The growing integration of renewable energy sources such as solar and wind power has intensified the demand for advanced energy storage technologies. Lithium-air (Li-O2) batteries are particularly attractive due to their exceptionally high theoretical specific energy, which surpasses that of the conventional lithium-ion system. However, their practical application is hindered by poor reversibility during discharge, primarily due to the formation and decomposition of lithium peroxide (Li2O2), which causes cathode passivation and capacity fading. Since the electrochemical performance of Li-O2 batteries is strongly influenced by the morphology, size, and spatial distribution of Li2O2 crystals, understanding the mechanisms governing their nucleation and growth is critical. To address this challenge, this work proposes a computational framework based on population balance modeling (PBM) to describe Li2O2 crystallization dynamics during battery discharge. The framework integrates population,... [more]

Pyrolysis is an emerging green pathway to produce bulk chemicals and sustainable fuels. However, pyrolysis oil requires stabilisation via hydrotreatment, and this process generates an aqueous waste containing alcohols (mainly methanol and ethanol), carboxylic acids, and some ketones. To increase the economic sustainability of biofuels production, there is increasing interest in recovering these valuable chemicals from water. Reliable thermodynamics are necessary to address the separation and design of equipment to fractionate such complex mixtures, with multiple azeotropes and non-idealities. The Non-Random Two-Liquids (NRTL) models in both Aspen Plus V12.0 and COFE V3.7, a license-free software released by AmsterChem, do not accurately reproduce the equilibrium measurements of most of the binary and ternary mixtures involving water, a C1-C4 alcohol, and a light carboxylic acid. This work aims to retune the activity-based model to improve the NRTL model predictivity, using experimental... [more]

The zero-pollution ambition of the European Union requires improvements in wastewater treatment to meet increasingly stringent regulations at achievable cost. One promising approach consists in model-based optimal control. However, wastewater treatment plants involve highly nonlinear and time-varying processes, making existing mechanistic models such as the Benchmark Simulation Model no.1 (BSM1) challenging for direct use in online control. Therefore, this study explores a hybrid modeling approach using the Hammerstein-Wiener (HW) structure. The proposed model combines a mechanistic steady-state model, derived from BSM1, with a data-driven approximation of the system dynamics, incorporating low-order linear dynamic models. In this work, the HW model was used as a surrogate for BSM1. The HW surrogate model attained coefficients of determination (R2) often exceeding 0.95 across key water quality indicators, such as total nitrogen and ammonium concentration. This accuracy was found to be... [more]

As economies advance towards decarbonization, industry follows suit. The ammonia (NH3) sector heavily relies on the energy- and carbon-intensive Haber-Bosch (HB) process, which accounts for nearly 2% of global CO2 emissions due to its reliance on fossil fuels. Emerging technologies are paving the way for fully renewable NH3 production, although the most mature green process still relies on the HB process, entirely replacing fossil fuels with electrolytic green hydrogen (H2). This work introduces the first developments towards the gradual incorporation of green H2 and air-separated nitrogen (N2) into a conventional NH3 production plant. Using Aspen Plus® V14 for modelling and simulation of Steam Methane Reforming (SMR), different scenarios incorporating 0 to 40 % of these alternative feedstocks were analyzed. An economic objective function is used in each scenario's optimization. To improve green H2 incorporation and ensure operational constraints were met, the simulations used an adapt... [more]

This study develops a multi-objective computer-aided process engineering (CAPE) framework to evaluate integrated sugarcane-based agro-industrial systems combining a high-yield cultivar, Haru-no-Ougi, and the "Inversion Process, " which reverses the conventional order of sugar crystallization and ethanol fermentation through selective fermentation of reducing sugars. The in-house CAPE tool SugaNol integrates agricultural, industrial, and environmental (life cycle assessment) models to simulate productivity, energy balance, greenhouse-gas (GHG) emissions, and relative economic performance on a per-hectare basis. Four scenarios were analyzed: NiF8-Conventional, KY01-2044-Conventional, Haru-no-Ougi-Conventional, and Haru-no-Ougi-Inversion Process. Simulation results showed that the combined Haru-no-Ougi and Inversion Process system increased total energy-equivalent productivity by approximately 40-45% compared with the baseline NiF8 system. Life cycle GHG emissions were reduced by 4-11%, w... [more]

Building a robust calibration curve is essential for accurate quantification of multicomponent mixtures. Matrix effects can distort the proportional relationship between the analyte concentration and instrumental response. In addition, classical machine learning models do not inherently incorporate simple physical constraints, such as the requirement that a zero response must correspond to a zero concentration, which can result in non-zero predictions. To address this limitations, prior-induced calibration models were proposed that inherently embed this physical constraint into the model architecture. A dataset was generated for ethanol electrooxidation products using headspace gas-chromatography-mass spectrometry (HS-GC-MS). Multiple linear regression (MLR), polynomial regression and artificial neural network (ANN) models were trained to investigate the effects of model complexity and the incorporation of physical information on predictive performance. Model selection and complexity w... [more]

Ethanol derived from biomass is a promising renewable fuel; however, its long-term use as a gasoline additive is becoming increasingly uncertain due to the rise of electric vehicles and alternative propulsion technologies. This trend motivates the exploration of higher-value applications for ethanol, particularly in the food and pharmaceutical sectors, where product safety is critical. A key challenge in ethanol purification is breaking the ethanol-water azeotrope, as conventional entrainers such as ethylene glycol or glycerol can leave residual traces that limit ethanol's use in sensitive markets. Magnesium chloride (MgCl2) offers an effective alternative, enabling high-purity ethanol without introducing hazardous organic residues, while exhibiting favorable hygroscopic properties and operational reliability. Simulating this system is challenging due to strong non-ideal and electrolyte interactions in phase equilibrium. Conducting a rigorous controllability analysis is also difficult;... [more]

Polylactic acid (PLA) is the most produced bioplastic, however, for it to compete with fossil-based plastics, maximum production efficiency is crucial. To achieve this, all scales, from catalyst scale to process scale, must be simultaneously considered. This challenge is particularly relevant for PLA production, where complex interactions of multiple phenomena occur in several unit operations and the development of active non-toxic catalysts is of major importance. For these reasons, developing a framework that allows a comprehensive understanding of the influence of design choices at different levels is of paramount importance. To address this, a multi-scale model was developed for the PLA production, coupling a custom devolatilizer reactor model to a process simulator that is subsequently linked to an environmental assessment tool via Python-based interfaces. With this model, sensitivity analysis was performed to assess the influence of operational variables on the most relevant KPI'... [more]

We present a steady-state, optimization-based techno-economic study of a continuous fluidized temperature-swing adsorption (TSA) system for post-combustion CO2 capture from biomass-derived flue gas, using two adsorption stages and one desorption stage with integrated heat-pump thermal management. The GAMS/CONOPT4 model couples molar and energy balances, Toth adsorption equilibrium, fluidized-bed hydrodynamics and literature cost correlations. Optimization yields CO2 purity of 96% v/v and 95.5% recovery at low, safe pressures with particle Reynolds numbers of 2-11, indicating near-minimum-fluidization operation. The nominal capture cost is 87 USD/tonCO2 with an internal rate of return of 42%; utilities comprise 49% of annualized costs and the adsorption compressor dominates equipment capital. Disabling the heat pump increases modeled capture cost to 124 USD/tonCO2, highlighting the heat pump's decisive role in reducing energy demand and costs. Adding adsorption stages lowers modeled cos... [more]

Heat exchangers frequently suffer from fouling, which is the accumulation of unwanted deposits on heat-transfer surfaces. This issue reduces thermal performance, increases pressure drop, and raises energy use and operating costs. Predicting fouling resistance remains challenging in process engineering, yet it is important for monitoring, maintenance planning, and mitigation actions that reduce economic losses and environmental impacts. Symbolic regression (SR) is a machine learning approach that searches for an explicit mathematical expression that best represents the relationship between process inputs and a target output. Unlike many black-box models, SR can capture nonlinear behavior while producing compact, interpretable equations that are easier to deploy and analyze in industrial settings. In this work, a methodology to rapidly obtain algebraic models for fouling resistance in industrial heat exchangers using SR was proposed. Plant measurements of hot- and cold-side flow rates an... [more]

This work presents a preliminary application of a developing methodology for assessing the operational flexibility of ammonia synthesis loops. Part of this methodology involves systematic dynamic testing in the synthesis loop. In the present case, a synthesis loop equipped with a two-bed, quench-cooled converter operating under variable feed conditions was considered. A high-fidelity model of the converter was developed in gPROMS Process using two-dimensional reactor models from its fixed-bed catalytic reactor library, and a synthesis loop configuration was modeled and designed in the same environment. A series of dynamic tests varied the make-up flow rate across four disturbance amplitudes (±25% and ±50%) and four disturbance durations (10 s, 600 s, 1800 s, 3600 s). The results showed that disturbances of ±50% magnitude led to the violation of one process operational constraint. These findings enable the construction of a preliminary operational map of this system, providing an initia... [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]

Activity coefficients are key thermodynamic quantities for describing phase equilibria, but their experimental determination entails laborious and costly phase-equilibrium measurements, making predictive approaches highly desirable. The potential of machine learning for such predictions has received growing attention as an alternative to physics-based models that require experimental data or expensive calculations for parameterization. We propose a physics-informed edge-enhanced graph attention network (PEGAT) to predict activity coefficients in multicomponent mixtures, where each molecule is encoded as a graph in which the nodes correspond to atoms and the edges to chemical bonds. The excess Gibbs free energy of the mixture is predicted using the proposed model, including a nonlinear transformation in the final layer to ensure that the excess Gibbs free energy vanishes for pure components. To further enforce thermodynamic consistency, the relevant activity coefficients are obtained vi... [more]

This work presents a data-driven surrogate modeling framework for predicting distillation behavior assuming an infinite number of stages and distillation limits informed by residue-curve topology and pinch-point feasibility analysis. The framework provides a direct mapping from feed composition and distillate-to-feed ratio (D/F) to distillate and bottom product compositions, making it suitable for flowsheet synthesis and optimization applications. The approach combines three components: a classifier that identifies feasible singular-point splits, a boundary regression model that predicts D/F limits separating pure- and mixed-product operating regimes, and a neural network that interpolates product compositions in the intermediate regime. The method is demonstrated for the ternary system ethanol, benzene, and water at 1 atm using data generated from rigorous vapor-liquid-liquid equilibrium analysis. Results show that the framework provides reliable predictions for pure splits while reta... [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]

The rapid electrification of the energy and transport sectors has increased the demand for efficient thermal management of Lithium-Ion Batteries (LIBs). LIBs operate optimally within a narrow temperature range (15-40 °C), effective Battery Thermal Management Systems (BTMS) are essential to prevent accelerated aging and performance degradation. Immersion cooling using dielectric fluids has emerged as a promising solution; however, a systematic comparison of their thermal and hydraulic performance remains limited. This study evaluates six dielectric fluids-Silicone Oil, Mineral Oil, Sunflower Oil, AmpCoolAC-100, E5-TM410, and Deionized Water-using Computational Fluid Dynamics (CFD) simulations of a lithium-ion battery pack composed of 32 fully immersed 18650 cells. The analysis focuses on key thermophysical properties, particularly the Prandtl number and thermal diffusivity, as criteria for coolant selection. The results demonstrate a strong correlation between cooling performance and th... [more]

Predictive modeling of surfactant properties is constrained by limited experimental datasets, a common challenge in specialty chemical development where property measurements require specialized equipment and significant time investment. In this study, we address data scarcity through a semi-supervised generative augmentation framework that leverages both labeled and unlabeled molecular data for surface tension prediction. We implemented a two-stage variational autoencoder (VAE) training strategy using a curated database of 600 non-ionic surfactants. First, 461 unlabeled surfactant structures were used for VAE pre-training to learn latent representations capturing molecular connectivity patterns and amphiphilic relationships. Second, 125 molecules with surface tension measurements were used for fine-tuning to embed property-structure relationships. Our stratified generation framework produces surfactants matching target property distributions (Wasserstein distance = 0.030, KS statistic... [more]

Developing predictive, high-resolution microbial models that retain mechanistic insight remains a central challenge in biochemical engineering. This paper addresses the challenge of preserving metabolic information while ensuring model accuracy and proper statistical definition. It employs the Hybrid Cybernetic Modelling (HCM) approach to integrate metabolic regulation with genome-scale information, and dynamically predict E.Coli phenotypes. The main aim of this work is to explore HCM's parameter identifiability to advance its accuracy and robustness for a limited set of data. To bypass the computational burden of computing the elementary flux modes, the opt-yield Flux Balance Analysis (opt-yield FBA) is employed to identify a physiologically relevant set of yield-maximising metabolic pathways. Metabolic Yield Analysis (MYA) then reduces this to four key pathways which capture 99% of the original metabolic yield space. The cybernetic model is formulated where "artificial enzymes" are a... [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.

Direct Air Capture (DAC) is recognised as a critical climate mitigation technology necessary for achieving global net-zero emissions by balancing difficult-to-avoid emissions. Despite its importance, the commercial deployment of DAC technology is currently challenged by the substantial energy demands and resultant extremely high operational costs associated with handling the low atmospheric CO2 concentration. This study aims to address these two challenges through dynamic process modelling, simulation and rigorous multi-objective optimisation of a solid sorbent-based temperature vacuum swing adsorption (S-TVSA) cycle. The system utilises an advanced amine-functionalized sorbent, selected for its favourable low-temperature regeneration kinetics.A technical performance assessment was conducted using a first-principle mathematical model to accurately simulate mass and heat transfer in the adsorbent beds. To improve system viability, a multi-objective optimisation with NSGA-II in MATLAB wa... [more]

Catalyst deactivation significantly affects reactor performance, process efficiency, and economic viability in chemical processes. The precise estimation of kinetic and deactivation parameters in transient tubular reactors is essential but remains challenging due to strong parameter correlations, nonlinear dynamics, and limited prior knowledge of parameter values. Model-based Design of Experiments techniques for improving parameter precision (MBDoE-PP) has been shown to enhance parameter identifiability by optimally designing informative transient experiments even when catalyst deactivation occurs. However, MBDoE-PP is highly sensitive to parameter misspecification and can lead to suboptimal or infeasible solutions under model uncertainty, in which is the typical case in reaction systems exhibiting catalyst deactivation. In this work, a robust MBDoE framework for parameter precision (RMBDoE-PP) is proposed to explicitly account for processmodel parameter mismatch (PMPM) during the expe... [more]

This work proposes a parsimonious dynamic filtration model for superheated steam containing paper-derived dust, suitable for parameter identification, prediction, and future optimization under limited observability. The model is based on Darcy's law, with the total resistance expressed as the sum of the intrinsic filter resistance and a time-dependent fouling contribution. Experimental data obtained from a dedicated superheated-steam filtration setup were used for parameter estimation and model validation under a single operating condition. Assuming a linear dust dosing rate, the model yields limited agreement with experimental data (R² = 0.24). By estimating the time-varying solid loading through minimization of the sum of squared errors between measured and predicted pressure drop, the agreement improves significantly (R² = 0.94). This demonstrates that uncertainties in the dust dosing rate strongly affect pressure drop predictions. The proposed model provides a foundation for extend... [more]

Renewable-to-X products such as methanol (MeOH) and dimethyl ether (DME) offer scalable, carbon-neutral options for decentralized chemical production. Microreactors, with superior heat and mass transfer, provide more controllable reaction environments. This improved control enhances selectivity and conversion, making microreactors particularly well suited for intensifying CO2/CO hydrogenation within a Power to X framework for synthetic products. However, an assessment of MeOH and DME synthesis routes under microreactor operation is still lacking. To address this gap, a microreactor-scale model was developed where two reactor configurations were analyzed: i) parallel configuration, in which MeOH synthesis and subsequent dehydration to DME take place in the same reactor, and ii) series configuration, in which MeOH synthesis is carried out in the first reactor, followed by MeOH dehydration to DME in a second reactor. To capture realistic process behavior, the simulations incorporated non-... [more]

Near-infrared (NIR) sorting systems in waste sorting plants operate under multiple settings, creating distinct input-output relationships that challenge predictive modeling. Conventional neural networks, such as multilayer perceptron (MLP), often suffer from catastrophic forgetting under continual training, limiting reliability across settings. This study evaluates Kolmogorov-Arnold Networks (KAN) for continual regression modeling of multi-setting NIR systems. KAN assign nonlinear transformations to network edges using localized spline grids, enabling structural isolation between input regions. We introduce controlled input-space manipulations (shifting successive settings to adjacent or non-overlapping grid regions) and compare KAN performance with MLPs of comparable parameter count. We also examine single-input versus multi-input configurations to assess dimensionality effects. Results show that KANs with sufficient input-space separation maintain previously learned knowledge with pe... [more]

Hybrid process models are increasingly used for real-time decision support in dynamic operations, where models must remain reliable under changing operating conditions. In such settings, models are often required to extrapolate beyond previously observed batch trajectories, yet conventional validation strategies may fail to reveal weaknesses in extrapolative behavior. This work investigates the effect of hybrid model structure on extrapolative performance using a pilot-scale batch crystallizer as a test case. A structured set of mechanistic and hybrid models is evaluated using nested batchwise leave-one-out cross-validation (NBLOOCV), in which entire batches are withheld to assess extrapolation across operating regimes. For this specific application, the results show that hybrid models employing simple linear correction terms consistently outperform more flexible neural network-based formulations under extrapolation, despite comparable training performance. For the studied process, the... [more]