The identification of predictive mathematical model structures (i.e. set of model equations) is essential for the development of digital twin models of chemical reactor systems. Recent work demonstrated the use of artificial neural networks (ANNs) for kinetic model recognition in a conceptual batch reaction experimental system. In practical chemical processes, however, system behaviour is governed not only by reaction kinetics but also by reactor hydrodynamics and system thermodynamics. While a very recent study incorporated hydrodynamic effects, this work integrates the three aspects: reaction kinetics, reactor hydrodynamics, and system thermodynamics, to develop a general reactor modelling recognition framework. The framework, which comprises three modules: 1) model generator module; 2) data generation module; and 3) ANN classifier module, was applied to a case study of benzoic acid esterification in a Taylor vortex flow reactor system. Analysing the framework's sensitivity, results... [more]

The pharmaceutical sector is essential for ensuring universal access to medicines, demanding ef-ficient supply chains that deliver drugs at optimal prices with minimal delays and shortages. Pharmaceutical supply chains (PSCs) face significant challenges, including strict quality controls, government regulations, drug perishability, high R&D costs, and complex transportation require-ments. The sector is undergoing a shift, driven by the rise of pharmaceutical components in emerging markets, unpredictable demand, and reduced R&D investments by major companies, which struggle to compete with generic pharmaceutical brands. Post-pandemic challenges and geopolitical risks have further exposed vulnerabilities in PSCs, leading to frequent supply disrup-tions, product shortages, and unreliable transportation. The increasing focus on regionalization highlights the need for more resilient supply chains to manage disruptions effectively. PSCs must incorporate robustness to address uncertainties an... [more]

Despite the growing economic importance of the fragrance industry, perfume formulation remains largely guided by empirical knowledge and iterative trial-and-error approaches. The structured design of fragrances, typically organised into top, middle, and base notes through the blending of perfume raw materials, is therefore time-consuming, costly, and difficult to generalise. Existing computational approaches have begun to address this challenge, but are commonly limited to small ingredient sets or require extensive sensory data that are not always available. This work proposes a computer-aided optimisation framework for Eau de Parfum formulation that simultaneously maximises perceived olfactory intensity and minimises formulation cost. The resulting optimisation problem is formulated to preserve the structural balance of top, middle, and base notes inherent to the perfume pyramid. Application of the framework to a fruity-floral Eau de Parfum formulation demonstrates a substantial incre... [more]

Styrene is mainly produced by dehydrogenating ethylbenzene over an iron oxide-based catalyst. The reaction is endothermic and thermodynamically limited when operated in conventional catalytic fixed-bed reactors. This makes the styrene production process a conversion-selectivity trade-off, where different objectives must be compromised. In this work, a one-dimensional reactor model accounting for changes in molar flowrate, temperature, and pressure is used to predict the performance of a membrane reactor. Three main hydrogen flux profiles were assumed along the reactor axial direction: constant, linearly increasing, and linearly decreasing. It is found that the styrene yield and selectivity in a membrane reactor operated with a linearly decreasing hydrogen flux profile are higher than those with constant or linearly increasing hydrogen flux profiles in both isothermal and nonisothermal cases. It is also observed that the styrene yield and selectivity of the membrane reactor operated wit... [more]

Stochastic gene regulatory networks exhibit complex dynamics that require efficient methods for parameter inference and uncertainty quantification. In this work, we propose a surrogate modelling framework that combines a partial integro-differential equation (PIDE) formulation with polynomial chaos expansions (PCE) to efficiently approximate the stochastic dynamics of gene expression models under parametric uncertainty. The approach represents the time evolution of low-order statistical moments as polynomial functions of uncertain kinetic parameters, enabling fast evaluations and tractable inference. The method is demonstrated on a self-regulating gene network, achieving accurate parameter estimation and a reduction of approximately two orders of magnitude in computational cost compared to direct PIDE-based optimisation.

Multiscale models provide a powerful framework to link bioprocess operation conditions with polymer microstructure, yet their predictive capability for polymer attributes such as chain length distributions (CLDs) remains limited. In this work, an advanced multiscale modeling framework for the microbial production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) in Cupriavidus necator is presented, targeting the quantitative prediction of polymer microstructure. The model consistently integrates a structured macroscopic kinetic description of substrate uptake, biomass growth, and copolymer accumulation with an explicitly formulated microscopic polymerization model resolving initiation, propagation, termination, and depolymerization reactions of living and dead chains. A central contribution of this study is the quantitative calibration of the polymerization kinetics based on experimental size-exclusion chromatography (SEC) data. Polymerization rate constants were identified by fit... [more]

The need to reduce greenhouse gas emissions and diversify energy sources has driven the development of hydrogen (H2) as a leading carbon-free energy vector with high gravimetric energy density (33.3 kWh/kg) for stationary and transport applications. However, its storage and transportation remain challenging. Therefore, ammonia emerges as a promising hydrogen carrier due to its high energy density and ease of liquefaction and transport. This work presents the development of a phenomenological based mathematical model for an ammonia electrolysis cell operating in a zero-gap configuration with an anion exchange membrane. The model considers the contributions of different overpotentials (activation, ohmic, concentration) and incorporates empirical and semi-phenomenological expressions adjusted to experimental data from Zhang et al [1]. The model shows both qualitative and quantitative agreement with experimental measurements, achieving a determination coefficient (R2) of 0.9874. Beyond ele... [more]

The pharmaceutical industry is placing growing emphasis on sophisticated process modeling to enhance the efficiency of drug design and production pipelines. Optimal control over these models can significantly improve manufacturing performance by lowering costs, boosting productivity, and ensuring rigorous quality compliance. However, the intricate nature and heavy computational load of these models often require the adoption of more practical or simplified alternative strategies for optimisation such as simulation-based approaches. In this work, we introduce a simulation-based framework including a "top-level" gradient-based mathematical programming optimisation model coupled with a "low-level" simulation scheme, to optimise multi-scale drug substance manufacturing flowsheets. The proposed framework optimises critical quality attributes, such as yield and purity, including green metrics such as process mass intensity, aligning with digital platforms (e.g. gPROMS) used in the pharmaceut... [more]

Geologic hydrogen has emerged as a promising low-carbon energy vector, but its sustainable recovery requires effective stimulation and production strategies. This study presents an integrated process-modeling framework for evaluating hydrogen extraction from hydraulically stimulated reservoirs. The framework combines fracture propagation, damage evolution, Darcy-scale multiphase flow, permeability-aperture dynamics, and a dual-porosity dual-permeability (DPDP) representation to simulate hydrogen production in fractured source rock systems. In addition to production dynamics, the framework tracks operational water and energy inputs and incorporates a simplified reaction-extent formulation to represent hydrogen generation under data-limited conditions. The model was benchmarked against published shale gas production results to evaluate its ability to reproduce fracture-controlled production behavior and was subsequently applied to a representative multi-well hydrogen development scenario... [more]

The volumetric gas-liquid mass transfer coefficient (kLa) is a key performance parameter in stirred tank reactors and is commonly determined through extensive experiments across the operational space. This work presents an automatic, closed-loop framework for kLa determination based on model-based design of experiments (MBDoE), in which agitation and aeration inputs are adapted in real time.During each experiment, dissolved oxygen data is collected and used to estimate the parameters of a Van't Riet kLa relation. The parameter uncertainty is quantified using the covariance matrix, and the experiments are iteratively selected based on D-optimality or E-optimality MBDoE, until a threshold of RSEi < 0.15 is reached for all parameters. The MBDoE approach is evaluated through repeated runs and compared against random designs, full factorial (FF) design, and a full grid design.The results demonstrate that the closed-loop MBDoE framework can significantly reduce the number of experiments requ... [more]

Bioprocesses are inherently multiscale, spanning intracellular metabolism to production-scale reactors. Simulation models that integrate these scales offer potential strategies to study the effect of changing metabolic states and enable efficient integration of biological knowledge gathered from lab-scale experiments. In this study, we demonstrate the potential of such simulation model towards the production of mevalonate, an important pharmaceutical drug compound produced through fermentation of a fungal species Aspergillus terreus. We integrate a genome-scale metabolic model of the organism with a plant-wide simulation model for the bioprocess that encompasses several upstream and downstream unit operations. Through this integration, we identify potential targets for metabolic engineering towards increased product flux and simultaneously estimate the associated oxygen requirements. This framework serves as a foundation for developing digital twins of bioprocesses that bridges strain... [more]

This work presents Pareto Front Guided Sampling (PFGS), a model-guided Design of Experiments (DoE) strategy for bioprocess development that makes the exploration-exploitation trade-off explicit and integrates human expertise into experiment selection. Starting from an initial experimental design, PFGS fits a probabilistic surrogate and then proposes new experiments by solving a multi-objective design problem that simultaneously rewards (i) high predicted performance (posterior mean) and (ii) high information gain (posterior uncertainty). Rather than collapsing this trade-off into a single acquisition value, PFGS generates a Pareto set of candidate experiments, that reflect different balances between improvement-seeking and learning. To prevent wasted runs, an automated screening step is performed to remove candidates in (i) low predicted-mean regions unlikely to yield near-optimal performance and (ii) low-uncertainty regions already well explained by the surrogate, concentrating effort... [more]

Decarbonization of hydrogen-intensive industrial clusters is essential to meet the European Union's net-zero targets. Although hydrogen can replace fossil-based feedstocks and fuels in refineries and chemical industries, its production remains largely dependent on natural gas. Therefore, cost-effective and low-emission supply routes require a system-level approach that integrates regional resources, technologies, and industrial demand. This study applies a multi-objective optimization framework to design a low-carbon hydrogen supply system for Galicia (northwestern Spain), addressing two gaps in regional energy system modeling: model transferability across regions and integration of social criteria beyond techno-economic assessment. The model quantifies trade-offs between total system cost and greenhouse gas emissions, and an employment indicator is integrated via post-processing using TOPSIS. The results show that meeting 100% of the projected 2030 demand (105 kt H2/a) yields a single... [more]

The aviation sector is difficult to decarbonize due to limits on aircraft electrification, making sustainable aviation fuel (SAF) a critical near-term solution. This study integrates Aspen-based process modeling with game-theoretic optimization to design a multi-agent SAF production network comprising coal gasification and CO2-assisted natural gas reforming for syngas production, and Fischer-Tropsch (FT) synthesis for SAF production. Techno-economic parameters from Aspen simulations inform an agent-based model in which agents maximize their net present value subject to capacity and demand constraints. Three decision-making frameworks are compared: (i) social welfare optimization, (ii) cooperative bargaining - symmetric (equal bargaining power) and asymmetric (bargaining power weighted by agents' competitiveness outside cooperation), and (iii) competitive equilibria modeled as generalized Nash equilibrium. The results show that social welfare maximization excludes coal and yields the hi... [more]

Human activities have already transgressed several planetary boundaries, yet energy system models remain largely focused on greenhouse gas mitigation, reflecting their original purpose of addressing climate change. Recent integrations of Planetary boundary-based Life Cycle Assessment into Energy System Optimisation Models show that even cost-optimal low-carbon pathways systematically violate multiple planetary boundaries, indicating that supply-side decarbonisation alone is insufficient for absolute environmental sustainability. At a 2050 horizon, where energy supply is largely decarbonised and technologies are assumed mature, further impact reductions through techno-economic optimisation become limited, positioning final energy demand as a key remaining lever for restoring feasibility under planetary constraints. To address this gap, we ex-tend an Energy System Optimisation framework coupled with a Planetary Boundary framework by explicitly treating final energy demand as a decision v... [more]

"Green" hydrogen production via polymer electrolyte membrane (PEM) electrolyzers must overcome significant energy penalties and high costs to become competitive in renewables-based energy systems. Adaptive operating strategies for PEM electrolyzers-by dynamically adjusting current density, pressure, and temperature-have demonstrated efficiency improvements in simple energy systems. However, their effectiveness in the context of complex power-to-X energy systems featuring variable downstream synthesis processes remains unclear. This work shows that integrated optimization of PEM electrolyzer operating parameters in conjunction with downstream methanation processes (MP) delivers substantial system-wide efficiency and cost benefits under dynamic hydrogen demand and pressure conditions. To demonstrate this, an equation-oriented process model of a PEM electrolysis system is embedded within a higher-level energy system model to compare sequential optimization (where the electrolyzer adapts t... [more]

Heat pumps offer the possibility of reducing CO2-emissions in the chemical industry. However, the integration of heat pumps, especially in non-continuous processes, faces several challenges. Energy storage facilitates a way to enhance heat integration by providing a continuous supply of heat flows. By doing so, the question arises as to whether this implementation should be applied to the process or to the utility level. At the process level, there is usually more freedom, as one is not bound by the existing temperature levels of the utility system, which are mostly difficult to retrofit. Therefore, this study presents an approach that generates heat integration concepts at the process level based on two different criteria. These criteria influence which process streams are grouped for a storage implementation and therefore influence the heat integration. The aim is to maintain the heat flows as continuous as possible by integrated heat storages. Finally, the possible heat integration... [more]

The reduction of CO2-emissions in the chemical industry is essential to meet European climate targets. Particularly, the reliance on fossil fuels for process heat supply is a key factor for CO2-emissions. Electrically driven compression heat pumps are a promising option to reduce fossil fuel consumption by upgrading low-temperature waste heat to a higher temperature level, provided that low-carbon electricity is available. However, the integration of heat pumps into chemical utility systems remains a challenge due to economic constraints and the high complexity associated with site-wide heat integration and retrofit of existing structures. This work presents a mixed-integer linear programming (MILP) approach for the optimization of utility systems with integrated heat pumps. To address computational complexity, candidate utility temperature levels are pre-selected, and feasible heat pump coefficients of performance (COP) are precomputed. The framework is applied to both greenfield and... [more]

This study presents the modeling and operation optimization of an adsorption heat storage to improve the supply of renewable heat to a house. The system configuration is an open system with water being carried by an air flow and adsorbed on zeolite 13X beads in a packed bed. A numerical model is developed based on mass and energy balances, using a Langmuir adsorption isotherm and a Linear Driving Force (LDF) mass transfer equation. The model is implemented in Pyomo and solved with the NLP solver IPOPT. A sensitivity analysis on the discretization parameters is performed to choose a good compromise between accuracy and computational time. The chosen model is then validated against experimental data from the literature, with a mean absolute percentage error less than 5%. The dynamic optimization of the operation of the system to satisfy a heat demand is then performed. The trajectory for the inlet fluid velocity is optimized in several heat demand scenarios. The results show that this nu... [more]

Behind-the-meter generation from variable renewable energy is a potential pathway for new data centers to obtain power more quickly and more sustainably than interconnecting to existing electrical grids. Energy storage is needed to accommodate the variability of wind and solar energy across multiple timescales. Hydrogen from electrolysis and ammonia made from this hydrogen can be used as fuel for dispatchable power generation while offering lower $/MWh storage costs than batteries. In this work, we analyze the economics of using hydrogen, and/or ammonia along with batteries in hybrid energy storage systems to enable data centers to be powered by 100% renewables. We perform this analysis using an optimization model for the selection, sizing, and coordinated hourly operation of constituent energy storage technologies toward minimizing the levelized cost of energy (LCOE). The model uses an hourly resolution scheduling horizon of five years to account for hourly, seasonal, and interannual... [more]

Green hydrogen constitutes a strategic energy vector for achieving the Sustainable Development Goals (SDGs 7, 9, 12, and 13) due to its high energy density, flexibility for renewable energy storage, and direct emission-free operation. However, its production critically depends on the supply of high-purity water, which is unsustainable in the context of a projected 40% global water deficit by 2030. Given that more than 97% of available water is saline, integrating desalination processes with electrolysis constitutes an essential strategy for transitioning toward circular economy models in water resource management. This work presents the conceptual design, detailed modeling, and optimization of an integrated process for the sustainable production of green hydrogen from saline water. The system couples a desalination technology (Solar Distillation) with two electrolysis technologies (AEL and SOEC), modeled through physicochemical, electrochemical, and thermodynamic principles. The object... [more]

When designing and planning Green Hydrogen Supply Chains (GHSCs), sustainability considerations are increasingly recognized as essential, particularly in light of decarbonization goals and climate policy targets. Existing research has largely focused on economic and environmental however, social sustainability aspects remain significantly underexplored. This work aims to develop a mathematical programming model to design a GHSC, considering simultaneously economic and social aspects. Solar PV, wind power, and PPA (wind) as energy sources are integrated, while transportation options include the construction of new pipelines, compared to the use of existing highways for trucks carrying liquefied or compressed hydrogen to deliver hydrogen to an oil refinery. The model is applied to a case study conducted in the Brazilian state of Bahia, where different social indicators will be explored, characterizing the case study context while allowing generalization to other contexts. Results allow u... [more]

The ecological transition demands innovative frameworks to reduce industrial resource consumption and environmental impacts. Industrial ecology, particularly through Eco-Industrial Parks (EIPs), provides a promising pathway by enabling exchanges of materials, energy, and water between firms. However, the deployment of EIPs is limited by the lack of standardized industrial profiles and transferable modeling approaches. This study develops a generic framework for representing industrial actors as standardized input-output black-box models, consolidating data on resource consumption, energy demand, by-products, and waste streams. These profiles are structured into a harmonized database to support resource-exchange analysis and scalable optimization across diverse contexts. Complementary mappings of processes and resources, as well as energy and heat demand profiles, enhance the feasibility of identifying synergies such as heat cascading and material reuse. The framework is designed to int... [more]

The integration of industrial symbiosis (IS) and carbon capture utilization (CCU) is recognized as a key strategy for achieving net-zero emissions in industrial parks (IPs). However, the optimization of these processes in combination remains an undeveloped research area. This work presents a multi-objective optimization framework implemented in Pyomo and linked with OpenLCA to simultaneously maximize material exchanges and minimize operational costs while evaluating CCU deployment. Applied to Portugal's largest industrial park, the model identified 26 feasible symbiotic exchange routes involving 14 enterprises and 7 potential CCU technologies. Maximum material exchange optimization yields 3,042,107 ton/year across 26 symbiotic routes and 7 CCU units, achieving 89.8 % reduction in climate change impact (from 13.5 to 1.76 million CO2eq/year); Cost minimization achieves 2,223,298 ton/year with 27 % fewer exchanges, delivering 87.0 % environmental reduction and net revenue of €65.5M/year v... [more]

The increasing popularity of electric vehicles (EVs) leads to an expected rise in the quantity of end-of-life lithium-ion batteries (LIBs) that require efficient management. This paper presents a State Task Network (STN) based optimization model to analyze and optimize the supply chain for LIBs, allowing for the selection of optimal processing routes, facility locations, capacities and reintegration of recovered materials, as well as analyzing the possible trade-offs between different end-of-life management strategies. Based on available data from the literature, the model is demonstrated with the LIB supply chain considering both primary production and different end-of-life strategies for spent LIBs (recycling and reuse). The case study reveals that mechanical pretreatment followed by hydrometallurgical recycling is the optimal pathway and it outperforms the linear supply chain in both costs and emissions. The cost optimal solution opts for more centralized collection and disassembly,... [more]