This study evaluates the potential for computer-aided real-time monitoring and decision-making in pilot-scale ion-exchange chromatography operations using only historical data from the pilot-scale facility. Historical data of flow and conductivity were utilized from students running pilot-scale ion exchanges that resemble industrial ion exchange processes. A Lumped Rate Model (LRM) with a Steric Mass Action (SMA) isotherm was implemented and parameterized to characterize the fixed-bed column. The Discontinuous Galerkin Spectral Element Method (DGSEM), implemented in CADET-Julia, enabled efficient simulation and parameter estimation. Using DGSEM, the LRM with SMA was solved in less time than the sensor measurement frequency. This development allows for the prediction of batch evolution in real time for operators of the ion-exchange column. Despite challenges related to data preprocessing and manual operation inconsistencies, the results demonstrate the feasibility of integrating real-t... [more]

Dynamic modelling of wind turbines and their simulation is a very useful tool for studying their behaviour. One of the key elements concerning the physical models of wind turbines is the power coefficient Cp, which acts as an efficiency in the extraction of power from the wind. Unfortunately, this coefficient is often unknown a priori, as it does not usually appear in the information provided by manufacturers. This paper first describes a methodology for obtaining the power coefficient parameters of a commercial wind turbine model using the power curve provided by the manufacturer, which indicates the theoretical power that the wind turbine can produce at each wind speed. To achieve this, a parameter estimation problem is formulated and solved to determine the power coefficient parameters. Nevertheless, this information is often insufficient, requiring additional knowledge, such as operational data, to improve the fit. Finally, a new parameter estimation is performed using only real da... [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]

This study models a Heat Recovery System (HRS) within a petrochemical plant, assessing its economic and environmental viability. The system integrates four combustion processes and a condensing steam turbine combined heat and power (ST-CHP) generation unit, along with waste heat recovery technologies to reduce the plant’s energy use. The developed system-based approach extends a previous methodology, initially focused on reducing energy consumption in production processes, to encompass energy supply systems (in which CHP is included) as well. Simulation models were developed for two improvement scenarios regarding the integration of the ST-CHP into the HRS: preheating either the combustion air stream or the inlet water of the ST-CHP’s boiler. The latter demonstrated greater potential for reducing energy-related operational costs, thus an NLP optimisation model was developed based on that scenario. Both simulation and optimisation models were created resorting to the capabilities of the... [more]

Process monitoring is essential in industrial settings to ensure system functionality, necessitating the identification and understanding of fault causes. While a substantial body of research focuses on fault detection, fault diagnosis has received significantly less attention. Typically, faults originate either from abnormal instrument behavior, indicating the need for calibration or replacement, or from process faults, signaling a malfunction within the system. A primary objective of this study is to apply the proposed fault diagnosis methodology to a benchmark that closely mirrors real-world conditions. Specifically, we introduce a fault diagnosis framework for a wastewater treatment plant (WWTP) that effectively addresses the challenges posed by imbalanced big data commonly encountered in large-scale systems. In our study, four distinct fault scenarios were investigated: fault-free conditions, process faults only, sensor faults only, and simultaneous sensor and process faults. To e... [more]

Planning and scheduling are integral components of process supply chains. The presence of data correlation, particularly multivariate demand data dependency, can pose significant challenges to the decision-making process. This necessitates the consideration of dependency structures inherent in the underlying data to generate good-quality, feasible solutions to optimisation problems such as planning and scheduling. This work proposes a chance-constrained optimisation framework integrated with copulas, a non-parametric data estimation technique to forecast uncertain demand levels in accordance with specified risk thresholds in the context of a planning and scheduling problem. We focus on the integrated planning and scheduling problem following a bi-level optimisation formulation. The estimated demand forecasts are subsequently utilised within the Data-driven Optimisation of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework to solve the integrated optimisation problem, and deri... [more]

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]

Fluid catalytic cracking is a crucial process in the refining industry, capable of converting lower-quality feedstocks into higher-value products. Due to the variability in feedstock properties and fluctuations in product market prices, timely adjustment and optimization of the FCC unit are essential. In this context, data-driven models have garnered increasing attention for their capacity to handle the complex, nonlinear reactions involved in the FCC process. However, on account of the limited operating range of the plants and the black-box nature of data-driven models, relying solely on these models for optimization may lead to contradictory decisions in optimization processes. To address these challenges, we integrate gradient information of product yields with respect to key variables derived from the mechanistic model Petro-SIM, into the training process of data-driven models. To mitigate the high computational demands of the Petro-SIM model, we propose the use of active learning... [more]

This study presents the application of a Q-learning algorithm to optimize the selection of chemical reactions for methane reforming processes. Starting with a set of 11 candidate reactions, the algorithm identified three key reactions. These reactions effectively represent the experimental data while aligning with the underlying physics of the process and previously reported findings. The algorithm employed an epsilon-greedy policy to balance exploration and exploitation during the training process. Furthermore, simulations based on the identified reactions revealed trends consistent with experimental data. This work highlights the efficiency and adaptability of Q-learning in modeling complex catalytic systems and provides a framework for further exploration and optimization of methane reforming processes.

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.

The three systems, water, energy and food, are intertwined since the effect of any of these systems can affect others. This study proposes a mathematical model incorporating uncertain parameters in the biomass energy-water-food nexus system. The novel aspects of this work include formulating and solving the problem as a mixed-integer linear program and addressing the presence of uncertain parameters through a two-stage stochastic mathematical programming approach. Taking maximising economic benefit as an objective function, this work compares the results of the deterministic model with the results computed by incorporating uncertainty in the model parameters. The results indicate that incorporation of uncertainty gives rise to reduced profitability, but increased greenhouse gas emission (GHG) as compared to the deterministic model. On the other hand, when minimisation of GHG emission is considered as an objective function, a significantly greater reduction in the profitability is obser... [more]

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.

This study compares the probability density function (PDF) of the power generated by a wind farm obtained analytically with the PDF considering the wake effect between wind turbines, a phenomenon that reduces the power generation capacity of wind farms. Instead of considering the wake effect in the analytical method, which is complex and difficult to solve, it has been proposed to use kernel estimators to obtain the PDF. To calculate it, a wind farm power output data set has been used. This data set was generated using historical wind speed and direction data and the Katic multiple wake model. Discrepancies between the analytical PDF and PDF fitted with the kernel estimators, can lead to an overstatement of the annual available energy by 4 an 9 %, depending on the complexity of the wind farm layout. These inconsistencies can have significant implications for production planning, wind farm design, and integration of wind power into the grid. Therefore, this analysis underscores the nece... [more]

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]

In the upcoming decades, the scale-up of hydrogen production will play a crucial role for the integration of renewable energy into energy system. One scale-up strategy is the numbering-up of standardized electrolysis units in modular plant concepts. The use of modular plants can support the integration of different technologies into heterogeneous electrolyzer plants to leverage technology-specific advantages and counteract disadvantages. This work focuses on the analysis of technical operability of large-scale modular electrolyzer plants in heterogeneous plant layouts using co-simulation. Developed process models of low-temperature electrolysis components are combined in Simulink as shared environment. Strategies to control process parameters, like temperatures, pressures and flowrates in the subsystems and the overall plant, are developed and presented. An operability analysis is carried out to verify the functionality of the presented plant layout and control strategies. The dynamic... [more]

New ways of reducing environmental impact of solid waste are constantly developed. Thermochemical conversion with focus on material or energy recovery is one of the viable options. To make the feedstock properties more suitable for such a process, refuse derived fuel (RDF) is created. Although several studies have focused on thermochemical conversion in recent years, only few have comprehensively compared the main aspects of incineration, gasification, and pyrolysis processes from multiple aspects. This study focuses on mathematical modeling of these three processes in the Aspen Plus environment. Comparison from economic, safety, and environmental viewpoints was performed. As a base for the calculations, 10 t/h of RDF was selected. All three processes demonstrated the suitability to be used for energy recovery. Pyrolysis showed the greatest potential for material recovery. Payback period was used as a parameter of economic comparison with pyrolysis being the most profitable process. Ba... [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]

The synthesis of membrane networks to recover components from liquid mixture is challenging due to an extensive array of feasible network configurations and the added complexity of modeling membrane permeators caused by nonidealities in liquid mixtures. We present a mixed-integer nonlinear programming (MINLP) framework for synthesizing membrane networks to recover multiple components from liquid mixtures. First, we develop a physics-based nonlinear surrogate model to accurately describe crossflow membrane permeation. Second, we propose a richly connected superstructure to represent numerous potential network configurations. Third, the two aforementioned elements are integrated into an MINLP model to determine the optimal network configuration. Finally, the effectiveness of the proposed approach is demonstrated through a range of applications.

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]

The mathematical modeling of enzymatic reactors for esterification of fatty acids with sugars in the production of biosurfactants has been a useful tool for studying and optimizing the process. In particular, artificial neural networks and fuzzy systems emerge as promising methods for developing models for those processes. In this work, regarding artificial neural networks application, coupling of networks to reactor mass balances was considered in hybrid models to infer reactant concentrations over time. Computationally, an algorithm was constructed incorporating material balances, neural reaction rates, and step-by-step numerical integration (employing the classical Runge-Kutta method). Besides, based on an available set of experimental data, fuzzy logic was applied for modeling and optimization of the conversion of esterification as a function of operational process parameters (such as time, temperature and molar ratio of substrates). All computational development was carried out us... [more]

Contains 423 original peer-reviewed research articles presented at the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35). Subject categories include Modelling and Simulation, Sustainable Product Development and Process Design, Large Scale Design and Planning/Scheduling, Model Based Optimisation and Advanced Control, Concepts, Methods and Tools, Digitalization and AI, CAPEing with Societal Challenges, CAPE Education and Knowledge, PSE4Food and Biochemical, and PSE4BioMedical and (Bio)Pharma.