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]

Nucleophilic aromatic substitutions (SNAr) are key chemical transformations in pharmaceutical and agrochemical synthesis, yet their complex mechanisms (concerted or two-step) complicate kinetic model identification. Accurate kinetic models for SNAr are essential for scale-up, optimization, and control of the reaction process, but conventional methods struggle with mechanism uncertainty driven by substrates, nucleophiles, and reaction conditions, with data collection being difficult due to its source-intensive nature. We address this using DoE-SINDy, a data-driven framework for generative modelling without complete theoretical understanding. A benchmark study on the SNAr reaction of 2,4-difluoronitrobenzene with morpholine in ethanol was conducted, incorporating parallel and consecutive side-product formation. Ground-truth kinetic models validated in prior studies were used to generate in-silico data under varying noise levels and sampling intervals. DoE-SINDy successfully identified th... [more]

This study evaluated the air/steam co-gasification of crude glycerol (CG) and linear low density polyethylene (LLDPE). It was demonstrated that operating the process using air or a mixture of air and steam has significant implications for carbon conversion efficiency (CCE), cold gas efficiency (CGE), lower heating value (LHV) gasifier output temperature and syngas concentration. The CCE reached a maximum value of 100% at equivalence ratio (ER) of 0.3 for 25% LLDPE and an ER of 0.35 for 75% LLDPE when air was used. When steam was introduced in the gasifier at a fixed rate (SFR =0.5), the CCE of 100% was maximised at ER of 0.25 for 25% LLDPE and 0.3 for 75% LLDPE content. An increase in the steam to feedstock ratio (SFR) did not alter the CCE for 25% LLDPE at a constant ER, but for that of 75% LLDPE, a CCE was maximized at an SFR of 0.25. In the case of CGE, a maximum value of 79.24% and 78.12% was reached at ER of 0.3 and 0.35 for 25% LLDPE and 75% LLDPE respectively when pure air was u... [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]

This study presents an economic analysis of a blue H2-N2 production system, using a novel intensified reformer system with a hydrogen production efficiency of 80%. The system’s ability to produce both high-purity H2 and N2 creates opportunities for small-scale blue H2 and distributed ammonia production. The system consists of three identical, optimized fixed-bed reforming reactors, a heat recovery system, and shift reactors. A dynamic model was developed to simulate three small-scale H2 production systems: 2.8 tpd, 7.1 tpd, and 17.1 tpd, enabling an evaluation of their economic viability. The results indicate that the cost of H2 production ranges from 2.7 to 3.1 USD/kgH2. Sensitivity analysis reveals that natural gas and CO2 transportation costs have a significant impact on the variability of H2 price. This research provides valuable insights into the economic feasibility of small-scale blue hydrogen production, offering a pathway to support the broader adoption of hydrogen technologie... [more]

Clinker is the main constituent of cement, produced in the pyroprocessing section of the cement plant. This comprises some high temperature and carbon intensive processes, which are responsible for the vast majority of the CO2 emissions associated with cement production. This paper presents first-principles mathematical models for the simulation of the pyroprocess section; more specifically the preheating cyclones, the calciner and the rotary kiln. The models incorporate material and energy balances, the major heat and mass transport phenomena, reaction kinetics and thermodynamic property estimation models. These mathematical formulations are implemented in the gPROMS® Advanced Process Modelling Environment and the resulting index-1 DAE (Differential Algebraic Equation) system can be numerically solved for various reactor geometries and operating conditions. The process models developed for each unit are then used to build a cement pyroprocess flowsheet model. The flowsheet model is va... [more]

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.