In this contribution, a thermodynamic model-based approach for the optimal design of a solid-state hydrogen storage and release system utilizing the reversible iron oxide/iron thermochemical redox mechanism is presented. Existing storage processes using this mechanism face significant limitations, including low hydrogen conversion, high energy input requirements, limited storage density, and slow charging/discharging kinetics. To address these challenges, a custom thermodynamic model using NIST thermochemistry data is developed, enabling an in-depth analysis of redox reaction equilibria under different conditions. Unlike previous studies, this approach integrates a multi-objective optimization framework that explicitly balances competing objectives: maximizing hydrogen yield while minimizing thermal energy demand. By systematically identifying optimal trade-offs, the study provides new insights into improving process efficiency and reactor design for thermochemical hydrogen storage. Th... [more]

A simple mathematical model of the co-precipitation of Ni-Mn-Co hydroxides is developed and applied to investigate the effect of pH, initial concentration of ammonia in the solution, concentration of the ammonia feed, nucleation rate constant and exponent, growth rate constant and growth exponent over the model output. The model is shown to produce a correct representation of the precipitation variables, and the general trends obtained for different sets of parameters are found in agreement with results presented elsewhere. A sensitivity analysis is carried out and the sensitivity indices are calculated. It is found that pH, initial concentration of ammonia and growth rate constant are the input parameters with the most relevant effect over the model input.

The pharmaceutical and specialty chemical industries are challenged with the requirement of faster time-to-process to meet market demands. Here, Modular Plants made up of predesigned process equipment assemblies (PEAs) are advantageous. Moreover, equipment-based Digital Twins of these modules can further reduce the time-to-process when combined with methods such as Quality by Design (QbD) and model-based design of experiments (MBDoE) to reduce uncertainty. This paper presents a lab scale-based workflow using an equipment-based Digital Twin, which applies QbD and MbDoE methods to identify the Design Space in the lab scale which can be transferred to production scale equipment as part of a Digital Twin based workflow for scale-up in Modular Plants.

Climate and environmental problems caused by increasing CO2 concentration in the atmosphere make the direct air capture (DAC) technology having great prospects for development. As the widely used solvent in carbon capture based on chemical absorption processes, MEA still fails to address the issues of high energy consumption and high costs when used in DAC process. In this study, piperazine (PZ) was used as the new solvent for DAC process. The new configuration was simulated in Aspen Plus® V11 and the model was validated through experimental data and model comparison. It is followed by investigation of the potential for energy efficiency and cost reduction. The standard DAC-PZ configuration could reduce the reboiler duty from 10.7 GJ/tCO2 to 8.9 GJ/tCO2 for DAC-MEA process. Economic analysis will be carried out through Aspen Process Economic Analyzer®. Further analysis (e.g. sensitivity analysis for different parameters and optimisation) will be performed to further reduce the energy c... [more]

Bipolar membrane electrodialysis are receiving the attention of the research community in the last years because they can help the electrification and the spread of direct air capture systems. In this work, a mathematical model of a bipolar membrane electrodialysis cell for carbon dioxide recovery is carried out in order to find the most significant parameters on efficiency through a global sensitivity analysis. The electrochemical cell can be integrated into an absorption column capturing carbon dioxide from the air. Results show that the most important parameter over all investigated figures of merit (specific energy consumption, costs, carbon dioxide desorption efficiency, potassium transport number, removal ratio of potassium cation and carbon) is the potassium cation concentration in the rich solution feeding the cell. A trade-off between energy efficiency, process speed and operational cost is suggested. Future research should be conducted in order to apply the global sensitivity... [more]

This study focuses on enhancing heat and shaft power integration within existing nitric acid production processes to optimize waste heat recovery and identify opportunities to improve process efficiency. A digital twin of the operational plant is utilized, which features a dual-stage pressure nitric acid production process with a capacity of 50 tons/h of HNO3 (100% equivalent). The authors conducted a simultaneous analysis of the thermal energy potential and the expansion capacity of tail gases to effectively fulfil the primary process's heating, cooling, and power requirements while increasing steam generation through waste heat recovery, all without compromising plant throughput. The proposed process modifications lead to a 23.8% reduction in cooling water usage and a 35.6% decrease in CO2 equivalent emissions while achieving a 13.1% increase in steam generation. These utility savings culminate in a 10.2% enhancement in plant throughput.

The deployment of CO2 capture technologies at a large scale will largely benefit from the knowledge acquired during pilot testing. A mobile CO2 capture pilot unit is currently being designed at the University of Liège. Here, the pilot plant is introduced, and the column sizing results are presented. The sizing was performed with a process model built in Aspen Plus. Overall, the pilot installation is expected to serve for process model validation, data collection and technology de-risking while assisting Belgian industries in their transition towards carbon neutrality.

As demand for long and accurate molecular simulations increases so too does the computation demand. Beyond using new, enterprise scale processor developments - such as the ARM neoverse chips – or performing simulations leveraging Graphics Processing Unit compute, there exists a potentially faster and more power efficient option in the form of custom hardware. Using hardware description languages it is possible to transform existing algorithms into custom, high performance hardware layouts. This can lead to faster and more efficient simulations but compromises on the required development time and flexibility. In order to take the greatest advantage of the potential performance gains, the focus should be on transforming the most computationally expensive parts of the algorithms. When performing molecular dynamics simulations in a polar solvent like water, non-bonded electrostatic calculations dominate each simulation step, as the interactions between the solvent and the molecular structu... [more]

Integrated Carbon Capture and Conversion (ICCC) technologies offer an efficient alternative to conventional Carbon Capture, Utilization, and Storage (CCUS) methods by simultaneously capturing and converting CO2 into value-added chemicals. Dual-function materials (DFMs) are particularly promising due to their capability to integrate adsorption and catalysis in a single step, thereby reducing both energy consumption and associated costs. This study models the dynamic behavior of CO2 adsorption within a laboratory-scale packed-bed reactor employing DFMs. The mathematical model incorporates momentum, mass, and heat transfer equations, implemented using COMSOL Multiphysics v5.6, and evaluates various axial dispersion models (ADMs) and mass transfer coefficients (MTCs). The results indicate that the Rastegar-Gu ADM, combined with an MTC of 8.3 × 10-2 s-1, provides the most accurate representation of breakthrough and saturation times, as well as the total quantity adsorbed. Furthermore, relat... [more]

This paper evaluates the thermodynamic efficiency of a newly synthesized large-scale pre-combustion CO2 capture process using a novel ionic liquid (IL) 1-octyl-2,3-methylimidazolium thiocyanate [OMMIM][SCN] for blue H2 production. In addition, the potential eco-toxicity of the selected IL was assessed using the ADMETlab 2.0 web tool. The results of these analyses were compared to those of an established IL 1-butyl-2,3-dimethylimidazolium bis(trifluoromethyl sulfonyl)imide [BMMIM][TF2N]. The eco-toxicity assessment confirmed that [OMMIM][SCN] is less environmentally toxic than [BMMIM][TF2N]. Thermodynamic analysis of the novel system shows the COOLER unit accounts for the highest energy demand; however, the [OMMIM][SCN] system demonstrates a 7.45% reduction in energy consumption in the COOLER unit compared to [BMMIM][TF2N]. The system experienced the highest exergy losses (irreversibilities) in the COOLER unit for [BMMIM][TF2N] (12982 kW) and in the flash separator unit for [OMMIM][SCN]... [more]

The co-gasification of biomass and plastic waste offers a promising pathway for sustainable syngas production, necessitating precise prediction of its composition to optimize efficiency. This study compares the performance of Aspen Plus models, including the thermodynamic equilibrium model (TEM) and restricted thermodynamic equilibrium model (RTM), with machine learning (ML) techniques, focusing on the support vector regression (SVR) for syngas prediction during steam and air co-gasification. Aspen Plus simulations provided valuable mechanistic insights, while the ML model demonstrated superior predictive accuracy. The SVR, enhanced by principal component analysis (PCA), significantly improved performance, achieved R² values of 0.879 for H2, 0.856 for CO, 0.859 for CO2, and 0.744 for CH4 on the testing dataset. It also outperformed other models in terms of RMSE, achieving exceptional precision for CH4 (0.0087), CO (0.0193), and H2 (0.0194). In contrast, RTM exhibited moderate accuracy... [more]

Process simulation is essential for optimizing chemical processes, offering a cost-effective alternative to the experimental approach. This study presents a co-current hollow fibre membrane model for CO2 separation, integrated into Aspen HYSYS® using the CAPE-OPEN standard. A one-dimensional boundary value problem (BVP) is solved through the shooting method, ensuring accuracy for complex gas separation processes. The unit is implemented in C++, facilitating interoperability, error handling, and optimization of key performance indicators like energy consumption and separation efficiency. Appropriate output variables are employed in the Aspen HYSYS® environment to enable direct sensitivity analysis and optimization within the process simulator. Results Sensitivity analysis results demonstrate that the co-current hollow fiber membrane unit improves methane recovery compared to a pressure swing water absorption (PSWA) column for biogas upgrading to biomethane. While membrane technology sho... [more]

Cell culture processes play a central role in the production of various therapeutic compounds. These processes are multiscale and highly complex, making them challenging to describe comprehensively using fully mechanistic models. In this study, we employ an integrated hybrid machine learning and first principles model to predict the viable cell density, product titer, and metabolite concentration profiles. We employ the concept of degree of hybridization, where we create a family of hybrid models each with increasing degree of process knowledge. Predictions from the feasible hybrid architecture were integrated with a genome scale metabolic model to evaluate the flux distribution of reactions related to the central carbon metabolism of the cell throughout the process duration. We demonstrate that the current approach not only reasonably predicts the bioprocess profile but also provides biologically relevant information that can uncover dynamics of intracellular metabolism which can open... [more]

Proton Exchange Membrane (PEM) fuel cells are gaining traction in automotive applications due to their efficiency and environmental benefits, but they face challenges such as high costs, degradation rates, and limited hydrogen availability. To address these issues, novel operational methods have been developed, focusing on customized designs rather than traditional uniform configurations. These advancements include the variable temperature flow field, which maintains high relative humidity without external humidification by leveraging internally generated water and heat, and graded catalyst loading, which enhances current density distribution. Additionally, complex flow fields have been designed using 3D metal printing to mitigate liquid water accumulation. These innovations have shown significant performance improvements, particularly when combined, demonstrating a 260% increase in current density at 0.6 V. These advancements hold promise for overcoming the limitations of conventional... [more]

The urgent need for sustainable energy drives the exploration of biomass and plastic waste co-gasification, a promising route for producing clean fuels and chemicals, reducing greenhouse gas emissions, and minimizing fossil fuel dependence. Modeling and simulation are vital for optimizing this process, particularly syngas yield, yet comparative studies on Aspen Plus modeling techniques for steam co-gasification are limited. This research addresses this gap by comparing three Aspen Plus strategies: thermodynamic equilibrium modeling (TEM), restricted thermodynamic modeling (RTM), and kinetic modeling (KM), for simulating the co-gasification of pine sawdust and polyethene (PE) with steam in bubbling fluidized bed gasifier (BFBG). The primary objective is to evaluate the effectiveness of each strategy in predicting the syngas composition under varying conditions. Three models were developed in Aspen Plus on the basis of each strategy, and their predicted syngas compositions were compared... [more]

Demographic shifts and increased automation in chemical plants are reducing the experience and skill levels of plant operators. Therefore, BASF has implemented Operator Training Simulators (OTS) to allow operators to practice and improve their skills in this safe and controlled environment. The OTS consists of a dynamic model of the process, a control system and safety logics. This paper describes the learnings from using OTS at BASF, where they are used to train operators in process understanding, optimization, procedural training, and disturbance handling. Benefits include reduced training costs, minimized risks and improved efficiency. Also organizational guidelines are provided to ensure that the mentioned benefits are realized in industrial practice. Additionally, high-accuracy OTS models support HAZOP, debottlenecking, and optimization studies.

Temperature regulating in liquid tanks is critical in the chemical industry and conventionally relies on sensor feedback. However, due to the complex thermo-hydrodynamics, unsensed local temperatures can deviate from desired thresholds, underscoring the need for improved tank temperature modeling. The absence of internal thermal or flow data, however, poses significant challenges for the development and validation of effective control strategies. In this study, a virtual model for regulating liquid tank temperature was developed using computational fluid dynamics (CFD). Adaptions were made mainly by involving (1) a simple on-off mechanism of feeding based on a virtual sensor to achieve temperature within the acceptable range and (2) the imposition of unfavorable temperatures on the walls representing ambient influences. Leveraging this virtual system, several new cases were simulated. The simulation results highlighted pronounced temperature non-uniformity, with discrepancies exceeding... [more]

The increasing global population has resulted in the scramble for more energy. Hydrogen offers a new revolution to energy systems worldwide. Considering its numerous uses, research interest has grown to seek sustainable production methods. However, hydrogen production must satisfy three factors, i.e., energy security, energy equity, and environmental sustainability, referred to as the energy trilemma. Therefore, this study seeks to investigate the sustainability of hydrogen production pathways through the use of a Multi-Criteria Decision- Making model. In particular, a modified Simultaneous Evaluation of Criteria and Alternatives (SECA) model was employed for the prioritization of 19 options for hydrogen production. This model simultaneously determines the overall performance scores of the 19 options and the objective weights for the energy trilemma in a South African context. The results obtained from this study showed that environmental sustainability has a higher objective weight v... [more]

In offshore environments, where space, weight, and energy efficiency are critical constraints, the effective removal of water from turbine exhaust gases is essential to enhance gas treatment processes. In this context, replacing conventional methods, such as molecular sieves, with supersonic separators (SSRs) emerges as a promising alternative. This study aims to determine the most suitable operating conditions and design parameters for water removal via supersonic separation (SS) in turbine exhaust gases (TxGs) on offshore platforms. Simulations were performed in Aspen HYSYS using a unit operation extension, based on typical TxGs compositions from offshore platforms. Key parameters, including operating conditions, separator dimensions, and shock Mach number, were evaluated to maximize efficiency while minimizing equipment footprint. The results indicated a water capture efficiency of 99.45%, demonstrating that SS technology is not only compact but also a viable and efficient alternati... [more]

Real-time carbon accounting is crucial for advancing policies that effectively meet sustainability objectives. This work introduces a carbon tracking tool specifically designed for the European electricity grid. The tool collects hourly data on electricity consumption and generation, cross-border power exchanges, and weather information to assess the real-time environmental effects of electricity use, employing locally-specific emission factors for the generation sources. It utilizes weather data from various stations across Europe to produce week-ahead forecasts of carbon intensity in the grid. Predictions are created using a random forest regressor, integrated within the optimal controller of an operational industrial batch process. This prediction-based optimizer seeks to reduce total emissions tied to the process schedule's electricity consumption by implementing a rolling horizon strategy. By leveraging enhanced energy flexibility, the controller provides significant opportunities... [more]

This study presents a numerical simulation of the liquid-vapor (L-V) equilibrium stage in a sieve plate distillation column using the Smoothed Particle Hydrodynamics (SPH) method. To simulate the equilibrium stage, periodic temperature boundary conditions were applied. The column design was carried out in Aspen One, considering an equimolar benzene-toluene mixture and an operating pressure ensuring a condenser cooling water temperature of 120°F. The Chao-Seader thermodynamic model was employed for property calculations. Key outputs included liquid and vapor velocities per stage, mixture viscosity and density, operating pressure, and column diameter. The geometry of the distillation column stage and sieve plate was developed using SolidWorks, and Computational Fluid Dynamics (CFD) simulations were performed using the DualSPHysics code. The results demonstrate the influence of sieve plate design on velocity and temperature distributions within the stage, providing insights for enhancing... [more]

This study explores multiphase flow dynamics with a focus on the annular flow regime using Computational Fluid Dynamics (CFD) simulations. The methodology included defining the physical model, generating the computational mesh, and analyzing flow patterns. The Volume of Fluid (VOF) model captured fluid interactions, while the k-? SST turbulence model ensured accurate flow predictions. Simulations examined mixture density behavior and identified optimal configurations. A dataset was generated and analyzed using k-means clustering to classify flow patterns effectively. The results demonstrate the reliability of this approach for improving multiphase flow systems, with applications in oil-water processes.

Pressure-swing distillation (PSD) is a frequently applied method to separate pressure-sensitive azeotropic mixtures; however, its energy demand is very high. In continuous mode, PSD is performed in a system consisting of a high- and a low-pressure column. If the composition of the feed is between the azeotropic compositions at the two pressures, it can be introduced into any of the columns, leading to two possible column sequences. Depending on the feed composition, one of the sequences is optimal whether in terms of energy demand or total annual cost (TAC). In the present work, surrogate model-based optimization is applied to determine the optimal TAC value as a function of the feed composition between the azeotropic ones. As a first step, the column sequence with feeding into the high-pressure column is studied here. The mixture to be separated consists of water and ethylenediamine, which form a maximum-boiling azeotrope. The columns are modeled separately and a large number of simul... [more]

A Digital Twin (DT) is a purposeful digital representation of a physical entity that employs data, algorithms, and software to enhance operations, making it possible to e.g., forecast failures, or evaluate new designs through the simulation of real-world scenarios. DTs are enablers for real-time monitoring, simulation, and optimization. However, traditional simulation DTs often rely on complex, non-linear mechanistic models with high computational demands, complex structures, and a large number of specific parameters and thus pose quite a challenge to maintainability. Surrogate models, on the other hand, are simplified approximations of more complex, higher-order models. These approximations are typically built using data-driven approaches, such as Random Forest Regression, facilitating faster simulations, simpler adaptation, and quicker deployment. This study analyzes the complexity of mechanistic and surrogate modeling approaches in the context of DTs to aid model selection. A model... [more]

Hydrogen-powered heavy-duty trucks have a high potential to significantly reduce CO2 emissions in the transportation sector. Therefore, efficient hydrogen storage onboard vehicles is a key enabler for sustainable transportation, as achieving high storage densities and extended driving ranges is essential for the competitiveness of hydrogen-powered trucks. Cryo-compressed hydrogen (CcH2), stored at cryogenic temperatures and high pressures, emerges as a promising solution. This study presents a comprehensive dynamic thermodynamic model that is capable of simulating the tank system across all operating conditions and, therefore, enables thermodynamic analysis of drive cycles. The core of the model is a differential-algebraic equation system that describes the thermodynamic state of the hydrogen in the tank. Additionally, surrogate models based on artificial neural networks are applied to efficiently describe quasi-steady-state heat exchangers integrated into the tank system. Several use... [more]