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

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

Temperature regulating in liquid tanks is critical in the chemical industry and conventionally relies on sensor feedback. However, due to the complex thermo-hydrodynamics, unsensed local temperatures can deviate from desired thresholds, underscoring the need for improved tank temperature modeling. The absence of internal thermal or flow data, however, poses significant challenges for the development and validation of effective control strategies. In this study, a virtual model for regulating liquid tank temperature was developed using computational fluid dynamics (CFD). Adaptions were made mainly by involving (1) a simple on-off mechanism of feeding based on a virtual sensor to achieve temperature within the acceptable range and (2) the imposition of unfavorable temperatures on the walls representing ambient influences. Leveraging this virtual system, several new cases were simulated. The simulation results highlighted pronounced temperature non-uniformity, with discrepancies exceeding... [more]

Natural gas production is expected to increase, leading to the exploitation of low-quality reserves that contain high levels of acid gases, such as carbon dioxide. The aim of this work is to compare various innovative and conventional technologies for the removal of CO2 from natural gas, considered as a binary mixture of methane and carbon dioxide, with CO2 contents ranging from 10 to 70 mol%. The processes are simulated using Aspen Plus® V9.0 and compared in terms of energy consumption, which is evaluated through the net equivalent methane method. The results show that novel low-temperature and hybrid technologies, which combine distillation and physical absorption, are the most energy-efficient for CO2 removal from natural gas with high acid gas contents, while conventional physical absorption processes are optimal for natural gas with low to moderate acid gas contents.

Dynamic or Programmable Catalysis is an innovative strategy to improve heterogeneous catalysis processes by modulating the binding energies (BE) of adsorbates on a catalytic surface. The technique enables the periodic favoring of different reaction steps, overcoming limitations imposed by the Sabatier Principle and allowing for higher overall reaction rates, otherwise unattainable. Previously, we implemented a simultaneous simulation approach using the algebraic modeling language Pyomo and the solver IPOPT to obtain cyclic steady state results for a unimolecular reactive system with up to four-order of magnitude increases in computational performance compared to the previously reported sequential approach. The flexibility of the method allowed for the investigation of the influence of forcing signal parameters on system behavior and provided a framework for waveform design. In this study, we use a hybrid framework that combines the sequential and the simultaneous simulation approaches... [more]

Sustainable aviation fuel (SAF) is a proven alternative to reduce CO2 emissions in the aviation sector, supporting sustainable growth. However, SAF processes remain economically uncompetitive with fossil-derived jet fuel, prompting interest in strategies to address these challenges. In 2022, Carrasco-Suárez et al. explored process intensification in the SAF separation zone of a biorefinery using waste cooking oil (WCO), achieving a 3.07% reduction in CO2 emissions and lower operational costs for steam and cooling water. Despite these gains, the WCO biorefinery remains economically unviable with high energy demands. This work presents the energy integration of the entire WCO biorefinery addressed from the pinch point methodology, combined with separation zones intensification (EI-PI-S), using the principles of sections movement for distillation columns; these energy efficiency strategies were applied on the biorefinery in Aspen Plus V.10.0 in order to improve the scheme. Key indicators—... [more]

The increasing global population has resulted in the scramble for more energy. Hydrogen offers a new revolution to energy systems worldwide. Considering its numerous uses, research interest has grown to seek sustainable production methods. However, hydrogen production must satisfy three factors, i.e., energy security, energy equity, and environmental sustainability, referred to as the energy trilemma. Therefore, this study seeks to investigate the sustainability of hydrogen production pathways through the use of a Multi-Criteria Decision- Making model. In particular, a modified Simultaneous Evaluation of Criteria and Alternatives (SECA) model was employed for the prioritization of 19 options for hydrogen production. This model simultaneously determines the overall performance scores of the 19 options and the objective weights for the energy trilemma in a South African context. The results obtained from this study showed that environmental sustainability has a higher objective weight v... [more]

In offshore environments, where space, weight, and energy efficiency are critical constraints, the effective removal of water from turbine exhaust gases is essential to enhance gas treatment processes. In this context, replacing conventional methods, such as molecular sieves, with supersonic separators (SSRs) emerges as a promising alternative. This study aims to determine the most suitable operating conditions and design parameters for water removal via supersonic separation (SS) in turbine exhaust gases (TxGs) on offshore platforms. Simulations were performed in Aspen HYSYS using a unit operation extension, based on typical TxGs compositions from offshore platforms. Key parameters, including operating conditions, separator dimensions, and shock Mach number, were evaluated to maximize efficiency while minimizing equipment footprint. The results indicated a water capture efficiency of 99.45%, demonstrating that SS technology is not only compact but also a viable and efficient alternati... [more]

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]