Sustainability is a thriving global topic of concern and following the advancement of technological progress and increased standards of living, the demands for energy, fuels, chemicals and other requirements have increased significantly. Methanol is one such chemical which has seen increases in demand due to its importance as a precursor in the development of widely used chemicals such as formaldehyde. In order to gain insight into the reaction mechanisms driving the process, it is beneficial to develop kinetic models that accurately describe the system for several reasons: (i) to develop process understanding; (ii) to facilitate control and optimisation; (iii) to reduce experimental burdens; and (iv) to expedite scale up and scale down of processes. Two commonly used kinetic reaction rate models are the power law and Langmuir-Hinshelwood expressions, however the strong assumptions made when developing such models may limit their predictive performance through the introduction of induc... [more]

This work presents an improved thermodynamic model, an equilibrium model, and a unified kinetic model for ammonia synthesis. The thermodynamic model accurately describes the non-ideality of the reaction system up to 1000 bar using a modified Soave-Redlich-Kwong Equation-of-State. The developed Langmuir-Hinshelwood kinetic model accurately describes ammonia synthesis on iron-based catalysts by incorporating N* and H* surface species, whereas H* species are mainly relevant below 400°C. The model fits an extensive dataset across diverse conditions (251-550°C, 1-324 bar, H2/N2 ratios 0.33-8.5, and space velocities of 1-1800 Nm3 kg-cat-1 h-1) and accounts for catalyst activity variations through a Relative Catalytic Activity factor.

Pillow-plate heat exchangers (PPHEs) represent a viable alternative to conventional shell-and-tube and plate heat exchangers. The waviness of their channels intensifies fluid mixing in the boundary layers and facilitates heat transfer. Applying secondary surface structuring can further enhance the overall thermo-hydraulic performance of PPHEs, thus increasing their competitiveness against conventional heat exchangers. In this work, streamlined secondary structures applied on the PPHE surface were studied numerically to explore their potential in enhancing near-wall fluid mixing. Computational fluid dynamics (CFD) simulations of single-phase turbulent flow in the inner PPHE channel were performed and pressure drop, heat transfer coefficients, and overall thermo-hydraulic efficiency were determined. The simulation results clearly demonstrate a favourable influence of secondary structuring on the heat transfer performance of PPHEs.

Power-to-Ammonia technology offers sustainable pathways for energy storage and chemical production, with fixed-bed reactors being critical components for efficient synthesis. Understanding reactor dynamics under varying conditions is essential for optimizing these systems, particularly when integrated with intermittent renewable energy sources. This study aims to develop and validate a 2D axisymmetric CFD model for analysing the dynamic response of a ruthenium-catalysed ammonia synthesis reactor to thermal perturbations. The model incorporates detailed reaction kinetics, multicomponent mass transport, and heat transfer mechanisms to predict system behaviour under transient conditions. Results reveal that a step increase in wall temperature from 400°C to 430°C enhances NH3 concentration by 136% (from 2.2 to 5.1 vol.%), with rapid system stabilization achieved within 0.5 seconds. The thermals response maintains consistent heat transfer patterns, exhibiting ~400K differentials between inl... [more]

One of the current challenges for hydrogen-related technologies is its storage and transportation. The low volumetric density and low boiling point require high-pressure and low-temperature conditions for effective transport and storage. A potential solution to these challenges involves storing hydrogen in chemical compounds that can be easily transported and stored, with hydrogen being released through decomposition processes. Ammonia stands out as a promising hydrogen carrier due to its high hydrogen content (17.8% by weight), relatively mild liquefaction conditions (~10 bar at 25°C), and the availability of a well-established storage and transportation infrastructure. The objective of this study was to develop a mathematical model to analyze and design a membrane fixed-bed reactor (MFBR) for large-scale ammonia decomposition. The kinetic model for the Ru-K/CaO catalyst was obtained from the literature and validated using the experimental data reported in the original study. This ca... [more]

Data-driven soft sensors help in process operations by providing real-time estimates of otherwise hard to measure process quantities, e.g., viscosities or product concentrations. Currently, soft sensors need to be developed individually per plant. Using transfer learning, machine learning based soft sensors could be re-used and fine-tuned across plants and applications. However, transferring data-driven soft sensor models is in practice often not possible, because the fixed input structure of standard soft sensor models prohibits transfer if, e.g., the sensor information is not identical in all plants. We propose a topology-aware graph neural network approach for transfer learning of soft sensor models across multiple plants. In our method, plants are modeled as graphs: Unit operations are nodes, streams are edges, and sensors are embedded as attributes. Our approach brings two advantages for transfer learning: First, we not only include sensor data but also crucial information on the... [more]

This study introduces the novel application of a Multi-Task Gaussian Process (MTGP) model to predict biogas production and critical anaerobic digestion (AD) performance indicators (soluble COD, volatile fatty acids (VFAs)), addressing feedstock variability and dynamic process behavior. We compare the MTGP against the widely used mechanistic AM2 model to evaluate its accuracy and applicability for probabilistic modeling in AD systems. The MTGP framework leverages multi-output correlations and uncertainty quantification, trained on experimental data, achieving superior predictive performance over AM2in this study, with lower RMSE (SCOD: 0.32 g/L; VFAs: 0.87 mmol/L; biogas: 0.15 L/day) and higher R² values (SCOD:0.91, VFAs:0.94, biogas :0.88) under the conditions tested. While AM2 provides biochemical insights, its reliance on unvalidated assumptions may limits robustness. The flexibility of MTGP and precision suggest its potential for real-world applications such as Bayesian Optimization... [more]

This article studies four dimple shapes: spherical, smoothed-spherical, normal distribution, and error distribution and how they enhance heat transfer on a plate within a plate heat exchanger using computational fluid dynamics. The dimple that showed the greatest efficiency of heat transfer was the normal distribution dimple, giving a temperature increase of 7.5 times of the smoothed-spherical and 15% more than the error distribution dimple shape. This was primarily due to the large increase in the turbulent kinetic energy caused by the eddies created upon the flow over the normal distribution shape. With the normal distribution shape being found to be the most effective in enhancing heat transfer, a layout of multiple normal distribution dimples based on the stage of flow development was also studied. It was found that a fully developed flow resulted in 9.5% more efficiency than half developed flow and 31% more efficient than placing dimples directly next to each other.

Optimizing twin-screw extruder (TSE) performance is critical in the plastics industry but is often resource-intensive. This study introduces a novel surrogate modeling approach using a Recurrent Deep Embedding Network (RDEN) that integrates deep autoencoders with recurrent neural networks to capture sequential dependencies and physical relationships in TSE processes. Leveraging Progressive Latin Hypercube Sampling (PLHS), the RDEN achieves robust predictions of key process variable, like mean residence time. Results demonstrate the model’s accuracy, generalization capabilities, and potential for automated screw design optimization.

Mixing is a critical operation in numerous industrial processes, traditionally performed in agitated tanks to ensure homogenization. Despite its importance, the design of tanks and impellers is often neglected during agitation system selection, resulting in excessive energy consumption and inefficient mixing. To mitigate these challenges, Computational Fluid Dynamics (CFD) serves as a powerful tool for analyzing tank hydrodynamics and quantifying mixing times. CFD employs mathematical models to simulate mass, heat, and momentum transport phenomena within fluid systems. Among the latest advancements in modeling stirred tank hydrodynamics is Smoothed Particle Hydrodynamics (SPH), a mesh-free Lagrangian approach that tracks individual particles characterized by properties such as mass, position, velocity, and pressure. SPH provides significant advantages over traditional mesh-based methods by accurately capturing fluid behavior through particle interactions. In this study, the performance... [more]

This study evaluates the introduction of Carbon Capture and Utilization (CCU) process in two Colombian refineries, focusing on their potential to reduce CO2 emissions and their associated impacts under a scenario aligned with the Net Zero Emissions by 2050 Scenario defined in the 2023 IEA report. The work uses a MILP programming tool (Linny-R) to model the operational processes of refinery sites, incorporating a net total cost calculation to optimize process performance over five-year intervals. This optimization was constrained by the maximum allowable CO2 emissions. The methodology includes the calculation of surplus refinery off-gas availability, the selection of products and CCU technologies, and the systematic collection of data from refinery operations, as well as scientific and industrial publications. The results indicate that integrating surplus refinery fuel gas (originally used for combustion processes) and HTL bio-crude off-gas (as a source of biogenic CO2) can significantl... [more]

In Quebec, the chemical sector is rapidly changing, with old facilities closing and new ones opening. Similar situation is happening in other geographies as well. Utilities need to understand the energy needs of these industries, particularly as they transition towards decarbonization. By studying existing data, they can estimate energy requirements and identify alternative technologies such as heat pumps, electric boilers, biomass boilers, and green hydrogen. Two key indicators to measure decarbonization performance: the Decarbonization Efficiency Coefficient and the GHG Performance Indicator. Decarbonizing could significantly reduce energy use, depending on the selected technologies, leading to variations of 6.1 TWh for electricity and 3.5 TWh for biomass.

This study proposes a model-based approach utilizing a hybrid population balance model (PBM) to optimize temperature profiles for minimizing agglomeration and enhancing crystal growth. The PBM incorporates key mechanisms—nucleation, growth, dissolution, agglomeration, and deagglomeration—and is ap-plied to the crystallization of an industrial active pharmaceutical ingredient (API), Compound K. Parameters were estimated through prior design of experiments (DoE) and refined via additional thermocycle experiments. In-silico DoE simulations demonstrate that the hybrid PBM outperforms traditional methods in assessing process performance under agglomeration-prone conditions. Results confirm that thermocycles effectively reduce agglomeration and promote bulk crystal formation, though their efficiency plateaus be-yond a certain cycle number. This model-based approach provides a more robust strategy for agglomeration control compared to conventional methods, offering valuable insights for indus... [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 per-formance of graph-based machine learning models, specifically Graph Convolutional Networks (GCN), Graph At-tention Networks (GAT), and a graph-transformer model, PharmHGT, for predicting CMC values. We aim to de-termine 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.

The escalating urgency to address climate change has driven carbon capture (CC) technologies into the spotlight, particularly for large-scale emitters, which benefit from economies of scale. However, small-scale emitters account for a significant share of CO2 emissions, yet such applica-tions remain largely overlooked in the literature. While CC cost is often used as a key perfor-mance indicator (KPI) for CC technologies, the lack of standardized cost estimation methods leads to inconsistencies, complicating comparisons, and hindering the deployment of CC sys-tems. This study addresses these challenges by developing flexible short-cut correlations for selected CC technologies, providing estimates of the total equipment cost (TEC) and energy consumption specific to small-scale applications across various CO2 inlet concentrations (mol%) and capture scales (10 – 100 kt/y). The flexibility of the correlations enables the integration of various cost estimation methods available in the liter... [more]

The growing size and complexity of energy system optimization models, driven by high-resolution spatial data, pose significant computational challenges. This study introduces methods to reduce model’s size and improve computational efficiency while preserving solution accuracy. First, a composite-curve-based approach is proposed to aggregate granular data into larger resolutions without averaging out specific properties. Second, a general clustering method groups geographically proximate fields, replacing multiple transportation arcs with a single arc to reduce transportation-related variables. Lastly, a two-step algorithm that decomposes the sup-ply chain design problems into two smaller, more manageable subproblems is introduced. These methods are applied to a case study of switchgrass-to-biofuels network design in eight U.S. Midwest states, demonstrating their effectiveness with realistic and detailed spatial data.

This is an Aspen Plus V14 model of a furnace process. Natural gas is combusted in excess air to produce heat. A heat exchanger model simulates the boiling of boiler feed water near 29 bar pressure to produce steam at about 235 deg C. Design specs are used to ensure certain process conditions and scales are met. This is supplementary material for the paper Adams TA. Exergy in Chemical Engineering Education, submitted to the ESCAPE 35 conference.

The transition to renewable energy sources, such as biogas, requires purification processes to separate methane from carbon dioxide, with adsorption-based methods being widely employed. Accurate simulations of these systems, governed by coupled PDEs, ODEs, and algebraic equations, critically depend on precise parameter determination. While traditional approaches often result in significant errors or complex procedures, optimization algorithms provide a more efficient and reliable means of parameter estimation, simplifying the process, improving simulation accuracy, and enhancing the understanding of these systems.
This work introduces an Artificial Intelligence-based methodology for estimating the isotherm parameters of a mathematical phenomenological model for fixed-bed experiments. The separation of CO₂ and CH₄ is used as case study. This work develops an algorithm for parameter estimation for the system's mathematical model. The results show that the validated model has a close fi... [more]

The document is the digital supplementary material for the article titled "Application of Artificial Intelligence in process simulation tool", submitted to the ESCAPE 35 conference. It contains additional information and figures.

Contains optimized design data, aspen simulation files for the three chemical heat pumps namely:
Isopropanol–acetone–hydrogen
2-Butanol–methyl ethyl ketone–hydrogen
2-Pentanol–methyl propyl ketone–hydrogen.
Optimization code (written in python) is also provided.

In this file can be found:
- Energy demands and scope 1 CO2 emissions for different lime production routes
- Assumptions underlying the price scenarios
- Commodities prices used for cost estimation

The classifier is an essential tool for the development of contemporary engineering technology. The application of classifiers is to categorize mixed-sized particles into multi-stage uniform particle sizes. In current studies, the particles in the classifier obtain their initial velocity when feeding. The classification effect is impacted by the inability to precisely control the initial state of the particles. To solve this problem, a pusher feed classifier was designed in this study, and a numerical simulation was performed to investigate its flow field characteristics and classification performance using the RNG-DPM method. A pusher is utilized to achieve particle feeding without initial velocity and to precisely control the initial state of the particles in the classification flow field. A newly developed two-way air inlet structure is designed to provide a superimposed flow field and enable the five-stage classification. Our results show that this pusher feed classifier has the be... [more]

While technical optimization focuses on maximizing the annual energy yield of utility-scale PV parks, the ultimate goal for power plant owners is to maximize investment profit. This paper aims to bridge the gap between technical and economic approaches by using simulation data from a real-case utility-scale PV park. It analyzes how changes in configuration parameters such as the DC−AC ratio and string length and PV technologies like solar tracking systems and bifacial modules impact the economic metrics of the project, i.e., net present value (NPV) and internal rate of return (IRR). PVSyst software was utilized as a simulation tool, while in-house developed software implementing appropriate technical and economic models served as a comparison platform and was used to validate the outputs generated through PVSyst. Results indicate that the commonly used horizontal single-axis tracking configuration may economically underperform compared with fixed-tilt setups. The optimal DC−AC ratio fe... [more]

Wobble-plate axial piston pumps, characterized by the lack of a slipper mechanism, experience reduced leakage in comparison to their swash-plate counterparts, which contributes to their higher volumetric efficiency. Presently, the primary focus of the research conducted by scholars both domestically and internationally is concentrated on wobble-plate axial piston pumps. The performance studies within this field are predominantly focused on investigating flow pulsation. They also investigate pressure pulsation. Additionally, they investigate cavitation phenomena. Research on inclined-axis axial piston pumps has been limited. This study focused on analyzing the operational form of the piston within an inclined-axis axial piston pump. A correction factor k was introduced based on the motion characteristics of the piston. The application of this factor significantly improved the accuracy of the simulations when compared to the experimental results. Specifically, at a load pressure of 10 MP... [more]

CO2-enhanced coalbed methane recovery (CO2-ECBM) has been demonstrated as an effective enhanced oil recovery (EOR) technique that enhances the production of coalbed methane (CBM) while achieving the goal of CO2 sequestration. In this paper, the grand canonical Monte Carlo simulation is used to investigate the dynamic mechanism of CO2-ECBM in anthracite pores. First, an anthracite pore containing both organic and inorganic matter was constructed, and the adsorption and diffusion characteristics of CO2 and CH4 in the coal pores under different temperature and pressure conditions were studied by molecular dynamics (MD) simulations. The results indicate that the interaction energy of coal molecules with CO2 and CH4 is positively associated with pressure but negatively associated with temperature. At 307.15 K and 101.35 kPa, the interaction energies of coal adsorption of single-component CO2 and CH4 are −1273.92 kJ·mol−1 and −761.53 kJ·mol−1, respectively. The interaction energy between ant... [more]