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]

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.

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]

Climate change, primarily caused by the excessive emission of greenhouse gases, particularly carbon dioxide (CO2), has intensified global efforts toward achieving carbon neutrality. In this context, renewable energy and CO2 utilization technologies have emerged as key strategies for reducing reliance on fossil fuels and mitigating environmental impacts. In this work, a superstructure model is developed to integrate renewable energy network and chemical production processes. The energy network integrates wind, solar, and biomass energy, complemented by storage systems to enhance reliability and reduce reliance on external power sources. The reaction network features various pathways that utilize CO2 as a raw material to produce high value-added chemicals such as polyglycolic acid (PGA), ethylene-vinyl acetate (EVA), and dimethyl carbonate (DMC), allowing for efficient conversion and resource utilization. A mixed-integer linear programming (MILP) model is formulated to minimize productio... [more]

The use of waste heat from electrolysis can significantly increase process efficiency. Alkaline and PEM electrolyzers, the most mature technologies, produce low-temperature waste heat. Most studies focus on using this waste heat for low-temperature applications like district heating. Alternatively, this waste heat can be upgraded to a temperature that can be usable in the chemical industry, e.g., for steam generation. The combination of an alkaline electrolyzer with a heat pump has been recently investigated to supply both hydrogen and medium-temperature heat. Optimizing electrolyzers for both hydrogen and heat production (combined design) has been shown to have advantages over optimizing for hydrogen only and upgrading the waste heat a posteriori (separate design). However, the effects of electrolyzer pressure and hydrogen compression were not considered, and it remains unclear if similar benefits apply to PEM electrolyzers. This work further analyzes the combined system (i.e., electr... [more]

Heat pumps play a crucial role in decarbonizing the chemical industry. The integration and sizing of heat pumps in chemical processes is a challenging task in multi-product chemical processes due to the fluctuating waste heat supply and heat demand. Integrating heat pumps may require a retrofit of the utility system. Mathematical optimization is a useful tool to tackle this challenge by enabling the analysis of correlation between relevant system parameters and equipment sizing. This study demonstrates the utilization of mathematical optimization and parameter studies for utility system equipment sizing addressing fluctuating heat supply and demand profiles.

Space exploration demands the integration of multiple scientific and engineering disciplines, with chemical engineering and process systems engineering playing pivotal roles. This paper examines their critical contributions to propulsion systems, life support mechanisms, and advanced materials essential for space missions. Recent advancements in chemical propellants and rocket fuels, illustrated by SpaceX and NASA missions, have significantly improved propulsion efficiency and safety. Chemical engineering is vital in developing air purification, water recycling, and bioregenerative life support systems, ensuring astronaut survival and mission sustainability. Additionally, creating heat-resistant, lightweight materials enhances spacecraft durability under extreme space conditions. Process systems engineering (PSE) complements these efforts by integrating, simulating, and controlling complex systems. PSE ensures reliable subsystem integration and uses predictive analytics and advanced mo... [more]

Sustainable development goals have spurred advancements in bioprocess design, driven by improved process monitoring, data storage, and computational power. High-fidelity models are essential for advanced process system engineering, yet accurate parametric models for bioprocessing remain challenging due to overparameterization, often resulting in poor predictive accuracy. Hybrid modeling, combining parametric and non-parametric methods, offers a promising solution by enhancing accuracy while maintaining interpretability. This study explores hybrid models for succinic acid fermentation by Escherichia coli, a critical process for sustainable bio-based chemical production. The research presents a structured exploration of hybrid model architectures and their robustness under varying conditions. Experimental data were preprocessed to remove noise and outliers, and hybrid model structures were developed with differing levels of hybridization (from one to all reaction rates). Kinetic paramete... [more]

We propose a methodology that allows communication with Piping and Instrumentation Diagrams (P&IDs) using natural language. In particular, we represent P&IDs through the DEXPI data model as labeled property graphs and integrate them with Large Language Models (LLMs). The approach consists of three main parts: 1) P&IDs are cast into a graph representation from the DEXPI format using our pyDEXPI Python package. 2) A tool for generating P&ID knowledge graphs from pyDEXPI. 3) Integration of the P&ID knowledge graph to LLMs using graph-based retrieval augmented generation (graph-RAG). This approach allows users to communicate with P&IDs using natural language. It extends LLM’s ability to retrieve contextual data from P&IDs and mitigate hallucinations. Leveraging the LLM's large corpus, the model is also able to interpret process information in P&IDs, which could help engineers in their daily tasks. In the future, this work will also open up opportunities in the context of other generative A... [more]

A piping and instrumentation diagram (P&ID) is a central reference document in chemical process engineering. Currently, chemical engineers manually review P&IDs through visual inspection to find and rectify errors. However, engineering projects can involve hundreds to thousands of P&ID pages, creating a significant revision workload. This study proposes a rule-based method to support engineers with error detection and correction in P&IDs. The method is based on a graph representation of P&IDs, enabling automated error detection and correction, i.e., autocorrection, through rule graphs. We use our pyDEXPI Python package to generate P&ID graphs from DEXPI-standard P&IDs. In this study, we developed 33 rules based on chemical engineering knowledge and heuristics, with five selected rules demonstrated as examples. A case study on an illustrative P&ID validates the reliability and effectiveness of the rule-based autocorrection method in revising P&IDs.

The electrification of industrial heat is a promising pathway for decarbonization, yet challenges persist in balancing capital costs, operating costs, and emissions reduction. While previous studies have assessed electrification through heat integration and graphical methods, these approaches do not inherently determine the optimal hybrid technology configuration. This study introduces an optimization-based framework that systematically evaluates the cost-optimal allocation of electrified and conventional heating technologies. Formulated as a Mixed-Integer Linear Programming (MILP) model and implemented in Gurobi, the framework minimizes Total Annualized Cost (TAC) while satisfying heat demand, technology constraints, and emissions targets. Applied to an industrial case study, the model compares three scenarios: a fully conventional system relying on steam boilers and fired heaters, a fully electrified system utilizing high-temperature heat pumps, electrode boilers, and electric heater... [more]

Large Language Models have demonstrated potential in accelerating scientific discovery, but they face challenges when making inferences in rapidly evolving and niche domains like Process Systems Engineering (PSE). To address this, we propose a Graph-based Retrieval-Augmented Generation (RAG) pipeline specifically designed for PSE papers. Our pipeline includes custom document parsing, knowledge graph construction, and refinement to enhance retrieval accuracy. We evaluate the effectiveness of our approach using an automatically generated benchmark consisting entirely of PSE-related questions. The results show that our pipeline outperforms both non-RAG and vanilla RAG implementations in terms of relevant document retrieval and overall answer quality. Additionally, our implementation is fully customizable, allowing users to select the papers most relevant to their specific tasks. This framework is openly available, providing a flexible solution for those working in PSE or similar domains.

Robust optimisation is a powerful approach for addressing uncertainty ensuring constraint satisfaction for all uncertain parameter realisations. While convex robust optimisation problems are effectively tackled using robust reformulations and cutting plane methods, extending these techniques to non-convex problems remains largely unexplored. In this work we propose a method that is based on a parallel robustness and optimality search. We introduce a novel spatial Branch-and-Bound algorithm integrated with robust cutting-planes for solving non-convex robust optimisation problems. The algorithm systematically incorporates global and robust optimisation techniques, leveraging McCormick relaxations. The proposed algorithm is evaluated on benchmark pooling problems with uncertain feed quality, demonstrating algorithm stability and solution robustness. The computational time for the examined case studies is within the same order of magnitude as state-of-the-art. The findings of this work hig... [more]

This work addresses the process design of Power-to-Ammonia plants (i.e. ammonia from renewable-powered electrolysis) by a novel methodology based on the multi-objective optimization of the “Three pillars of sustainability”: economic, environmental, and social. Specifically, we developed a tool estimating the installed capacities of every main process section typically featured by Power-to-Ammonia facilities (e.g., the renewable power plant, the electrolyzer, energy and hydrogen storage systems, etc.) to maximize the plant’s “Global Sustainability Score”.

Bayesian classification (BC) is a powerful supervised machine learning method for modelling the relationship between a set of continuous variables and a set of discrete variables that represent classes. BC has been successful in engineering and medical applications, including feasibility analysis and clinical diagnosis. Gaussian process (GP) models are widely used in BC methods to model the probability of assigning a class to an input point, typically through an indirect approach: a GP predicts a continuous function value based on Bayesian inference, which is then transformed into class probabilities using a nonlinear function like a sigmoid. The final class labels are assigned based on these probabilities. In this commonly used workflow, the uncertainty associated with the class prediction is usually evaluated as the uncertainty in the GP function values. A disadvantage of this approach is that it does not consider the uncertainty directly associated with the decision-making. In this... [more]

Developing piping and instrumentation diagrams (P&IDs) is a fundamental task in process engineering. For designing complex installations, such as petroleum plants, multiple departments across several companies are involved in refining and updating these diagrams, creating significant challenges in data exchange between different software platforms from various vendors. The primary challenge in this context is interoperability, which refers to the seamless exchange and interpretation of information to collectively pursue shared objectives. To enhance the P&ID creation process, a unified, machine-readable data format for P&ID data is essential. A promising candidate is the Data Exchange in the Process Industry (DEXPI) standard. We present pyDEXPI, an open-source implementation of the DEXPI format for P&IDs in Python. pyDEXPI makes P&ID data more efficient to handle, more flexible, and more interoperable. We envision that, with further development, pyDEXPI will act as a central scientific... [more]

This study presents an integrated approach combining the Analytic Hierarchy Process (AHP) with a Mixed-Integer Multi-Objective Linear Programming (MOMILP) model to evaluate sustainable power generation alternatives for Pamplona, Colombia. The MOMILP model includes solar, wind, biomass, and diesel technologies, aiming to minimize costs (net present value) and CO2 emissions while considering design, operational, and budget constraints. The AHP method evaluates multiple criteria such as social acceptance, job creation, technological maturity, and environmental impact. The results show that solar panels are prioritized, with small diesel plants added due to resource limitations. The most sustainable option is a hybrid system with 49% solar, 29% wind, 14% biomass and 8% diesel, generating a net present value of 121,360 USD and 94,720 kg of CO2 emissions. The proposed methodology can be applied to assess and select the most feasible alternative within a wide range of new projects for the int... [more]

This work presents a plantwide model of a Haber-Bosch ammonia synthesis loop (HB-loop) in a PtA plant, consisting of heat exchangers, compressors, steam turbines, flash separators and catalytic reactor beds. The total electrical power utility of the HB-loop is a combination of compressor power, refrigeration power, and steam turbine power. We optimise the HB-loop operating parameters, subject to constraints for maximum reactor temperatures, compressor choke and stall, minimum steam temperature, and maximum loop pressure. The loop features six degrees of freedom (DOFs) for the optimisation: three reactor temperatures, reactor N2/H2-ratio, separator temperature, and loop pressure. The optimisation minimises the total loop power utility for a given hydrogen make-up feed flow, with the PtA load varied by ranging the hydrogen make-up feed flow from 10 % to 120 % of the nominal. Across this load range, different constraints become active, with the compressor surge limit being particularly cr... [more]

In the steam power system for the centralized heat supply in an industrial park, heat demands of all consumers are satisfied by the energy station, leading to the high steam delivery costs caused by the several distant enterprises. Additionally, the number of steam levels is limited due to the trade-off between distance-related costs and heat cascaded utilization, thus some consumers are supplied with heat at higher temperature than that of required, resulting in the low energy efficiency. To deal with the above problems, this work proposes an optimization model for steam power systems (SPSs) in industrial parks, which incorporates the distributed heat supply and auxiliary steam turbines (ASTs). Field erected boilers (FEBs) can independently supply heat to consumers, thereby avoiding the excessive pipeline costs. ASTs are used for the re-depressurization of steam received by consumers, which can increase the electricity generation capacity and improve the temperature matching of heat s... [more]

Due to the intermittent nature of most renewable energy sources, developing good and flexible control structures for green electrolysis systems is crucial for maintaining efficient and safe plant operation. This work uses the “top-down” section of Skogestad’s plantwide control procedure to propose a suitable control architecture for PEM electrolysis systems based on advanced regulatory control. Advanced regulatory control structures, such as active constraint control, may offer several advantages over MPC and AI-based control methods as they are computationally less expensive, less affected by model accuracy, easier to scale, and offer fast disturbance rejection. In our approach, we first mapped the constraint regions for the system. Then, we reduce the complexity by reformulating the optimization problem slightly, to remove some constraint regions to obtain a simpler solution structure that gives a negligible loss. Finally, we propose an active constraint control architecture using PI... [more]

Safe Bayesian Optimization (Safe BO) has demonstrated significant promise in enhancing data-driven optimization strategies in safety-critical settings, where model discrepancies, noisy measurements, and unknown safety constraints are prevalent. Despite these advancements, there still remains a limited understanding on the effectiveness and applicability of these Safe BO methods, particularly within process system engineering. Specifically, this study adapts and examines Safe Exploration for Optimization with Gaussian Processes (SafeOpt), Goal-oriented Safe Exploration (GoOSE), Gaussian Processes with Trust Region (GPs-TR) and Adversarially Robust Gaussian Processes (StableOpt). Methods such as SafeOpt and GoOSE face challenges in managing continuous systems due to their reliance on system discretization and together with StableOpt, lack the capability to manage multiple safety constraints. Thus, this work presents a comprehensive evaluation of state-of-the-art safe BO methods, with our... [more]

Green hydrogen is widely recognized as a key player in the decarbonization of the energy system. To transport it efficiently, hydrogen must be converted into a carrier, such as liquefied hydrogen or ammonia, to increase its volumetric density. The supply chain of these carriers includes hydrogen conversion into the carrier, overseas transport, and carrier reconversion back to hydrogen. A case study involving hydrogen transportation across the Mediterranean Sea is used to evaluate the carrier efficiency. The processes involved in the supply chain are simulated in Aspen Plus® V11 to determine material and energy balances, and the "net equivalent hydrogen" method is applied to calculate the equivalent amount of hydrogen needed to supply thermal or electric power. The efficiency, defined as the ratio of net hydrogen delivered (after accounting for consumption and boil-off losses) to the initial hydrogen input, is higher for ammonia than for liquefied hydrogen (73% vs 60%, respectively). Th... [more]

Olefins are essential precursors in producing a wide range of chemical products, including plastics, detergents, adhesives, rubber, and food packaging. Ethylene and propylene are the most ubiquitous olefin components and are predominantly produced through steam cracking. However, steam cracking is highly energy- and carbon-intensive, making its decarbonization a priority as the energy sector shifts toward clean, renewable electricity. Electrifying the steam cracking process is a promising pathway to reduce carbon emissions. However, this is challenged by the intrinsic conflict between the continuous operational nature of ethylene plants and the intermittent nature of renewable energy sources (e.g., solar and wind) in modern power systems. Massive energy storage systems or full plant reconfigurations to meet the power demand of electrified crackers are shown to be economically and practically infeasible. Thus, a more viable solution is to pursue a gradual electrification pathway and ope... [more]

A key technology for integrating fluctuating renewable energy into the process industry is the production of green hydrogen through water electrolysis plants. Scaling up electrolysis plant capacity remains a significant challenge for the renewable energy transition. System simulation of large-scale electrolysis plants can support process design, monitoring, optimization, and maintenance scheduling. Hybrid modeling methods are promising for improving simulation reliability by combining process knowledge with process data, addressing gaps in understanding of the underlying processes. These hybrid, semi-parametric models have shown improved accuracy than purely mechanistic models. This study develops a hybrid, semi-parametric model for an anion exchange membrane electrolysis (AEMEL) stack unit. Parameters such as heat loss and heat transfer, which cannot be directly measured, are estimated using real process data. Sensors provide data on lye tank temperature, outlet temperature, and flow... [more]

The growing demand for sustainable energy has driven research into renewable methane production to reduce greenhouse gas emissions and reliance on fossil fuels. Promising feedstocks include lignocellulosic dry residues, wet waste, and captured CO2, converted via gasification, anaerobic digestion, and synthetic processes with renewable hydrogen. This study uses a multiscale approach to compare these sources, incorporating a techno-economic evaluation to identify key performance indicators (KPI) for facilities and renewable energy sources. A facility location pro- blem (FLP) determines plant locations and production capacities, considering material availability and transportation costs. The analysis focuses on the decentralised use of wastes and CO2 from point and diluted sources across Spain, employing an MILP model to optimise waste and CO2 utilisation alongside solar and wind energy systems. Results highlight lignocellulosic dry waste and CO2 captured with MEA from point sources as th... [more]