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]

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]

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]

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]

As greenhouse gas emissions accelerate global warming, new capture and storage technologies are essential for reducing the industrial CO2 concentration in the atmosphere. This study addresses the urgent need for greenhouse gas capture technologies by developing a detailed dynamic mathematical model for Chemical Looping Process with Integrated Combustion and CO2 capture (CL-ICCC). In the CL-ICCC process configuration, CO2 capture is integrated into the chemical looping combustion system, resulting in a higher-purity, more efficient process. In this work Cu/CuO oxygen carrier material and CaO/CaCO3 sorbent materials were considered in a fixed bed reactor as solid phase to investigate Oxidation and Reduction/Calcination processes under different operating conditions. The simulation results were compared with experimental results from the literature. In case of the oxidation process, a sensitivity study was performed to investigate the behavior of the process for variation of different ope... [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]

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]

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]

Electrochemical reduction of CO2 (ERCO2) in gas diffusion electrode (GDE)-based electrolyzers represents a potential strategy for global decarbonization, achieving simultaneously the valorization of this abundant carbon resource. While significant progress has been achieved in enhancing CO2 conversion in these systems, further advances are required to enable their practical implementation at the industrial scale. Physics-based simulations offer a powerful tool to guide the optimization of design and operating parameters as well as for the efficient scale-up of CO2 electrolyzers. In this work, we have developed a three-dimensional multiphysics model of the cathodic compartment of a GDE electrolyzer for ERCO2 to formate. For that purpose, the software COMSOL Multiphysics has been used. The model is experimentally validated, confirming its accuracy at reproducing current density and Faradaic efficiency at cathode potentials in the range -1.2 V and -1.7 V. Moreover, kinetic parameters are... [more]

Chemical processes are characterized by high complexity and inherent hazards, necessitating systematic and comprehensive risk assessment methodologies. However, traditional risk assessment approaches are often limited to static analyses and small-scale models, resulting in insufficient coverage and a lack of detailed, unit-level insights. This paper proposes an automated Bayesian network-based dynamic risk assessment (DRA) framework for full-process risk analysis in chemical plants. The proposed method automatically extracts knowledge from established risk analysis documents, such as HAZOP reports, and integrates it with dynamic process monitoring data, expert knowledge, and reliability databases. By effectively leveraging multi-source heterogeneous data, an equipment-level causal inference network is constructed automatically, addressing the high labor demand and limited scalability of conventional DRA approaches. The proposed framework is validated through an industrial FCC case stud... [more]

Simulation and modeling technologies play an increasingly critical role in enhancing feedstock utilization, improving product quality, and enhancing enterprise competitiveness in the refining industry. Molecular-level kinetic models for refining processes typically involve highly detailed and complex data, significantly increasing computational costs. Additionally, the data granularity within these models fails to align with the practical requirements of industrial production. To address these, this study proposes the Reaction Network Coarse-Graining Integration (RNCGI) algorithm to achieve flexible adjustments to model complexity and data granularity. Species with similar molecular structures and kinetic behaviors within the reaction network are identified and integrated. Subsequently, a kinetic parameter mapping approach is developed to preserve the critical reaction behaviors. Finally, the reaction coarse-graining process significantly reduces the number of reactions within the netw... [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]

Biopharmaceutical manufacturing requires systematic identification, understanding and control of critical process parameters (CPPs) and critical quality attributes (CQAs) While deterministic machine learning models have proven valuable for describing complex systems and providing insights into process behaviour, the extension to probabilistic frameworks allows for the capture of intrinsic biological and process variability, improving robustness, understanding and safety. This work introduces a framework for autoregressive variational Bayesian neural networks (BNNs) that integrate uncertainty quantification into data-driven modelling. The approach is demonstrated on a multicolumn countercurrent solvent gradient purification (MCSGP) process for monoclonal antibody purification. The BNN is trained autoregressively on high fidelity data generated using a detailed mechanistic process model data. A heteroscedastic variance head is included to model signal-dependent observation noise, while t... [more]

Manufacturing kinetically stable products remains a challenge in the agrochemical industry. Current agrochemical formulation design relies on semi-empirical and trial-and-error methods. The inconsistency is caused by the lack of a mechanistic understanding of the formulation, making the design a black-box optimisation problem. In addition, validating the ground truth of the high-dimensional design space is expensive, driving chemists to explore possible solutions using data-driven methods. We proposed a Bayesian optimisation framework employing a Gaussian process as the surrogate model to intelligently guide the screening of the design space. The uniqueness of our framework is the application to the classification task to increase the number of hits of stable formulation recipes. The framework was tested on a provided industry dataset with a focus on emulsifiable concentrates. The performance reached a comparable accuracy with only ~25% of the data being sampled and hit more stable for... [more]