Renewable energy and energy efficiency are increasingly recognised as crucial for creating new economic opportunities and mitigating environmental impacts. Anaerobic digestion (AD) transforms organic materials into a clean, renewable energy source. Co-digestion of various organic wastes and energy crops addresses the disadvantages of single-substrate digestion, increasing production flexibility yet adding process complexity and sensitivity. This study employs a two-pronged approach to optimise biogas production while considering global warming potential: a nonlinear programming (NLP) model for dynamic system economic optimisation with a model predictive control (MPC) strategy for precise temperature regulation within the digester. The NLP model integrates a combined heat and power (CHP) system to leverage dynamic electricity, heat, and gas prices, accounting for physical and economic parameters such as biomethane potential, chemical oxygen demand, and substrate density. A cardinal temp... [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.

The production of hydrogen from solar energy has surged in popularity in recent years, driven by global initiatives to combat climate change. The Greater Middle East (GME) region, with its favorable geographical position, offers considerable potential for solar-based hydrogen generation. This study combines Geographic Information System (GIS) spatial analysis and the Analytical Hierarchy Process (AHP) with data-driven optimization models to assess land suitability and hydrogen production potential within the region under various scenarios. Findings highlight that water availability is the primary limiting factor, followed closely by road accessibility in determining land suitability for hydrogen production. According to the AHP analysis, only 3.8% of the GME region is highly suitable for such initiatives. Projections suggest that by 2050, the region could achieve a total hydrogen production capacity of up to 1590 Mt/y, potentially avoiding around 4586 Mt of CO2 emissions if all highly... [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.

Rare earth elements (REE) and many other critical minerals are necessary for the manufacturing of modern everyday technologies, including microchips, batteries and electric motors. Recovery of these materials typically involves aqueous systems which can be modeled as chemical equilibrium problems. One common method for solving these problems is the law of mass action approach (LMA), where a system of non-linear equations involving the equilibrium constants is solved. However, despite being theoretically simple, these problems are in practice very difficult to solve. Currently, the use of iterative heuristics based on saturation indices to decide on which species and reactions to include in the calculations is the state of the art to arrive at a solution. Here, we present an optimization-based alternative to solve chemical equilibria problems involving precipitation/dissolution reactions without the need for such heuristics. Our approach is first validated against the LMA software MINTE... [more]

Ensuring access to clean water, preserving water reserves, and meeting energy needs are fundamental for sustainability and a priority for global organizations like the UN and EU. The Mediterranean, particularly Greece, faces severe water imbalances due to rising demand, prolonged droughts, and seasonal tourism pressure. This over-exploitation of water resources threatens agriculture, employment, and regional sustainability. Addressing these challenges, this study analyzes the water-energy nexus in high-stress areas and develops an optimization model for sustainable water resource management. The model integrates sectoral demands, energy consumption, and seasonal variability to improve efficiency while balancing economic and environmental constraints. Additionally, it incorporates demand forecasting to align water use with ecosystem sustainability, reducing environmental impacts. By providing a systematic framework for decision-makers, this research supports the development of long-term... [more]

The climate crisis continues to grow as an existential threat. Establishing reliable energy resources that are renewable and zero-carbon emitting is a critical endeavour. Hydrogen has emerged as one such critical resource due to its high gravimetric energy density and near-abundant availability. However, it suffers from low volumetric energy density and is incredibly challenging to store and transport. The metal hydride, a solid-state storage method, provides a viable solution to the current limitations. Storage is achieved through the chemical absorption of hydrogen into a porous metal alloy’s sublattice. But its challenging thermodynamic functionality leaves a gap between the ideal storage capacity that current industry requires and the limited capacity that reusable metal hydrides currently provide. This work used mathematical modelling to determine optimal operating conditions for a metal hydride in order to maximise hydrogen storage capacity. Computational fluid dynamics is used t... [more]

One of the proposed strategies to reach net-zero goals is the diversification of a country’s energy mix and transition to technologies that favour the mitigation of greenhouse gas emissions, while decreasing dependency on conventional fuels. This work presents a mathematical model that describes key production routes for two proposed energy transition vectors, biomethane and hydrogen, expressed as a Mixed-Integer Linear Problem (MILP). The supply chain is optimized with the objective of maximizing the profits from the global supply chain. The problem is formulated as an allocation problem, with production distributed between biomethane and hydrogen markets. The case study focuses on a region in Mexico where second-generation biomass for biogas production is abundant, while hydrogen is produced from biomethane using steam methane reforming. The results highlight the importance of balancing resource allocation in shared supply chains. With a production ratio of 60% biomethane and 40% hyd... [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]

Hydrogen is a key element in the global transition toward a low-carbon economy, with green hydrogen offering significant potential to decarbonize industries and energy systems. This study focuses on designing and optimizing a green hydrogen supply chain (HSC) for the state of Bahia, Brazil, using a deterministic Mixed-Integer Linear Programming (MILP) model. The model evaluates 24 scenarios combining production sites, storage technologies, transportation methods, and energy sources, minimizing the Total Sustainable Cost (TSC). The TSC integrates financial and environmental costs, monetizing CO2 emissions using international carbon pricing. Results indicate that economies of scale play a critical role allowing the minimization of the financial costs while achieving lower greenhouse gas (GHG) emissions compared to other scenarios. The study emphasizes the importance of aligning production strategies with regional renewable energy resources to enhance both cost-effectiveness and sustainab... [more]

This study optimizes the Sustainable Aviation Fuel Supply Chain Network (SAFSCN) in the Czech Republic, using wheat straw as feedstock. It integrates geospatial data, transportation logistics, and economic feasibility, applying mixed-integer linear programming (MILP) to optimize pyrolysis plant locations and minimize costs. Sensitivity analysis varied wheat production growth by ±0.1% and ±0.2%. Results confirm Sustainable Aviation Fuel (SAF) production is technically and economically viable, with costs projected to decline up to 30.64% and revenues rising 49.07% from 2030 to 2050 due to technological advancements, improved logistics, and economies of scale. The findings underscore the critical role of SAF in achieving EU aviation decarbonization targets and highlight the importance of efficient supply chain planning for scaling SAF production.

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.

Climate change, driven by increasing CO2 emissions, necessitates innovative mitigation strategies, particularly for hard-to-abate industries. Carbon Capture and Utilization technologies offer promising solutions by capturing CO2 from industrial flue gases and converting it into value-added products. Among capture methods, membrane separation stands out for its compact design, energy efficiency, and scalability. Following capture, CO2 can be converted into chemicals like formic acid using electrocatalytic processes, enabling energy storage from renewable sources. This study proposes the design of an industrial demonstrator for a CO2 recycling plant targeting hard-to-abate sectors such as textile and cement industries. The system integrates polymeric membranes for CO2 capture and a 100 cm² electrochemical reactor for CO2 electroreduction into formic acid. Experimental data from both stages are used to develop predictive models based on artificial neural networks (ANN), optimizing system... [more]

In this work, we propose a mixed-integer linear programming (MILP) model that optimizes economic and environmental objectives by retrofitting fuel stations for the case study of Spain. The model contains set-covering constraints that ensure that there is at least one retrofitted fuel station within a radius of 20 kilometers of each retrofitted fuel station. The results indicate that by retrofitting fuel stations to allow for electric vehicles, both economic and environmental objectives improve, while showing which power plants would be tasked with the increase in electricity production to satisfy the increased electric demand.

The transition to renewable energy sources, such as biogas, requires purification processes to separate methane from carbon dioxide, with adsorption-based methods being widely employed. Accurate simulations of these systems, governed by coupled PDEs, ODEs, and algebraic equations, critically depend on precise parameter determination. While traditional approaches often result in significant errors or complex procedures, optimization algorithms provide a more efficient and reliable means of parameter estimation, simplifying the process, improving simulation accuracy, and enhancing the understanding of these systems. This work introduces an Artificial Intelligence-based methodology for estimating the isotherm parameters of a mathematical phenomenological model for fixed-bed experiments. The separation of CO2 and CH4 is used as case study. This work develops an algorithm for parameter estimation for the system's mathematical model. The results show that the validated model has a close fit... [more]

A reinforcement learning framework is developed for the synthesis of distillation trains. The rigorous Naphtali-Sandholm algorithm for equilibrium separation modeling was implemented in JAX and coupled with the benchmarking Jumanji RL library. The vanilla actor-critic agent was successfully trained to build distillation trains for a seven-component hydrocarbon mixture. A transformer encoder structure was used to apply self-attention over the agent’s observation. The agent was trained on minimal data representation containing quantitative component flows and relative volatility parameters between present components. Training sessions involving 5·104 episodes (3·105 column designs) were typically run in under 60 minutes. While training was fast and reliable with appropriate tuning of the hyperparameters, further improvements are needed in the generalizability performance for similar separation problems.

Developing ultra- or high-performance liquid chromatography (HPLC) methods for analysis or purification requires significant amounts of material and manpower, and typically involves time-consuming iterative lab-based workflows. This work demonstrates in two case studies that an autonomous HPLC platform coupled with a mechanistic model that self-corrects itself by performing parameter estimation can efficiently develop an optimized HPLC method with minimal experiments (i.e., reduced experimental costs and burden) and manual intervention (i.e., reduced manpower). At the same time, this HPLC platform, referred to as Smart HPLC Robot, can deliver a calibrated mechanistic model that provides valuable insights into method robustness.

Catalysis design and reaction condition optimization are considered the heart of many chemical and petrochemical processes and industries; however, there are still significant challenges in these fields. Advances in machine learning (ML) have provided researchers with new tools to address some of these obstacles, offering the ability to predict catalyst behaviour, optimal reaction conditions, and product distributions without the need for extensive laboratory experimentation. In this contribution, the potential applications of ML in heterogeneous catalysis and photocatalysis are explored by analysing datasets from different reactions, including Fischer-Tropsch synthesis and photocatalytic pollutant degradation. First, datasets were collected from literature. After cleaning and preparing the datasets, they were employed to train and test several models. The best model for each dataset was selected and applied for optimization.

Operational decision-making in Process Systems Engineering (PSE) has achieved high proficiency at specific levels, such as supply chain optimization and unit-operation optimization. However, a critical challenge remains: integrating these layers of optimization into a cohesive, hierarchical decision-making framework that enables sustainable and automated operations. Addressing this challenge requires systems capable of coordinating multi-level decisions while maintaining interpretability and adaptability. Multi-agent frameworks based on Large Language Models (LLMs) have demonstrated significant promise in other domains, successfully simulating traditional human decision-making tasks and tackling complex, multi-stage problems. This paper explores their potential application within operational decision-making for PSE, focusing on sustainability-driven objectives. A realistic Gas-Oil Separation Plant (GOSP) network is used as a case study, mimicking a hierarchical workflow that spans from... [more]

Process synthesis is a fundamental step in process design. The aim is to determine the optimal configuration of unit operations and stream flows to enhance key performance metrics. Traditional methods provide just one optimal solution and are strongly dependent on user-defined technologies, stream connections, and initial guesses for unknown variables. Usually, a single solution is not sufficient for adequate decision-making, especially, when properties such as flexibility or reliability are considered in addition to the process economics. Wastewater Treatment network synthesis and design is a complex problem that demands innovative approaches in design, retrofits, and maintenance strategies. Considering this, an enhanced framework for improving reliability in wastewater transportation networks based on graph theory and machine learning is presented. Machine learning models were developed to predict failure probability, where the XGBoost model provided the best predictions. To select t... [more]

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]

Currently, state-of-the-art approaches to solving complex optimization problems have focused solely on methods requiring high computational time and unable to find the global optimal solution. In this work, a methodology based on quantum computing is presented to overcome these drawbacks. The novelty of this framework stems from the quantum computer’s architecture and taking into consideration the quantum phenomena that take place to solve optimization problems with specific structure. The proposed methodology includes steps for the transformation of the initial optimization problem into an unconstrainted optimization problem with binary variables and its embedding onto a quantum device. Moreover, different resolution levels for the transformation step and different architectures for the embedding process are utilized. To illustrate the procedure, a case study based on Haverly’s pooling and blending problem is examined while demonstrating the potential of the proposed approach. The res... [more]

Optimization under uncertainty is inherent to many PSE applications ranging from process design to RTO. Reaching process true optima often involves learning from experimentation, but actual experiments involve a cost (economic, resources, time) and a budget limit usually exists. Finding the best trade-off on cumulative process performance and experimental cost over a finite budget is a Partially Observable Markov Decision Process (POMDP), known to be computationally intractable. This paper follows the nonmyopic Bayesian optimization (BO) approximation to POMDPs developed by the machine-learning community, that naturally enables the use of hybrid plant surrogate models formed by fundamental laws and Gaussian processes (GP). Although nonmyopic BO using GPs may look more tractable, evaluating multi-step decision trees to find the best first-stage candidate action to apply is still expensive with evolutionary or NLP optimizers. Hence, we propose modelling the value function of the first-st... [more]

One of the most successful continuous high-performance liquid chromatography (HPLC) processes for drug manufacturing is the Simulated Moving Bed (SMB). SMB is a multi-column, continuous, chromatographic process that can handle much higher throughputs than regular batch chromatographic processes. The process is initially transient, but eventually arrives at a cyclic steady state, which makes optimization very challenging, even more so when superstructure optimization is involved. To simplify the optimization problem, many researchers fixed the SMB structure, optimizing only the continuous variables, so they cannot be considered superstructure optimization. In this work, an SMB superstructure that can simultaneously optimize column structure and operation is proposed. The results showed that the superstructure proposed is reliable, and it is more efficient compared to current optimization approaches if the optimal column structure has to be identified.

Uncertainty plays a crucial role in strategic supply chain design. In this study, we explore robust approaches to model uncertainty when the non-deterministic parameters are placed in the independent term, on the right-hand side (RHS) of the constraints. We consider the "disjunctive adjustable column-wise robust optimization" (DACWRO), a disjunctive formulation introduced previously in our group, and compare it with the adjustable column-wise robust optimization (ACWRO) formulation, a specific technique for solving robust optimization problems when the original robust optimization approach may assume too-conservative results. Given that the proposed method is based on the generalized disjunctive programming (GDP) technique, it is a higher lever modelling approach that represents the discrete nature of the decision process. In addition, it provides alternative MILP representations that can be further tested and compared. The analysis assesses the computational performance and reformulat... [more]