Proton Exchange Membrane (PEM) fuel cells are gaining traction in automotive applications due to their efficiency and environmental benefits, but they face challenges such as high costs, degradation rates, and limited hydrogen availability. To address these issues, novel operational methods have been developed, focusing on customized designs rather than traditional uniform configurations. These advancements include the variable temperature flow field, which maintains high relative humidity without external humidification by leveraging internally generated water and heat, and graded catalyst loading, which enhances current density distribution. Additionally, complex flow fields have been designed using 3D metal printing to mitigate liquid water accumulation. These innovations have shown significant performance improvements, particularly when combined, demonstrating a 260% increase in current density at 0.6 V. These advancements hold promise for overcoming the limitations of conventional... [more]

The urgent need for sustainable energy drives the exploration of biomass and plastic waste co-gasification, a promising route for producing clean fuels and chemicals, reducing greenhouse gas emissions, and minimizing fossil fuel dependence. Modeling and simulation are vital for optimizing this process, particularly syngas yield, yet comparative studies on Aspen Plus modeling techniques for steam co-gasification are limited. This research addresses this gap by comparing three Aspen Plus strategies: thermodynamic equilibrium modeling (TEM), restricted thermodynamic modeling (RTM), and kinetic modeling (KM), for simulating the co-gasification of pine sawdust and polyethene (PE) with steam in bubbling fluidized bed gasifier (BFBG). The primary objective is to evaluate the effectiveness of each strategy in predicting the syngas composition under varying conditions. Three models were developed in Aspen Plus on the basis of each strategy, and their predicted syngas compositions were compared... [more]

Demographic shifts and increased automation in chemical plants are reducing the experience and skill levels of plant operators. Therefore, BASF has implemented Operator Training Simulators (OTS) to allow operators to practice and improve their skills in this safe and controlled environment. The OTS consists of a dynamic model of the process, a control system and safety logics. This paper describes the learnings from using OTS at BASF, where they are used to train operators in process understanding, optimization, procedural training, and disturbance handling. Benefits include reduced training costs, minimized risks and improved efficiency. Also organizational guidelines are provided to ensure that the mentioned benefits are realized in industrial practice. Additionally, high-accuracy OTS models support HAZOP, debottlenecking, and optimization studies.

The increasing need for Direct Air Capture (DAC) technologies is driven by the urgent global need to mitigate rising CO2 levels due to climate change. In humid climates, DAC systems face challenges as high humidity increases the energy required for regeneration. This study introduces a novel DAC system integrated within an Air Handling Unit (AHU) that includes a silica gel wheel for air dehumidification before CO2 capture, significantly enhancing physisorbent performance by optimizing conditions for CO2 adsorption. This system, tailored for the AHU of Doha Tower, involves dehumidifying return air, subsequently cooling it for effective CO2 capture. The introduction of the silica gel wheel notably reduced the energy requirements by 81.5% for NbOFFIVE compared to configurations without dehumidification, and the thermal energy cost for NbOFFIVE when integrated with HVAC and silica gel is 70 USD/ tonCO2, compared to 160 USD/ tonCO2 for SBA-15 + TEPA used alone. Additionally, the thermal ene... [more]

This work presents a macroscopic, high-level representation of the interconnected nexus system, utilizing a resource allocation model to capture the interactions between the power and water subsystems. The model is employed to assess the system's performance under various external stressor impact scenarios, determining the thresholds at which the system can no longer maintain a continuous supply of functional services (i.e. power and water), which reveal the system's vulnerabilities. Resilience metrics are incorporated to interpret these results and characterize the nexus performance. The proposed methodology is generalizable, and its capabilities will be demonstrated through a case study on the energy-water nexus in the Gulf Cooperation Council region.

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]