Simulation plays a crucial role in the design and optimization of gasifiers by providing a detailed understanding of the involved physical processes and complex chemical reactions without the need for extensive trial-and-error experiments. It can also serve as a valuable tool for identifying potential technical issues in experimental devices that operate below expected performance. This study presents a comprehensive simulation of biomass gasification using the open-source software DWSIM. The simulated results were compared with experimental data from a pilot-scale spouted bed reactor, featuring a square-based design with a 20 kWth capacity, using pruning of apple tress as feedstock. Experimental results revealed that the reactor operated effectively at temperatures exceeding 850°C, maintaining stable conditions across a wide range of equivalence ratios. However, the distribution of products—particularly hydrogen (H2)—did not match expected results based on both literature and simulati... [more]

As initiatives to decarbonize societies increase, industry is also being considered for policies to encourage its sustainability. Ammonia (NH3) industry relies entirely on Haber-Bosch (HB) process, consuming fossil fuels for hydrogen production and energy purposes, accounting for more than 1 % of anthropogenic carbon dioxide (CO2) emissions. Emerging technologies such as the electrochemical synthesis of NH3 promise sustainable production from water, air, and renewable energies, but low TRLs are still reported. The electrification of the HB process opens a more viable path for sustainable NH3 production in the near future, where hydrogen (H2) is produced by electrolysis of water, powered from renewable energy sources. Many studies have focused on the production of 100 % green NH3 using only electric HB. In this work, a different approach is presented, which consists of studying the gradual incorporation of green H2 into a conventional NH3 plant. An Aspen Plus® V14 model of the methane-f... [more]

Liquid Organic Hydrogen Carriers (LOHCs) represent a promising solution for the efficient transport and storage of hydrogen, addressing critical challenges associated with its low volumetric density and safety concerns in gaseous and liquefied forms. LOHCs are oil-like substance, capable of reversibly binding and releasing hydrogen through catalytic hydrogenation and dehydrogenation. Hydrogenation is performed where renewable energy is extensively available: at the loading terminal, green H2 is produced and is chemically bonded to the LOHC molecule. In this way, the hydrogenated molecule is transported safely under ambient conditions using existing liquid fuels infrastructures. At the delivery site, the dehydrogenation process releases high-purity hydrogen for industrial or mobility applications, with the regenerated LOHC carrier being recycled back to the H2 production site for reuse. In view of highlighting critical issues associated to LOHCs implementation at large scale, this paper... [more]

Decarbonizing the watch industry and improving its sustainability requires enhancing the energy efficiency, intensifying the waste heat recovery and integrating renewable energy resources to significantly reduce the energy consumption and reduce the overall process emissions. The watchmaking industry operates across different periods, each one characterized by own levels of average power demand, plant activity, and duration. While working days (Work D) experience the highest energy demand; other periods, such as weekend (Wend D1, Wend D2 and Wend N), exhibit a substantial drop in energy consumption. It requires a suitable selection of utility systems and an energy planning that can meet the energy requirements regardless of the different operation regimes, reducing the consumption of fossil resources. To this end, this work leverages a systematic framework to perform the techno-economic analysis and explore the feasibility of incorporating alternative energy conversion (e.g. electrific... [more]

An essential problem in precision agriculture is to accurately model and predict root-zone (top 1 m of soil) soil moisture profile given soil properties and precipitation and evapotranspiration information. This is typically achieved by solving agro-hydrological models. Nowadays, most of these models are based on the standard Richards equation (RE), a highly nonlinear, degenerate elliptic-parabolic partial differential equation that describes irrigation, precipitation, evapotranspiration, runoff, and drainage through soils. Recently, the standard RE has been generalized to time-fractional RE with any fractional order between 0 and 2. Such generalization allows the characterization of anomalous soil exhibiting non-Boltzmann behavior due to the presence of preferential flow. In this work, we focus on inverse modeling of time-fractional RE; that is, how to accurately estimate the fractional order and soil property parameters of the fractional RE given soil moisture content measurements. S... [more]

The pulp and paper industry has significant potential to reduce its carbon footprint by optimizing energy and water usage, contributing to global efforts toward net-zero emissions. A critical unit operation in this industry is pulp washing using twin roll press washers, which enhance pulp quality. The filtrate from this unit operation can be concentrated and combusted to generate a significant amount of energy for the plant and nearby industries. However, these washers are prone to blockages that disrupt production and decrease energy efficiency. Despite their importance, models for managing blockages in twin roll press washers are lacking. This study addresses this gap by developing empirical models to predict roll speed based on key process parameters. These models performed well on a case study of un-sanitized data from a real pulp and paper plant, achieving R2 of the order of 0.7. The models can potentially be used by operators to ensure uninterrupted production, and optimize resou... [more]

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

Dynamic process modeling in process industries has been extensively studied, especially with the development of deep learning techniques. Recurrent neural networks (RNN) and stacked autoencoders (SAE) are two powerful tools for dynamic modeling and data processing. However, most existing research primarily focuses on extracting features from process input data, often neglecting the temporal autocorrelation of output variables. In this work, a hierarchical model based on time-series RNN structure is proposed. The upper layer employs a long short-term memory (LSTM) network to extract temporal features from process input data. The lower layer uses a gated recurrent unit (GRU) to model the temporal dependencies of output variables across samples. These two parts are concatenated to form the model. Additionally, SAE is utilized to perform dimensionality reduction and reconstruction of process input, seamlessly integrating the reconstruction process with the RNN into a unified framework, ter... [more]

Dual function materials (DFMs) integrate CO2 capture and conversion, offering a streamlined approach to Power-to-Gas (PtG) processes. This study develops a cyclic steady-state model for the DFM-based methanation of CO2 using the finite difference method. The model captures the adsorption, purge, and methanation stages and incorporates a semi-implicit numerical scheme for stability and accuracy. Bayesian optimisation is used to explore operational and design parameters to maximise methane productivity, CO2 conversion, and product purity. Multi-objective optimisation reveals key trade-offs among these metrics, while the impact of pressure, hydrogen concentration, DFM weight, geometry and cycle times is systematically evaluated. Results reveal that lower flow rates enhance recovery and purity, while higher flow rates improve productivity. Extended adsorption times favour purity, whereas longer methanation times significantly benefit recovery and productivity. Multi-objective optimisation,... [more]

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

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

Reject brine discharge and high CO2 emissions from desalination plants are major contributors to environmental pollution. Managing reject brine involves significant costs, mainly due to the energy-intensive processes required for brine dilution and disposal. In this context, Solvay process represents a mitigation scheme that can effectively reduce reject brine salinity and sequestering CO2 while producing sodium bicarbonates simultaneously. The Solvay process represents a combined approach that can effectively manage reject brine and CO2 in a single reaction while producing an economically feasible product. Therefore, this study reports a systematic techno-economics assessment of conventional and modified Solvay processes, while incorporating brine and carbon tax. The model evaluates the significance of implementing a brine and CO2 tax on the economics of conventional and Ca(OH)2 modified Solvay compared to industries expenditures on brine dilution and treatment before discharge to the... [more]

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

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]