set of operational (control) settings was identified and validated. The results demonstrate that while en... [more]