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 optimization of chemical reactions requires navigating a high-dimensional design space composed of both discrete and continuous variables. Although one-hot encoding has been widely adopted, it lacks chemically meaningful information and suffers from sparsity and poor generalization. To address these limitations, we explored the use of pretrained molecular foundation models to generate latent representations as input variables for optimization. However, rigorously comparing different combinations of reaction representations and optimization algorithms remains a time- and resource-intensive challenge. In this work, we developed an end-to-end benchmarking platform that systematically evaluates diverse encoding schemes and optimization strategies under identical conditions. The platform automates the entire workflow from data preprocessing to result analysis, supporting fair comparison across multiple representation-optimizer combinations. Furthermore, we designed a custom reaction rep... [more]

The growing penetration of renewable energy sources and power-to-hydrogen (P2H) systems demands high-fidelity, large-scale optimization frameworks that capture the nonlinear physics of both AC power flow and hydrogen thermodynamics. However, existing approaches rely on DC approximations and simplified electrolyzer models, neglecting critical operational constraints. As a result, accurately modeling such systems leads to large-scale nonlinear programs that are computationally intractable for conventional CPU-based solvers. This motivates the need for scalable optimization frameworks capable of handling both physical fidelity and computational complexity. This paper proposes a fully GPU-native framework for solving large-scale multi-period AC optimal power flow (AC-OPF) problems with integrated power-to-hydrogen systems. High-fidelity thermodynamic models of hydrogen production, compression, cooling, and storage are coupled with AC power flow constraints, resulting in large-scale nonline... [more]

This work explores symbolic regression (SR) as an interpretable surrogate modelling approach for the multi-objective optimisation of pressure swing adsorption (PSA) systems for CO2 capture. A first-principle model was used as a virtual plant to generate synthetic datasets covering the operating space defined by cycle step durations. Two surrogate frameworks were developed and compared: SR models derived through evolutionary search and deep neural networks (DNNs) trained via Hyperband-based tuning. Both surrogates were used as simulation models within an optimisation procedure based on a particle swarm optimisation (PSO) algorithm to maximise CO2 purity and recovery. While DNNs achieved the lowest prediction errors (MSE ˜ 10-6), the SR surrogates provided compact analytical representations and significantly faster optimisation. The SR framework yielded a denser and more diverse Pareto front (4345 vs 508 points). It was about 34 times faster (38.6 s vs 1331 s), confirming its efficiency... [more]

The scheduling of multi-line polyolefin production is a complex decision-making process characterized by sequence-dependent changeovers, strict physicochemical constraints, and dynamic market environments. Traditional optimization methods often suffer from high computational costs and a lack of flexibility in online adjustments. To address these challenges, this paper proposes a Deep Reinforcement Learning (DRL) framework for dynamic scheduling tasks. We first construct a high-fidelity simulation environment that meticulously models realistic industrial constraints, including transition materials, shutdowns, and inventory limits. A Soft Actor-Critic (SAC) agent with a tuple-based action space is employed to mitigate the combinatorial explosion associated with multi-line decisions. Furthermore, a dynamic action masking mechanism embedded with domain knowledge is introduced to strictly enforce hard constraints and significantly improve sample efficiency. Case studies based on real-world... [more]

Ammonia has received in recent years significant attention as potential carbon free fuel. However, its combustion properties limit its direct application for both providing heat and in power generation through gas turbines. Ammonia cracking is one potential solution to circumvent the problem by producing hydrogen. When using the ammonia in gas turbines, it is possible to heat integrate the endothermic decomposition reaction with the exhaust gas from the gas turbine. Thermodynamic and kinetic limitations have however a major impact on the achievable ammonia conversion. Based on the consideration of these limitations, this paper presents a detailed investigation of key design parameters affecting the overall process efficiency utilizing both an equilibrium reactor model and a reactor model based on detailed kinetics and heat transfer. Ammonia decomposition should occur at sufficiently high pressure to avoid a) the com-pression energy demand for achieving the pressure of the combustion ch... [more]

The state task network (STN) representation is a widely used modeling approach for optimal multipurpose batch production scheduling. In practice, STNs have been traditionally formulated as mixed-integer programming (MIP) problems and solved using general-purpose MIP solvers relying on branch-and-bound and branch-and-cut. In the meantime, alternative modeling and solution paradigms for optimization have been developed, enabling the incorporation of alternative variable types and optimization algorithms. Specifically, this work relies on the Hexaly software, which introduced set-based models and their solution through general-purpose hybrid algorithms, i.e., methods that combine traditional MIP with constraint programming, local search, large neighborhood search, among other tools. So far, Hexaly has shown promising results when tackling optimal scheduling problems, however, set-based models and solution approaches for STN optimization have not been studied in the literature. Aiming to f... [more]

Renewable powered water electrolysis offers a promising strategy to decarbonize industrial sectors with high demand for hydrogen. Operational optimization of industrial electrolyzer systems is often formulated as mixed-integer linear programming (MILP) problems, where a constant hydrogen production to electrical power consumption ratio is assumed instead of the nonlinear relationship. Incorporating a nonlinear electrolyzer model into a linear optimal hydrogen dispatch framework remains a significant challenge. This study addresses this challenge by formulating the optimization problem in two ways. First, the model is solved as a nonlinear programming (NLP) problem by incorporating a nonlinear model of an alkaline electrolyzer (AEL) into the optimization framework. The binaries and integers are relaxed to continuous variables and associated penalty terms are added to the objective function to enforce integrality. The NLP is solved using the local nonlinear solver Interior Point OPTimize... [more]

This paper presents a Europe-wide optimisation framework for designing and operating a multi-period Carbon Capture and Storage (CCS) supply chain across Europe. A MATLAB preprocessing pipeline constructs an auditable techno-economic dataset (emission nodes, ports, aquifers, candidate pipeline/shipping arcs and costs) and exports it to a GAMS optimisation model. The planning problem is formulated as a two-stage stochastic MILP, where scenario-independent first-stage decisions select discrete pipeline and shipping capacity bands and port operating modes, while scenario-dependent second-stage decisions allocate capture, transport and sequestration flows. Uncertainty is represented through correlated scenarios of capture unit costs for four capture technologies (CV=0.35, rho=0.8, Ns=20). To address the computational burden induced by inter-temporal binary investments and scenario replication, we apply a two-phase arc-screening heuristic: an LP relaxation on the full network identifies prom... [more]

Pharmaceutical supply chains face increasing pressure to deliver high service levels while meeting environmental and social expectations. Vaccine supply chains amplify these challenges due to strict cold-chain requirements, demand uncertainty driven by acceptance and preferences, and the urgency of public-health objectives. This paper develops a multi-objective mixed-integer linear programming (MILP) framework for national-scale vaccine distribution that explicitly integrates economic cost, service level, greenhouse-gas emissions, and population-level vaccine effectiveness. Behavioral realism is incorporated by modeling vaccine acceptance and brand preferences as operational constraints rather than ex-post indicators. Trade-offs are explored using an e-constraint method that preserves the MILP structure and enables systematic recovery of Pareto-optimal solutions. The framework is applied to a 52-week national case study for metropolitan France during the 2021 COVID-19 vaccination campa... [more]

While classical heuristics can be applied to decide on the preferred reactor concept for simple reaction schemes, more complex reaction networks require more sophisticated methods, such as the multilevel reactor design approach or superstructure optimization. Based on an analysis of the existing methods a nonlinear programming framework for a superstructure-based reactor network synthesis is presented, emphasizing numerical robustness and flexible network representation without relying on integer decisions. The approach, which is implemented in GAMS, allows for the combination of continuous stirred-tank and cross-flow reactor models. An exemplary application for the classical Van de Vusse reaction is first shown for validation, prior to the application to an enzymatic cascade based on the Weimberg pathway. Assuming fast co-factor regeneration, the performance of the resulting PFR cascade, which can also be interpreted as a sequence of batch reactions, is compared with a commonly applie... [more]

Shell-and-tube heat exchangers are critical assets in the process industries, yet their design optimization typically relies on deterministic formulations that ignore operational variability. This study presents a systematic comparison of four optimization approaches, spanning three paradigms for decision-making under uncertainty, applied to shell-and-tube heat exchanger design. The objective minimizes total annualized cost (comprising capital and pumping costs) subject to thermal duty, pressure drop, velocity, and geometric constraints. Six uncertain parameters are modeled across three categories: mass flowrates (coefficient of variation = 10%), inlet temperatures (coefficient of variation = 2%), and fouling resistances (uniform distribution). Shell-side heat transfer is computed via the Bell-Delaware method, while tube-side correlations follow Sieder-Tate. The four approaches benchmarked are: (1) deterministic optimization as a baseline, (2) sample-average approximation, a classical... [more]

Sohar Port and Freezone is well-positioned to drive Oman`s economic future through continued expansion, increased reliance on sustainable energy, and the integration of advanced digital tools. Sohar contributes 25% of the current GHG emissions in Oman. Decarbonizing Sohar industrial clusters is a strategic challenge for emerging economies striving to attain net-zero objectives. This research develops a superstructure optimization framework for designing an integrated Carbon Capture, Utilization and Storage (CCUS) value chain, specifically applied to the Sohar Port and Freezone in Oman. A mixed-integer linear programming (MILP) formulation that simultaneously selects capture technologies, identifies optimal transport modes (pipeline, trucking, and shipping), and allocates CO2 flows among utilization and geological storage alternatives. The novelty of this work lies in the multi-scale integration of techno-economic, spatial, and policy dimensions, coupled with GIS-based route generation... [more]

Flowsheet synthesis is an integral step in process design, entailing the selection of a set of unit operations and their connectivity to convert raw materials to products. Superstructure optimisation represents a promising class of synthesis approaches, allowing for the systematic exploration of the flowsheet design space. Despite this, many superstructure formulations suffer from numerical instabilities, combinatorial explosion, and/or rely on restrictive assumptions on the types of flowsheet alternatives that can be considered. The modified state-operator network (MSON) formalism has recently been proposed to address some of these issues for isobaric flowsheets. The constant-pressure assumption restricts the applicability of the MSON to real process applications as pressure is a key process variable in many unit operations, such as distillation, reaction, and extrusion, and is necessary to elicit flow. In this work, we present the extended MSON (E-MSON) which inherits the numerical s... [more]

A topology optimization approach is applied to the design of a compact fluid distribution device that achieves uniform flow distribution among parallel channels while minimizing the mean residence time under a pressure loss constraint. Such devices are widely used in applications including parallel microreactors, thermal management systems, and energy devices, where both compactness and precise flow control are required. The design problem is formulated using a density-based method in which the design variable represents the local void fraction of a fictitious porous medium. The governing equations are based on the incompressible Navier-Stokes equations with an additional Darcy-type resistance term. The objective function is defined as the spatially averaged design variable, corresponding to the minimization of the effective flow passage volume and thus the mean residence time. Constraints are imposed on both flow rate uniformity among parallel channels and the total pressure loss. A t... [more]

In this study, we propose a method to uniquely determine robust operating conditions for simulated moving bed (SMB) chromatography, an essential continuous liquid-phase separation technique in the pharmaceutical industry, in the form of explicit algebraic equations. The proposed method incorporates process robustness-defined as the probability of meeting the target purities under flow-rate uncertainty due to pump errors-without requiring computationally expensive dynamic simulations. In a computational demonstration, the method achieved a joint probability of 0.960 for simultaneously attaining 99.9% purity in both extract and raffinate products.

Anaerobic digestion (AD) across Europe and the UK faces increasing economic and operational pressure from volatile feedstock supply under climate extremes. Existing stochastic programming (SP) approaches for feedstock planning often rely on limited historical observations and/or simplify yield uncertainty in ways that miss the joint, non-linear response of crops to weather variability, thereby understating downside supply risk. We develop an integrated decision-support framework that links climate uncertainty to AD procurement planning by coupling mechanistic crop simulation, generative surrogate modelling, and stochastic optimization. First, APSIM is used offline to generate a mechanistic yield knowledge base across weather trajectories and discrete planting-density choices. Then, a conditional GAN (CGAN) is trained to produce non-parametric joint yield samples for multi crops conditioned on scenario features and management, enabling fast Monte Carlo evaluation. At last, these samples... [more]

Aluminium cold rolling is an energy-intensive process that has a substantial impact on CO2 emissions and production cost, yet plant-level optimization remains challenging due to strong process nonlinearities and various operational constraints. This study develops a physics-consistent hybrid model that combines a Stone-Hitchcock-Ludwik analytical rolling-energy formulation with a residual deep neural network to predict the daily electricity consumption of three single-stand cold rolling mills. Using plant raw data, the hybrid model achieves lower prediction errors than conventional data driven model and yields line-specific physical parameters that agree well with the observed behaviour of each mill. On this basis, an NSGA-II-based tri-objective optimization is carried out to minimise daily energy use, CO2 emissions, and specific production cost (SPC) by adjusting pass-wise reduction and tension schedules and line-wise production allocation. Case studies on a representative operating d... [more]

To improve the energy efficiency of distillation processes, various process intensification concepts have been proposed, including direct heat integration and thermal coupling. Identifying the most suitable alternative for a given separation task requires a rigorous and consistent techno-economic optimization. Superstructure models typically rely on isobaric operation and fixed HETP values, in order to avoid treating column hydraulics when solving the already challenging mixed-integer nonlinear optimization problems. In order to overcome this limitation and evaluate the effect of the simplification, the current work extends a rigorous equilibrium-stage superstructure model to account for tray-specific pressure drop and HETP values. A polylithic solution approach is implemented to improve the convergence for the resulting optimization problems. The proposed approach is demonstrated for the optimization of heat-integrated distillation sequences operated at close to atmospheric and vacuum... [more]

The pipeline transportation and distribution process of natural gas, from production sources to end consumers, can be divided into three stages: upstream production and supply, midstream storage and pipeline transportation, and downstream distribution and end-use. In the optimization of natural gas pipeline networks, considering the full life cycle, multiple uncertainties in planning, design, operation, and maintenance often affect the efficiency and quality of model optimization. This study addresses the uncertainty in end-user demand during the operation of natural gas pipeline net-works and investigates a scheduling method that simultaneously meets user demand while achieving coordinated optimization of operational costs and carbon emissions. Based on one year of historical demand data, a normal distribution is fitted to characterize the demand, and several representative scenarios with corresponding probabilities are extracted through clustering to capture demand uncertainty. A two... [more]

Batch production scheduling of resources to meet fluctuating product demand is a critical topic in the process industry. Existing optimisation approaches, based on heuristic and exact methods, trade off solution optimality and scalability to large problems. In this work, we investigate deep reinforcement learning as a powerful alternative in order to learn heuristics for batch scheduling. We formulate the batch scheduling problem as a Markov decision process operating on a state-task network representation encoded using graph neural networks, capturing relevant structural inductive biases. We propose a centralised training with decentralised execution architecture, in which agents placed on machines individually choose which tasks to complete using a global view of the network, cooperating towards task schedules that optimise the final production quantity. Preliminary results demonstrate that the proposed end-to-end framework learns to construct task schedules comparable to the optimal... [more]

This work aims to systematically generate intensified process designs by integrating reinforcement learning (RL)-driven process synthesis and phenomena-based modeling via Generalized Modular Framework (GMF). Rather than considering flowsheet synthesis with conventional unit-operations, GMF utilizes fundamental building blocks, also known as mass and heat exchange modules, to describe the physiochemical phenomena and to enhance novel process discovery. At its core are driving forces which characterize the mass transfer feasibility based on the total change in Gibbs free energy of the system. RL is integrated with this phenomena-based modeling strategy to drive flowsheet generation by exploring much of the total action space and minimizing pre-postulation of stream connections. All possible inlets, outlets, and interconnections between modules are contained in a stream matrix. Deep Q-Network is used as the RL agent, which contains a multi-layer convolution neural network followed by a mu... [more]

Although solvent-based crystallization is widely adopted for separation and purification of crystalline pharmaceutical products, solvent choice and utilisation critically influence product quality, manufacturing cost, and the environmental performance of the pharmaceutical process. Escalating demands to reduce process mass intensity (PMI), together with increasing vulnerabilities in the supply chains, necessitate the development of more efficient and resilient process designs, incorporating solvent and active pharmaceutical ingredient (API) recycling. The conceptual design of crystallization processes offers a viable route to identify flowsheets with substantially reduced solvent consumption. In this paper we present a computer-aided molecular and process design (CAMPD) formulation to explore the benefits of solvent/API recycle for two processes/APIs: (i) a continuous cooling crystallization process for mefenamic acid (MA) employing a binary solvent mixture and (ii) a batch cooling cry... [more]

In recent years, research for systematic process design approaches has gained traction, especially with the rise in popularity of generative machine learning models and reinforcement learning. However, works from the literature will often focus on proof-of-concept studies, limited to a specific process synthesis problem. Despite showing promising results, it is not clear how easily these methodologies could be transposed to new applications, and whether they would be successful. In this context, this work evaluates the possibility of using a Natural Language Processing model, which has already proven itself for thermodynamic cycle generation, for another different case: seawater desalination. The processes generated by this model will initially be those using reverse osmosis processes aimed at desalinating a seawater solution containing 25000 ppm of NaCl. Results show that the model has been successful in designing structural reverse osmosis desalination processes without defining asse... [more]

Heat Pumps (HPs) can play a vital role in the decarbonization of heating in industry. The performance of a HP strongly depends on the refrigerant, the working fluid within the HP. In order to maximize HP performance, systematic selection of the refrigerant is key. Refrigerant choice affects the very feasibility of employing a HP to deliver heating to a process. A flexible and robust method is required to select refrigerants that are the best fit for a given heating application. A computer-aided molecular & process design (CAMPD) method is developed to design the optimal refrigerant that is tailored to process needs. The method is applied to three case studies across which the HP performance objectives and constraints, and heat source and heat sink temperatures are varied. In addition, the design of refrigerants with low (<150) global warming potentials and zero ozone depletion potentials is investigated. For all applications across all case studies, the CAMPD approach successfully iden... [more]