Oxidation of arabinose to arabinoic acid is an innovative way to valorize local biomass to a high add value product. Previously done experiments on oxidation of arabinose to arabinoic acid with molecular oxygen were used to determine the optimum reaction conditions, scale-up the process and analyse the techno-economic aspects. These results were utilized to analyse the environmental impact of the scaled-up process during its lifetime using the life cycle assessment (LCA) methodology. SimaPro software combined with the impact assessment method IMPACT 2002+ were applied. The results revealed that heating seems to be the largest contributor to the environmental impact even if the reaction is performed under rather mild conditions (70oC). This highlights the importance of reducing the energy consumption via efficient heat integration.

An intensified approach to ?-valerolactone (GVL) production is achieved using a reactive distillation column. Conventional methods require multiple units, leading to high energy consumption, costs, and limited scalability. The proposed technology integrates reaction and separation into a single unit, enhancing process efficiency for biomass-derived chemicals. A multiobjective optimization framework balances economic, environmental, and operational goals, reducing total annual cost (TAC) by 43% and environmental impact (EI99) by 45% compared to conventional processes. Additionally, energy consumption drops by 63%, while GVL production increases by 25%, highlighting the potential of reactive distillation for improved efficiency and sustainability.

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]

In this research, a novel process is developed that utilizes low density polyethylene from plastic waste to produce ethylene. In this process, waste polyethylene is reacted in a microwave reactor to produce ethylene. A conceptual flowsheet based on this reactor is developed in the ASPEN Plus environment. Heat integration tools are utilized to reduce the hot and cold utilities used in this process. This novel design is compared with the conventional process of making ethylene from ethane via cracking. A technoeconomic analysis is conducted to demonstrate the economic feasibility of this process.

In this research, a chemical looping scheme is combined with dry reforming of natural gas in a novel microwave reactor to produce industrial quantity of methanol. Simulation results show that the chemical looping scheme can produce all the hydrogen required by the methanol reactor as well as a significant portion of the carbon dioxide required for the syngas reactor. A heat exchanger network is developed to substantially reduce the hot and cold utility usage. A technoeconomic analysis indicates a significant positive net present value along with a substantial reduction in carbon dioxide emissions as well as a reduction in energy consumption.

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.

Process Systems Engineering (PSE) provides the advanced conceptual framework and software tools to formulate and optimise well-considered integrated solutions that could accelerate the sustainability transition within the industrial sector. The landscape of advanced PSE is poised to undertake a considerable transformation with the rise in popularity of open-source and script-based software platforms with predictive modelling capabilities based on modern mathematical optimization techniques. This paper highlights three leading equation-based platforms—IDAES, Modelica, and GEKKO-that are increasingly utilised for the modelling, simulation, and optimisation of complex systems within the advanced PSE domain, alongside the strengths and limitations of each approach. Following this, we present a framework through which emerging techniques within the domain of Software Engineering could be leveraged to address these limitations, with a vision of improving the accessibility and flexibility of... [more]

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]

Predicting the critical micelle concentration (CMC) of surfactants is essential for optimizing their applications in various industries, including pharmaceuticals, detergents, and emulsions. In this study, we investigate the performance of graph-based machine learning models, specifically Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and a graph-transformer model, PharmHGT, for predicting CMC values. We aim to determine the most effective model for capturing the structural and physicochemical properties of surfactants. Our results provide insights into the relative strengths of each approach, highlighting the potential advantages of transformer-based architectures like PharmHGT in handling molecular graph representations compared to traditional graph neural networks. This comparative study serves as a step towards enhancing the accuracy of CMC predictions, contributing to the efficient design of surfactants for targeted applications.

In the present paper, we come up with application of machine learning using data for flow visualization as a method for predicting unsteady flow patterns in oscillatory baffled reactors (OBRs). Application of the proper orthogonal decomposition (POD) is investigated for dynamic analysis of spatio-temporal data acquired by particle image velocimetry (PIV) to determine inputs and outputs for neural network model. It has demonstrated that three sets of modes and time-varying mode coefficients extracted by the POD could be useful for dynamic analysis and prediction of time-variant flow patterns in OBR. Also it is shown that decomposition of the time-series data for the mode coefficients by Fourier series expansion was effective for deriving reduced order model.

Accurately predicting biogas production for real-time applications remains a challenge in anaerobic digestion (AD) due to the process's complexity and dynamic nature. While mechanistic models are essential for understanding and modelling AD processes, however they are highly non-linear and depend on detailed feedstock characterisation and parameter calibration. In contrast, data-driven models do not rely on predefined equations and rather use process data to capture the system's underlying dynamics. This study compares mechanistic and data-driven models for biogas prediction using lab-scale data. A state estimation framework with a rolling window was used for the mechanistic model, based on biomass and substrate concentrations with Haldane kinetics, achieved an accuracy of (R² = 0.91). A Long Short-Term Memory (LSTM) model with Bayesian Optimisation for hyperparameter optimisation, trained on the same data showed superior performance (R² = 0.93–0.98) and captured temporal dependencies... [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]

This work presents a comprehensive multiscale model for Solid Oxide Fuel Cells (SOFCs), integrating microscale and macroscale simulations to analyze internal reforming and its impact on overall cell performance. The microscale model [1], [2] captures the intricate mass and charge transport phenomena at the pore scale of porous electrodes, resolving electrochemical reactions at the triple-phase boundaries and modeling chemical reactions at pore spaces. Simultaneously, the macroscale model provides a broader view of the entire cell's behavior by solving the same transport equations on a coarser computational mesh. The multiscale approach is particularly useful for addressing the challenges posed by simultaneous chemical and electrochemical reactions at the anode, which complicate the modeling of internal reforming. To overcome these challenges, a novel approach is introduced [3], spatially separating the regions of chemical and electrochemical activity in the pore scale domain by taking... [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.

The increasing need for Direct Air Capture (DAC) technologies is driven by the urgent global need to mitigate rising CO2 levels due to climate change. In humid climates, DAC systems face challenges as high humidity increases the energy required for regeneration. This study introduces a novel DAC system integrated within an Air Handling Unit (AHU) that includes a silica gel wheel for air dehumidification before CO2 capture, significantly enhancing physisorbent performance by optimizing conditions for CO2 adsorption. This system, tailored for the AHU of Doha Tower, involves dehumidifying return air, subsequently cooling it for effective CO2 capture. The introduction of the silica gel wheel notably reduced the energy requirements by 81.5% for NbOFFIVE compared to configurations without dehumidification, and the thermal energy cost for NbOFFIVE when integrated with HVAC and silica gel is 70 USD/ tonCO2, compared to 160 USD/ tonCO2 for SBA-15 + TEPA used alone. Additionally, the thermal ene... [more]

This work presents a macroscopic, high-level representation of the interconnected nexus system, utilizing a resource allocation model to capture the interactions between the power and water subsystems. The model is employed to assess the system's performance under various external stressor impact scenarios, determining the thresholds at which the system can no longer maintain a continuous supply of functional services (i.e. power and water), which reveal the system's vulnerabilities. Resilience metrics are incorporated to interpret these results and characterize the nexus performance. The proposed methodology is generalizable, and its capabilities will be demonstrated through a case study on the energy-water nexus in the Gulf Cooperation Council region.

This study presents the design and evaluation of a plantwide control (PWC) system for Formic acid (FA) production under unsteady green Hydrogen supply. Starting from a steady-state foundation in Aspen Plus V12, the system was prepared to handle variable inputs and was subsequently transitioned into Aspen Dynamics for real-time responsiveness. The two-level design methodology to build a PWC scheme, which is comprised of equipment-specific and plantwide controllers, effectively managed fluctuations in feed rates ranging up to ±20%, maintaining FA purity and production rate targets. Gradual SRAMP (sinusoidal ramp) adjustments of 1% per hour provided optimal stability. These results confirm the PWC system's effectiveness in maintaining production goals under the variability of throughput.

Mathematical modelling and simulation are pivotal components in process systems engineering. Focusing on polymerization process systems, identifying microscopic properties of polymers is highly sought after for advancing kinetic comprehension and facilitating industrial applications. Among various computational methods predicting polymeric properties microscopically, kinetic Monte Carlo (kMC) offers a stochastic framework to characterize individual polymer chains and track dynamic system evolution, providing mechanistic insights into complex polymerization kinetics. In this study, an accurately accelerated Superbasin-aided kMC model is developed for enhancing the kinetic understanding of the advanced photo-iniferter RAFT (PI-RAFT) polymerization. The contribution is twofold, presenting advancements in both the mathematical modelling techniques for complex dynamic process systems and the mechanistic understanding of photo-induced polymerizations. Leveraging the increased computational p... [more]