Power-to-ammonia (P2A) provides a carbon-free alternative to conventional ammonia production by replacing fossil-based feedstocks with electrolytic hydrogen and nitrogen from air separation. For decentralized P2A systems, pressure swing adsorption (PSA) offers a flexible alternative to cryogenic air separation. However, its industrial implementations are largely proprietary, and open, first-principles models capable of simulating its cyclic, nonlinear transport are scarce in literature. This work presents a first-principles, dynamic, one-dimensional model of a PSA superstructure for nitrogen generation, formulated with thermodynamically consistent equations of state, coupling multicomponent mass, energy, and momentum balances with kinetically limited adsorption on carbon molecular sieves. The resulting system of partial differential-algebraic equations is semi-discretized using the finite volume method, integrated using diagonally implicit Runge-Kutta methods, and cyclic steady states... [more]

This work presents the scale-up and detailed analysis, including comparison of relevant key performance indicators (KPIs) and energy analysis, of three innovative technologies for producing biomethane and its subsequent upgrading. The SEMPRE-BIO project tested and validated three different innovative technologies and pilots within the Horizon Europe framework. The three pilots are in Spain (biomethanation of biogas from wastewater treatment fermentation), France (biomethanation of syngas from biomass gasification), and Belgium (purification of biogas from manure anaerobic digestion). The three case studies will be presented and discussed. The analysis will dive into the layout of the optimised process layout of the scale-up plants as well as an exhaustive comparison, presenting advantages, shortcomings and bottlenecks of each technology, accounting for the main KPIs, i.e., electricity and steam demand, consumables including cooling water and others specific to the technologies.

Traditional approaches for mechanistic modelling require in-depth understanding of the underlying chemical and physical phenomena to construct reliable and predictive models. However, at early stages of development, limited experimental data, incomplete expert knowledge, and non-observable states often hinder a full understanding of the underlying mechanisms. Symbolic regression (SR) enables systematic model discovery and offers a practical route to addressing these challenges by automating the identification of interpretable model structures and the estimation of associated parameters from available data. However, structural identifiability and observability (SIO), a critical property of such models, is often overlooked in SR, thereby limiting its broader adoption and effective deployment. To address these limitations, this study proposes a comprehensive framework, which leverages scarce prior knowledge in SR and incorporates SIO analysis, offering a potential solution to capture the... [more]

The climate crisis and dependence on fossil fuels make the transition to renewable energy sources imperative, with biomass standing out for promoting decarbonization and circular economy. In this context, fluidized bed gasification emerges as an efficient route for converting waste into syngas, applicable to power and hydrogen generation. Given the variability of real operating conditions, dynamic models are essential to represent coupled fluid dynamic, thermal, and kinetic phenomena over time. In this work, a dynamic phenomenological model was developed using a lumped 0D approach, in which the reactor is divided into two interacting zones represented as continuous stirred-tank reactors (CSTRs): a dense bed, where drying, devolatilization, and heterogeneous reactions occur, and a freeboard, dominated by homogeneous gas-phase reactions. The model was validated against experimental data from a bubbling fluidized bed gasifier, showing good agreement for major syngas species (CO and H2, me... [more]

Carbon dioxide hydrogenation to produce methane, as an energy carrier or raw material, has great potential for the chemical industry. Since methanation reaction is strongly exothermic and sensitive to diffusion, radial flow reactors represent a clear solution thanks to their low pressure drop and effective heat removal. A two-dimensional mixing cell network (MCN) approach to model the carbon dioxide methanation in a radial flow reactor is proposed. The reaction is catalyzed by a bi-functional Ni-Ce zeolite 13X supported catalyst, combining catalytic and adsorption functions. This contribution outlines the ongoing work, starting from a straightforward MCN pseudo-homogeneous approach comparing it with a tubular packed bed reactor. Both methanation kinetics and water adsorption have been successfully implemented in both models, setting feasibility for further improvements. Future developments will be necessary aiming to aid the design of units employing Ni-Ce/13X catalysts.

Copper refining via electrolysis is a core metallurgical process that takes place in tankhouses, subject to strict temporal, spatial, and operational constraints. The efficiency and stability of this process depend critically on the coordinated scheduling of crane operations responsible for handling anodes, cathodes, and auxiliary tasks. In industrial practice, crane scheduling must simultaneously satisfy long-term production targets and short-term operational feasibility, while respecting process-dependent timing constraints imposed by electrochemical parameters. Inefficient or inconsistent schedules can lead to process delays, suboptimal resource utilization, and degraded electrolysis performance, ultimately affecting product quality and operational stability. This paper presents a modeling approach for optimizing tankhouse operations. The uniqueness of this case lies in the broad range of constraints, including human capacity, energy restrictions, metallurgical rules, and logistical... [more]

The valorization of lignocellulosic biomass represents a key pathway toward sustainable chemical production, as it enables the development of circular economy products with reduced dependence on fossil resources. Among the platform molecules derived from biomass, furfural stands out as a versatile intermediate that can be transformed into several high-value chemicals, such as furfuryl alcohol, 2-methylfuran, tetrahydrofurfuryl alcohol, furan, tetrahydrofuran, and maleic anhydride. In this work, an integrated biorefinery scheme for producing the main furfural derivatives is proposed and evaluated through process simulation and sustainability metrics. The process was modeled in Aspen Plus V14, using furfural obtained from lignocellulosic biomass (corn stover) as raw material, following hydrogenation and oxidation routes. The process is multi-product, meaning that the main furfural derivatives are produced simultaneously within the same plant. This was achieved by implementing splitters t... [more]

Liquefied Natural Gas (LNG) plays a strategic role in the global energy transition, as it represents a less carbon-intensive alternative to coal. Separation of CO2 from raw natural gas is a critical step for meeting LNG specifications and enabling Enhanced Oil Recovery (EOR) in offshore fields. However, high CO2 concentrations and formation of a CO2 ethane azeotrope increase the process complexity, often requiring extractive distillation with heavier hydrocarbons. Severe limitations are faced in offshore environments due to their weight, volume and high energy consumption. Due to that, Process Intensification (PI) seeks to enhance heat and mass transfer efficiency, potentially reducing equipment volume and weight. Rotating Packed Beds (RPB) have demonstrated significant potential for intensifying LNG purification by using centrifugal forces to drive liquid through a porous medium in contact with a gas stream. Experimental measurements of total pressure drop, and local liquid holdup are... [more]

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]