The escalating urgency to address climate change has driven carbon capture (CC) technologies into the spotlight, particularly for large-scale emitters, which benefit from economies of scale. However, small-scale emitters account for a significant share of CO2 emissions, yet such applications remain largely overlooked in the literature. While CC cost is often used as a key performance indicator (KPI) for CC technologies, the lack of standardized cost estimation methods leads to inconsistencies, complicating comparisons, and hindering the deployment of CC systems. This study addresses these challenges by developing flexible short-cut correlations for selected CC technologies, providing estimates of the total equipment cost (TEC) and energy consumption specific to small-scale applications across various CO2 inlet concentrations (mol%) and capture scales (10 – 100 kt/y). The flexibility of the correlations enables the integration of various cost estimation methods available in the literatu... [more]

The growing size and complexity of energy system optimization models, driven by high-resolution
spatial data, pose significant computational challenges. This study introduces methods to reduce model’s size and improve computational efficiency while preserving solution accuracy. First, a composite-curve-based approach is proposed to aggregate granular data into larger resolutions without averaging out specific properties. Second, a general clustering method groups geographically proximate fields, replacing multiple transportation arcs with a single arc to reduce transportation-related variables. Lastly, a two-step algorithm that decomposes the supply chain design problems into two smaller, more manageable subproblems is introduced. These methods are applied to a case study of switchgrass-to-biofuels network design in eight U.S. Midwest states, demonstrating their effectiveness with realistic and detailed spatial data.

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... [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.

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]

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]

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]