Capacitive Deionization (CDI) is an emerging electrochemical technology for energy-efficient brackish water desalination. However, the rigorous design and scale-up of CDI systems are frequently hindered by the complexity of validating predictive models. The coupling of electrochemical double-layer kinetics with macroscopic mass transport often leads to structural parameter correlations, where multiple combinations of kinetic rates yield indistinguishable effluent trajectories. This paper addresses these challenges by proposing a simulation-driven Model-Based Design of Experiments (MBDoE) framework. We develop and implement a reduced-order Dynamic Langmuir (DL) model within the gPROMS platform, designed to capture cyclic adsorption-desorption dynamics with high computational efficiency. Sensitivity analysis reveals that information content is highly transient, concentrated primarily in the short time windows following voltage switching, and that the effluent concentration is significant... [more]

Biological systems are governed by coupled interactions between intracellular metabolism and bioreactor operation that span multiple time scales. Constraint-based metabolic models are widely used to describe intracellular metabolism, but repeatedly solving the optimization problem at each time step in dynamic models introduces numerical challenges related to infeasibility and computational efficiency. This work presents a multi-scale modeling framework that integrates genome-scale, constraint-based metabolic models with dynamic bioreactor simulations. Intracellular metabolism is described using positive flux variables in a parsimonious flux balance analysis, and the resulting embedded optimization problem is replaced by a neural network surrogate. The surrogate provides a smooth approximation of the embedded optimization mapping and eliminates repeated linear program solves during simulation. The approach is demonstrated for fed-batch fermentation of Escherichia coli, in which the surr... [more]

Liquefied natural gas (LNG) plays a key role in the energy transition, but its production is often limited by the high CO2 content of some reservoirs, which increases operating costs and solidification risks. This study evaluates the dual-pressure low-temperature cryogenic distillation process applied to a recently discovered gas field with a high CO2 content (25%) for LNG production. The critical properties of the gas streams and the process operating conditions were analyzed using Aspen Plus v14. The results indicate that reducing the CO2 concentration throughout the column is essential to prevent solid formation by maintaining a fluid composition with a freezing temperature below the operating temperature of the stages. It was also observed that the reflux affects LNG purity and freezing temperature in all stages. Furthermore, the adoption of a low-pressure separator upstream of the distillation proved crucial to producing condensates within commercial specifications. The work exten... [more]

Energy Baselines (EnBs) provide a reference for evaluating Energy Key Performance Indicators (eKPIs), and their establishment is mandated under ISO 50001. Since eKPIs are typically defined per functional unit, such as product, recipe or recipe phase, EnBs should not be averaged across heterogeneous operating conditions but instead be defined in a context-specific manner. This requires detailed mechanistic models or sufficiently rich operational data for statistical approaches, both of which are often unavailable in highly flexible, semi-continuous production systems.This paper proposes a four-stage framework for the automated generation of surrogate EnBs to address this gap. In the first stage, the relevant training data space is defined, including non-influenceable variables (e.g., equipment deviations), design parameters (e.g., material properties), and adaptable recipe parameters (e.g., operating conditions and control actions). In the second stage, these parameters are systematical... [more]

Waste incineration with combined heat and power (CHP) supplies electricity and heat to cities and industrial clusters but remains a significant source of greenhouse gas emissions. This work develops an optimization-based, system-level decarbonization framework for an integrated waste-to-energy and chemical production cluster under operational, societal, and economic constraints. The framework is applied to a real-world case study including a municipal waste incineration plant, an energy utility system, and multiple chemical production units. A layered decarbonization strategy is implemented. First, energy efficiency is enhanced through waste heat valorization using heat pumps. Second, coordination between industrial actors is improved through solid waste storage management and operational alignment of heat and power supply with demand. Third, alternative feedstocks are introduced to reduce fossil-based inputs. Within the work material and heat-stream inventories are collected, and the... [more]

Reliable data for the standard enthalpies and Gibbs free energies of formation, DHf° and DGf° are essential for process synthesis, energy integration, and lost-work analysis. However, many biomass-derived compounds lack reliable thermodynamic property data, limiting optimization of energy and carbon utilization in biomass conversion processes. This study proposes a composition-based method to estimate DHf° and DGf° for compounds containing carbon, hydrogen, and oxygen. The method exploits widely available heats of combustion data and establishes a linear correlation between the enthalpy and Gibbs free energy of combustion, DHC and DGC using tabulated organic compounds. The applicability of this relationship to biomass-derived compounds is tested using published data for cellulose, starch, and glucose. Thornton's correlation between heat of combustion and oxygen demand is then incorporated to derive simple expressions for estimating formation properties directly from elemental compositi... [more]

Slug flow in multiphase production systems is a critical challenge in the oil and gas industry, characterized by complex and oscillatory dynamics, e.g., limit cycles. First-principle (FP) models often employ physics simplification, such as a virtual valve for the slug formation. To capture complex physics poorly modeled by FP models, hybrid models combine data-driven techniques and physical knowledge, such as the architecture known as universal differential equation (UDE). This work aims to employ a hybrid model based on neural networks to enhance the modeling of multiphase oil production systems. In the UDE model, a neural network is embedded within the structure of the FP differential equations. To demonstrate the feasibility of the methodology, the model was trained based on synthetic data, employing parameters estimated from OLGA simulations. Since the system faces oscillatory behavior, we trained the UDE in two stages: the first one employs smooth collocation on data to obtain an... [more]

Fouling is a persistent issue in industrial heat-transfer equipment, increasing energy demand and reducing efficiency. This is also true in the dairy industry where falling-film evaporators are central to powder production. Most dynamic models, however, neglect gradual fouling, limiting predictive accuracy during extended operation. As a result, model-based control can become unreliable when fouling becomes significant. The dynamic models by Bojnourd et al. [1] are widely used but assume clean-surface operation. While this captures short-term thermal behavior, it cannot represent the progressive decline in heat-transfer performance caused by fouling. Díaz-Ovalle et al. [2] introduced a fouling-layer model that explicitly describes the growth of a fouling deposit over time. Building on this concept, the present work incorporates a simple dynamic fouling model for falling-film evaporators and validates it using industrial data from a four-effect evaporator using thermal vapor recompressi... [more]

To produce complex injectable formulations, it is imperative that the contents of the formulation are not damaged during processing. Some formulations require the removal of excess solvent often through evaporation. Wiped Film Evaporators can be applied for these scenarios, since they can operate at relatively low temperatures with mass transfer being promoted through a large surface area. This is created by wiping the liquid against the inner heated wall of the equipment, with the proper operation requiring, as much as possible, a stable and continuous liquid film. In some cases, however, depending on operating conditions or physical properties of the materials, film ruptures and wetting/de-wetting dynamics are observed, and in more severe situations the liquid is dispersed in isolated globules and drops. These are undesirable situations of operation, where it is not possible to guarantee an optimal and homogenous heat and mass transfer. In this work, Computational Fluid Dynamic (CFD)... [more]

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 use of green hydrogen to reduce iron ore is a promising approach to drastically decrease CO2 emissions in the steel industry. To enable the rapid adoption of this technology, it is essential to start from the fundamentals, namely understanding the intrinsic kinetics of iron oxide reduction. In this work, a kinetic investigation was conducted in a PBRlike system using both pure and commercial iron oxide powders under a wide range of operating conditions. The thermal conductivity of the outlet gas was measured and innovatively correlated with the extent of solid reduction through a rigorous mathematical procedure. To simulate the reduction process and determine the kinetic parameters, a deterministic axial dispersion model was developed in conjunction with a dynamic multistep shrinking core model. The model incorporates the particle size distribution of the solid into the mass balance and includes a reactionfront control mechanism to ensure physical consistency during kinetic param... [more]

Process modelling is essential to improve carbon capture unit design. However, depending on the modelling decisions made, such as the thermodynamic model and calculation method, the results obtained may vary significantly, hindering reliable process design. Nevertheless, studies that decouple the effects of thermodynamic packages and model approximations on simulation results are not well established. This contribution focuses on clarifying these effects and providing guidelines for the simulation of a CO2 capture process with monoethanolamine (MEA) at different liquid-to-gas (L/G) ratios and CO2 partial pressures in Aspen Plus. The calculations are validated with eight pilot campaign runs. The analysis reveals that the use of rate-based with kinetic reactions significantly improves the accuracy of the simulations. This approach, combined with the ENRTL-RK thermodynamic model, using Peng-Robinson for the vapor phase, provides the best performance, with average deviations below 3% in te... [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]

The growing global energy demand and the need to reduce dependence on fossil fuels have intensified efforts toward developing renewable alternatives. Among these, biodiesel and ethanol emerged as viable and sustainable fuel sources. In this context, the use of palm oil and ethanol as raw materials represents a promising production route, due to their availability, high productivity per unit of cultivated area, and renewable characteristics. However, ethanolic transesterification still faces challenges, such as lower productivity compared to methanolic processes and higher energy consumption due to reaction characteristics that impact the whole process. Bearing this in mind, this work aims to develop process simulations in Aspen Plus to optimize biodiesel production from palm oil and ethanol, coupled with an energy integration analysis. The process was divided into three main stages: (i) feed preparation, (ii) transesterification reaction, and (iii) separation and purification of biodie... [more]

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]

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]