The electrification of regional aviation requires coordinated planning of airport energy systems that integrate renewable generation, energy storage, and hydrogen technologies in a cost-efficient and resilient manner. This paper presents a scalable techno-economic optimization framework that models multiple airports as collaborative energy hubs. An object-oriented mixed-integer linear programming (MILP) formulation is combined with a genetic algorithm (GA) to optimize infrastructure sizing and energy dispatch. The framework is applied to three Swedish regional airports-Västerås, Jönköping, and Visby. A set of scenarios, including parties operating under shared wind-energy contracts using power purchase agreements (PPAs) and dynamic pricing (DP), was studied. Detailed representations of battery energy storage, hydrogen production and storage, and market interactions are included. Results show that coordinated operation and airport collaboration under a smart energy management system can... [more]

The conceptual design and performance of a novel Methylcyclohexane-Toluene-Hydrogen based chemical heat pump was studied using steady state simulations. The distillation operating parameters of the chemical heat pump were optimized to maximize the Coefficient of Performance based on heat quantity (COP) and its corresponding Coefficient of Performance based on electric work input (COPW) was calculated. The best operating temperature ranges of the endothermic and exothermic reactor are 200°C-225°C and 250°C-275°C respectively. An endothermic temperature of 200°C and an exothermic temperature of 250°C results in a COP of 0.1357 and a COPW of 13.3. By integrating this chemical heat pump with a vapor compression heat pump COP increased to 0.1445 while COPW reduced to 4.9.

A flexible start-stop operating policy that involves full shut-down and start-up may be beneficial for electrified plants under certain grid conditions, such as dispatchable demand response. This paper introduces a multi-period Hamilton-Jacobi reachability framework to explore the space of state trajectories for plant shut-down and start-up. Shut-down is defined in terms of operations leading to a stand-by state with no material flows or energy inputs, and variables within safety constraints. Candidate stand-by states are identified by constructing backwards reachability tubes from the desired steady-state operating point. The candidate shut-down/stand-by state is partitioned in fast and slow regions. Admissible control input trajectories are determined for the fast region, from which the minimum time trajectory is selected as optimal for fast start-up. A proof-of-concept simulation using a reaction/separation/recycle plant is presented.

Mathematical models in process systems engineering (PSE) are widely used to support decision-making in design and operation, but they are mostly limited to deterministic models. For biochemical systems, the biological variability can give rise to stochastic dynamics. This work addresses the question of when to act in such processes, as the stochastic dynamics affect the timing of important events. We consider the case of batch production of malic acid using Ustilago trichophora. The goal is to predict when the substrate concentration falls below a predefined threshold. We extend an existing deterministic model of the process to a stochastic differential equation (SDE) formulation by introducing a Monod-like noise term. Simulations of the SDE model reveal a distribution of substrate depletion times and a deviation between the mean of the stochastic trajectory and the deterministic solution due to nonlinear effects. To determine optimal intervention times under uncertainty, we formulate... [more]

Traditional approaches for mechanistic modelling require in-depth understanding of the underlying chemical and physical phenomena to construct reliable and predictive models. However, at early stages of development, limited experimental data, incomplete expert knowledge, and non-observable states often hinder a full understanding of the underlying mechanisms. Symbolic regression (SR) enables systematic model discovery and offers a practical route to addressing these challenges by automating the identification of interpretable model structures and the estimation of associated parameters from available data. However, structural identifiability and observability (SIO), a critical property of such models, is often overlooked in SR, thereby limiting its broader adoption and effective deployment. To address these limitations, this study proposes a comprehensive framework, which leverages scarce prior knowledge in SR and incorporates SIO analysis, offering a potential solution to capture the... [more]

Centralized monitoring methods experience reduced fault detection sensitivity in large-scale industrial systems due to the masking effect arising from the aggregation of many interconnected variables. Decentralized monitoring, where variables are grouped into subsystems, has been shown to effectively address these limitations. However, the performance of this class of methods critically depends on how the network is partitioned, and the role of its structural factors on fault detection remains poorly understood. This work studies how network topology and causal structure affect decentralized monitoring in high-dimensional systems. Using SimCaNet, a DAG-based data simulator, where large-scale systems with 100-1000 variables were generated, we rigorously compared the performance of centralized and decentralized causal log-likelihood monitoring methods under process perturbations and sensor bias faults. Network partitioning is performed using the Leiden community detection algorithm and c... [more]

As greenhouse gas emissions accelerate global warming, new capture and storage technologies are essential for reducing the industrial CO2 concentration in the atmosphere. This study addresses the urgent need for greenhouse gas capture technologies by developing a detailed dynamic mathematical model for Chemical Looping Process with Integrated Combustion and CO2 capture (CL-ICCC). In the CL-ICCC process configuration, CO2 capture is integrated into the chemical looping combustion system, resulting in a higher-purity, more efficient process. In this work Cu/CuO oxygen carrier material and CaO/CaCO3 sorbent materials were considered in a fixed bed reactor as solid phase to investigate Oxidation and Reduction/Calcination processes under different operating conditions. The simulation results were compared with experimental results from the literature. In case of the oxidation process, a sensitivity study was performed to investigate the behavior of the process for variation of different ope... [more]

A collection of Aspen Plus files for various cases used in the study (see attached report). Simulation cases include a base case, an optimized base case, and variants including cellulose incineration and no decolorization step versions.

The transition toward circular economy principles in water treatment requires advanced process systems engineering tools to evaluate the trade-offs between environmental sustainability and economic viability, particularly for energy-intensive Zero Liquid Discharge (ZLD) systems. While classic ZLD systems treat concentrated brine as waste, circular ZLD (CZLD) systems incorporate salt recovery technologies that generate marketable salt product. This study presents a comprehensive technoeconomic assessment framework for CZLD systems integrated with renewable energy. The framework is developed to evaluate different CZLD configurations that generate saleable sodium chloride. The assessment methodology integrates solar photovoltaic systems with increasing capacities (100-1400 kW) to analyze renewable energy penetration and energy storage requirements. The renewable energy integration model incorporates hierarchical energy dispatch algorithms prioritizing direct solar utilization, battery sto... [more]

The integrated recovery of heat and mechanical work has gained increasing importance in process integration due to the strong thermodynamic coupling between temperature and pressure changes in many industrial systems. This work presents a rigorous framework for the simultaneous synthesis of Work and Heat Exchanger Networks (WHEN), in which heating, cooling, compression, expansion, throttling, and pumping are optimized in a coordinated manner. The problem is formulated using Generalized Disjunctive Programming (GDP), allowing the explicit representation of alternative thermodynamic paths, phase-dependent behavior, and logical equipment choices. Process streams are defined by supply and target states, while only bounds are imposed on intermediate pressures, temperatures, and flow rates. Streams may change classification between hot and cold multiple times and may undergo several phase transitions.Rigorous thermodynamic correlations obtained from Aspen HYSYS are embedded in the optimizati... [more]

In the field of biochemical process design, the accurate modeling of microbial growth is essential for the development and optimization of biological reactors used in the production of high-value compounds. Achieving this objective requires a detailed understanding of how environmental factors-such as pH and nutrient availability-influence microbial dynamics across the four distinct growth phases: lag, exponential, stationary, and death. Traditionally, reactor design relies heavily on the Monod model, which provides a simplified representation of microbial growth, focusing primarily on the exponential phase under constant operating conditions (1). However, this model presents substantial limitations when applied to dynamic environments where key parameters vary over time. To overcome these constraints, the present study proposes a data-driven modeling approach using a multilayer perceptron (MLP) artificial neural network for the prediction of microbial growth trajectories under varying... [more]

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]

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]