OM
O. Moultos
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23 records found
1
Master thesis
(2026)
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K. Ahmed, T.J.H. Vlugt, H. Hajibeygi, T. Hulikal Chakrapani, O. Moultos, Evgeny A. Pidko
Understanding wettability in subsurface gas–water–rock systems is essential for applications such as geological hydrogen storage, carbon sequestration, and reactive transport in porous media. In this study, molecular dynamics simulations were performed to investigate the wettability behavior of mixed H2-CO2 gas bubbles on a mineral surface under aqueous conditions. The focus was placed on disentangling the roles of gas composition and gas–rock interaction strength in controlling contact angle behavior. Systematic scaling of gas–solid interaction parameters revealed that wettability can be governed by the adsorption affinity, rather than gas fraction alone. Therefore, by increasing the CO2–rock interaction, a significant rise in contact angles can be observed, whereas scaling H2–rock interactions produced weaker effects. These findings indicate that CO2 acts as the dominant wettability controlling species due to its stronger dispersion interactions and quadrupolar character, which promote preferential adsorption at the mineral interface. Additionally, simulations varying the CO2 fraction demonstrated two distinct regimes depending on which gas dominated the interfacial adsorption layer. When CO2 formed the primary adsorbed layer, increasing its fraction enhanced surface hydrophobicity. In contrast, when H2 dominated the interface, changes in composition produced a different wettability response. This highlights the importance of interfacial structuring over bulk composition. The results provide a mechanistic framework for understanding competitive gas adsorption and its influence on wettability in mixed-gas systems. These insights are relevant for predicting multiphase behavior in subsurface energy storage and carbon management applications, where interfacial phenomena critically impact gas trapping, mobility, and long-term stability.
...
Understanding wettability in subsurface gas–water–rock systems is essential for applications such as geological hydrogen storage, carbon sequestration, and reactive transport in porous media. In this study, molecular dynamics simulations were performed to investigate the wettability behavior of mixed H2-CO2 gas bubbles on a mineral surface under aqueous conditions. The focus was placed on disentangling the roles of gas composition and gas–rock interaction strength in controlling contact angle behavior. Systematic scaling of gas–solid interaction parameters revealed that wettability can be governed by the adsorption affinity, rather than gas fraction alone. Therefore, by increasing the CO2–rock interaction, a significant rise in contact angles can be observed, whereas scaling H2–rock interactions produced weaker effects. These findings indicate that CO2 acts as the dominant wettability controlling species due to its stronger dispersion interactions and quadrupolar character, which promote preferential adsorption at the mineral interface. Additionally, simulations varying the CO2 fraction demonstrated two distinct regimes depending on which gas dominated the interfacial adsorption layer. When CO2 formed the primary adsorbed layer, increasing its fraction enhanced surface hydrophobicity. In contrast, when H2 dominated the interface, changes in composition produced a different wettability response. This highlights the importance of interfacial structuring over bulk composition. The results provide a mechanistic framework for understanding competitive gas adsorption and its influence on wettability in mixed-gas systems. These insights are relevant for predicting multiphase behavior in subsurface energy storage and carbon management applications, where interfacial phenomena critically impact gas trapping, mobility, and long-term stability.
A Quantum Mechanical Study of Lithium Titanium Oxide for Lithium Recovery
Sustainable and High-Selectivity Extraction in Complex Brines
The accelerating demand for lithium in energy storage technologies is straining conventional mining and evaporation methods, driving interest in selective recovery from low-grade and chemically complex brines. This thesis evaluates spinel lithium titanium oxide (Li4Ti5O12, LTO) as a lithium-ion sieve using density functional theory (DFT) to provide atomistic insight into its thermodynamic stability, structural evolution, and ion-exchange kinetics. Two objectives guided this work: (i) establishing the phase stability and electrochemical response of LTO during lithium extraction with proton incorporation, and (ii) identifying and quantifying lithium and proton migration pathways that govern ion-exchange rates.
DFT thermodynamics show that proton substitution stabilizes delithiated frameworks relative to vacancy states, shifting the voltage profile to lower potentials and yielding solid-solution behavior with near-zero-strain structural evolution. Nudged elastic band (NEB) analysis revealed a kinetic hierarchy: lithium diffuses efficiently via the 8a-16c-8a pathway, where the 16c site acts as a kinetic bridge, while octahedral 16d lithium remains kinetically trapped. In contrast, proton migration exhibits substantially higher barriers (at least 1.9 eV) that scale with geometric path length, indicating that H+ mobility is the likely rate-limiting step in the exchange cycle.
These findings reconcile experimental observations of structural robustness yet sluggish adsorption-desorption kinetics in LTO sieves. They highlight three design levers for improving performance: enhancing 16c accessibility to accelerate Li+ diffusion, engineering shorter or lower-barrier proton pathways, and maintaining homogeneous lithiation for optimal sieving rates. Together, this study establishes a basis for rational improvements to spinel LTO sieves and sets a computational-experimental agenda for advancing sustainable and economic lithium recovery from challenging brines.
The accelerating demand for lithium in energy storage technologies is straining conventional mining and evaporation methods, driving interest in selective recovery from low-grade and chemically complex brines. This thesis evaluates spinel lithium titanium oxide (Li4Ti5O12, LTO) as a lithium-ion sieve using density functional theory (DFT) to provide atomistic insight into its thermodynamic stability, structural evolution, and ion-exchange kinetics. Two objectives guided this work: (i) establishing the phase stability and electrochemical response of LTO during lithium extraction with proton incorporation, and (ii) identifying and quantifying lithium and proton migration pathways that govern ion-exchange rates.
DFT thermodynamics show that proton substitution stabilizes delithiated frameworks relative to vacancy states, shifting the voltage profile to lower potentials and yielding solid-solution behavior with near-zero-strain structural evolution. Nudged elastic band (NEB) analysis revealed a kinetic hierarchy: lithium diffuses efficiently via the 8a-16c-8a pathway, where the 16c site acts as a kinetic bridge, while octahedral 16d lithium remains kinetically trapped. In contrast, proton migration exhibits substantially higher barriers (at least 1.9 eV) that scale with geometric path length, indicating that H+ mobility is the likely rate-limiting step in the exchange cycle.
These findings reconcile experimental observations of structural robustness yet sluggish adsorption-desorption kinetics in LTO sieves. They highlight three design levers for improving performance: enhancing 16c accessibility to accelerate Li+ diffusion, engineering shorter or lower-barrier proton pathways, and maintaining homogeneous lithiation for optimal sieving rates. Together, this study establishes a basis for rational improvements to spinel LTO sieves and sets a computational-experimental agenda for advancing sustainable and economic lithium recovery from challenging brines.
This report benchmarks the performance of several machine learning interatomic potentials (MLIPs) in simulating thermodynamic and transport properties of water. Classical force fields are efficient but often fail to capture complex interactions such as many-body effects and nuclear quantum effects. Quantum-scale methods, such as ab initio molecular dynamics, can account for these phenomena with high accuracy, but their computational cost limits their use to small systems and short timescales. MLIPs offer a practical compromise by learning interatomic potentials from quantum mechanical data, enabling simulations that are both accurate and scalable.
We evaluate two MLIPs, DeePMD and Allegro, trained on SCAN-metaGGA multiphase data, and assess their ability to reproduce key properties such as self-diffusivity and isothermal compressibility. The models yielded self-diffusivity values of 0.82 × 10−9 m2 /s and 0.62 × 10−9 m2 /s, both of which are significantly lower than the experimental value of 2.30 × 10−9 m2 /s. This discrepancy is attributed to overly strong hydrogen bonding from the SCAN meta-GGA functional, consistent with prior studies. We also observed that training on multi-phase datasets caused the models to produce structural features characteristic of a mixture of phases, as revealed by the radial distribution functions. While DeePMD produced values closer to experiment, it exhibited unphysical trends, including decreasing diffusivity with increasing system size and decreasing isothermal compressibility with increasing temperature.
These results illustrate both the promise and limitations of MLIPs in capturing the complex behavior of water and suggest that care must be taken in dataset construction and model validation. ...
We evaluate two MLIPs, DeePMD and Allegro, trained on SCAN-metaGGA multiphase data, and assess their ability to reproduce key properties such as self-diffusivity and isothermal compressibility. The models yielded self-diffusivity values of 0.82 × 10−9 m2 /s and 0.62 × 10−9 m2 /s, both of which are significantly lower than the experimental value of 2.30 × 10−9 m2 /s. This discrepancy is attributed to overly strong hydrogen bonding from the SCAN meta-GGA functional, consistent with prior studies. We also observed that training on multi-phase datasets caused the models to produce structural features characteristic of a mixture of phases, as revealed by the radial distribution functions. While DeePMD produced values closer to experiment, it exhibited unphysical trends, including decreasing diffusivity with increasing system size and decreasing isothermal compressibility with increasing temperature.
These results illustrate both the promise and limitations of MLIPs in capturing the complex behavior of water and suggest that care must be taken in dataset construction and model validation. ...
This report benchmarks the performance of several machine learning interatomic potentials (MLIPs) in simulating thermodynamic and transport properties of water. Classical force fields are efficient but often fail to capture complex interactions such as many-body effects and nuclear quantum effects. Quantum-scale methods, such as ab initio molecular dynamics, can account for these phenomena with high accuracy, but their computational cost limits their use to small systems and short timescales. MLIPs offer a practical compromise by learning interatomic potentials from quantum mechanical data, enabling simulations that are both accurate and scalable.
We evaluate two MLIPs, DeePMD and Allegro, trained on SCAN-metaGGA multiphase data, and assess their ability to reproduce key properties such as self-diffusivity and isothermal compressibility. The models yielded self-diffusivity values of 0.82 × 10−9 m2 /s and 0.62 × 10−9 m2 /s, both of which are significantly lower than the experimental value of 2.30 × 10−9 m2 /s. This discrepancy is attributed to overly strong hydrogen bonding from the SCAN meta-GGA functional, consistent with prior studies. We also observed that training on multi-phase datasets caused the models to produce structural features characteristic of a mixture of phases, as revealed by the radial distribution functions. While DeePMD produced values closer to experiment, it exhibited unphysical trends, including decreasing diffusivity with increasing system size and decreasing isothermal compressibility with increasing temperature.
These results illustrate both the promise and limitations of MLIPs in capturing the complex behavior of water and suggest that care must be taken in dataset construction and model validation.
We evaluate two MLIPs, DeePMD and Allegro, trained on SCAN-metaGGA multiphase data, and assess their ability to reproduce key properties such as self-diffusivity and isothermal compressibility. The models yielded self-diffusivity values of 0.82 × 10−9 m2 /s and 0.62 × 10−9 m2 /s, both of which are significantly lower than the experimental value of 2.30 × 10−9 m2 /s. This discrepancy is attributed to overly strong hydrogen bonding from the SCAN meta-GGA functional, consistent with prior studies. We also observed that training on multi-phase datasets caused the models to produce structural features characteristic of a mixture of phases, as revealed by the radial distribution functions. While DeePMD produced values closer to experiment, it exhibited unphysical trends, including decreasing diffusivity with increasing system size and decreasing isothermal compressibility with increasing temperature.
These results illustrate both the promise and limitations of MLIPs in capturing the complex behavior of water and suggest that care must be taken in dataset construction and model validation.
The transition toward renewable energy systems requires flexible energy conversion technologies capable of responding to intermittent power availability. A modular alkaline water electrolysis system offers a promising pathway for such flexibility, as it can dynamically adapt its production capacity to match the fluctuating output of sources like photovoltaics. While alkaline water electrolysis is technically mature, its deployment as a large-scale energy storage solution remains economically unattractive under current market conditions. To improve its viability in this role, targeted optimization of system operation and sizing is essential, particularly in direct coupling scenarios where real-time control is required to ensure both efficiency and safety. Existing literature has largely focused on component-level improvements, whereas system-level behavior under variable conditions remains underexplored.
This thesis presents a physics-based 0-D digital twin of a modular AWE system designed to enable optimal operation under varying power conditions. The model explicitly accounts for electrochemical behavior, heat generation and dissipation, and gas crossover dynamics. To the best of the author's knowledge, this is the first model that integrates these coupled physical phenomena within a modular alkaline electrolysis architecture. The model is validated against experimental data reported by Brauns et al., ensuring accurate representation of steady-state and transient system responses.
Based on the steady-state behavior across a range of voltages, flow rates, and temperatures, a novel voltage tracking strategy is developed that defines a safe and efficient operating voltage window. This approach enables the controller to adjust the number of active stacks and regulate operating conditions in real time, based solely on the measured busbar voltage. The method avoids direct intervention at the stack level, allowing for simple and robust implementation even in systems without power electronics. The digital twin thus serves as the foundation for a low-complexity yet effective control strategy and sizing methodology tailored to directly coupled photovoltaic systems. Its performance is evaluated through dynamic simulations using measured solar irradiance and ambient temperature data from multiple real-world locations. Compared to a conventional system equipped with maximum power point tracking (MPPT), the proposed modular configuration achieves approximately 45% higher power utilization and hydrogen yield over the course of a year, demonstrating a clear performance advantage under real-world solar conditions. ...
This thesis presents a physics-based 0-D digital twin of a modular AWE system designed to enable optimal operation under varying power conditions. The model explicitly accounts for electrochemical behavior, heat generation and dissipation, and gas crossover dynamics. To the best of the author's knowledge, this is the first model that integrates these coupled physical phenomena within a modular alkaline electrolysis architecture. The model is validated against experimental data reported by Brauns et al., ensuring accurate representation of steady-state and transient system responses.
Based on the steady-state behavior across a range of voltages, flow rates, and temperatures, a novel voltage tracking strategy is developed that defines a safe and efficient operating voltage window. This approach enables the controller to adjust the number of active stacks and regulate operating conditions in real time, based solely on the measured busbar voltage. The method avoids direct intervention at the stack level, allowing for simple and robust implementation even in systems without power electronics. The digital twin thus serves as the foundation for a low-complexity yet effective control strategy and sizing methodology tailored to directly coupled photovoltaic systems. Its performance is evaluated through dynamic simulations using measured solar irradiance and ambient temperature data from multiple real-world locations. Compared to a conventional system equipped with maximum power point tracking (MPPT), the proposed modular configuration achieves approximately 45% higher power utilization and hydrogen yield over the course of a year, demonstrating a clear performance advantage under real-world solar conditions. ...
The transition toward renewable energy systems requires flexible energy conversion technologies capable of responding to intermittent power availability. A modular alkaline water electrolysis system offers a promising pathway for such flexibility, as it can dynamically adapt its production capacity to match the fluctuating output of sources like photovoltaics. While alkaline water electrolysis is technically mature, its deployment as a large-scale energy storage solution remains economically unattractive under current market conditions. To improve its viability in this role, targeted optimization of system operation and sizing is essential, particularly in direct coupling scenarios where real-time control is required to ensure both efficiency and safety. Existing literature has largely focused on component-level improvements, whereas system-level behavior under variable conditions remains underexplored.
This thesis presents a physics-based 0-D digital twin of a modular AWE system designed to enable optimal operation under varying power conditions. The model explicitly accounts for electrochemical behavior, heat generation and dissipation, and gas crossover dynamics. To the best of the author's knowledge, this is the first model that integrates these coupled physical phenomena within a modular alkaline electrolysis architecture. The model is validated against experimental data reported by Brauns et al., ensuring accurate representation of steady-state and transient system responses.
Based on the steady-state behavior across a range of voltages, flow rates, and temperatures, a novel voltage tracking strategy is developed that defines a safe and efficient operating voltage window. This approach enables the controller to adjust the number of active stacks and regulate operating conditions in real time, based solely on the measured busbar voltage. The method avoids direct intervention at the stack level, allowing for simple and robust implementation even in systems without power electronics. The digital twin thus serves as the foundation for a low-complexity yet effective control strategy and sizing methodology tailored to directly coupled photovoltaic systems. Its performance is evaluated through dynamic simulations using measured solar irradiance and ambient temperature data from multiple real-world locations. Compared to a conventional system equipped with maximum power point tracking (MPPT), the proposed modular configuration achieves approximately 45% higher power utilization and hydrogen yield over the course of a year, demonstrating a clear performance advantage under real-world solar conditions.
This thesis presents a physics-based 0-D digital twin of a modular AWE system designed to enable optimal operation under varying power conditions. The model explicitly accounts for electrochemical behavior, heat generation and dissipation, and gas crossover dynamics. To the best of the author's knowledge, this is the first model that integrates these coupled physical phenomena within a modular alkaline electrolysis architecture. The model is validated against experimental data reported by Brauns et al., ensuring accurate representation of steady-state and transient system responses.
Based on the steady-state behavior across a range of voltages, flow rates, and temperatures, a novel voltage tracking strategy is developed that defines a safe and efficient operating voltage window. This approach enables the controller to adjust the number of active stacks and regulate operating conditions in real time, based solely on the measured busbar voltage. The method avoids direct intervention at the stack level, allowing for simple and robust implementation even in systems without power electronics. The digital twin thus serves as the foundation for a low-complexity yet effective control strategy and sizing methodology tailored to directly coupled photovoltaic systems. Its performance is evaluated through dynamic simulations using measured solar irradiance and ambient temperature data from multiple real-world locations. Compared to a conventional system equipped with maximum power point tracking (MPPT), the proposed modular configuration achieves approximately 45% higher power utilization and hydrogen yield over the course of a year, demonstrating a clear performance advantage under real-world solar conditions.
Magnetic refrigeration is a highly investigated topic with great potential in cooling and heating applications. Its promising efficiency and environmentally friendly operation make it an attractive and resilient solution. The modeling of such thermodynamic systems is a central research focus, due to the high costs of manufacturing and testing real-life designs. To achieve the maximum capabilities of magnetic refrigerators, the tuning of their design parameters is essential. The complexity of magnetic refrigeration applications results in a high-dimensional design space that is difficult to solve analytically.
In this thesis, a surrogate model-based optimization framework was developed and validated for near-room-temperature Active Magnetic Regenerators (AMRs) that balance second-law efficiency against magnet mass. The framework combines a Multi-layer Perceptron (MLP) surrogate model with a genetic algorithm to efficiently explore a design space defined by more than eight parameters: length, width, and height of the regenerator, number of magnetocaloric material (MCM) layers, individual layer thicknesses, Curie temperature per layer, porosity of MCM layers, applied magnetic field, and void spaces. The surrogate model approximates a computationally expensive 1-D thermodynamic AMR model, reducing evaluation time and paving the way to multi-dimensional complex optimization AMR problems. The results demonstrate that with sequential model-based optimization (SMBO), the model can predict with higher accuracy the efficiency of each design, eventually leading to various design configurations with high efficiency and within the desired cost limits. The effect of SMBO is captured every training round by the mean absolute error (MAE) metric.
The optimal AMR configuration identified through this framework for a 15 K temperature span features 8 MCM layers with Curie temperatures spanning 274.8 K to 291.8 K, a regenerator geometry of 68 mm × 25 mm × 29 mm (length × width × height), 23% porosity for the MCM blocks, and operates under a 1.3 T magnetic field. This configuration represents a practical balance between hermodynamic performance and manufacturing feasibility for near-room-temperature magnetic refrigeration applications. ...
In this thesis, a surrogate model-based optimization framework was developed and validated for near-room-temperature Active Magnetic Regenerators (AMRs) that balance second-law efficiency against magnet mass. The framework combines a Multi-layer Perceptron (MLP) surrogate model with a genetic algorithm to efficiently explore a design space defined by more than eight parameters: length, width, and height of the regenerator, number of magnetocaloric material (MCM) layers, individual layer thicknesses, Curie temperature per layer, porosity of MCM layers, applied magnetic field, and void spaces. The surrogate model approximates a computationally expensive 1-D thermodynamic AMR model, reducing evaluation time and paving the way to multi-dimensional complex optimization AMR problems. The results demonstrate that with sequential model-based optimization (SMBO), the model can predict with higher accuracy the efficiency of each design, eventually leading to various design configurations with high efficiency and within the desired cost limits. The effect of SMBO is captured every training round by the mean absolute error (MAE) metric.
The optimal AMR configuration identified through this framework for a 15 K temperature span features 8 MCM layers with Curie temperatures spanning 274.8 K to 291.8 K, a regenerator geometry of 68 mm × 25 mm × 29 mm (length × width × height), 23% porosity for the MCM blocks, and operates under a 1.3 T magnetic field. This configuration represents a practical balance between hermodynamic performance and manufacturing feasibility for near-room-temperature magnetic refrigeration applications. ...
Magnetic refrigeration is a highly investigated topic with great potential in cooling and heating applications. Its promising efficiency and environmentally friendly operation make it an attractive and resilient solution. The modeling of such thermodynamic systems is a central research focus, due to the high costs of manufacturing and testing real-life designs. To achieve the maximum capabilities of magnetic refrigerators, the tuning of their design parameters is essential. The complexity of magnetic refrigeration applications results in a high-dimensional design space that is difficult to solve analytically.
In this thesis, a surrogate model-based optimization framework was developed and validated for near-room-temperature Active Magnetic Regenerators (AMRs) that balance second-law efficiency against magnet mass. The framework combines a Multi-layer Perceptron (MLP) surrogate model with a genetic algorithm to efficiently explore a design space defined by more than eight parameters: length, width, and height of the regenerator, number of magnetocaloric material (MCM) layers, individual layer thicknesses, Curie temperature per layer, porosity of MCM layers, applied magnetic field, and void spaces. The surrogate model approximates a computationally expensive 1-D thermodynamic AMR model, reducing evaluation time and paving the way to multi-dimensional complex optimization AMR problems. The results demonstrate that with sequential model-based optimization (SMBO), the model can predict with higher accuracy the efficiency of each design, eventually leading to various design configurations with high efficiency and within the desired cost limits. The effect of SMBO is captured every training round by the mean absolute error (MAE) metric.
The optimal AMR configuration identified through this framework for a 15 K temperature span features 8 MCM layers with Curie temperatures spanning 274.8 K to 291.8 K, a regenerator geometry of 68 mm × 25 mm × 29 mm (length × width × height), 23% porosity for the MCM blocks, and operates under a 1.3 T magnetic field. This configuration represents a practical balance between hermodynamic performance and manufacturing feasibility for near-room-temperature magnetic refrigeration applications.
In this thesis, a surrogate model-based optimization framework was developed and validated for near-room-temperature Active Magnetic Regenerators (AMRs) that balance second-law efficiency against magnet mass. The framework combines a Multi-layer Perceptron (MLP) surrogate model with a genetic algorithm to efficiently explore a design space defined by more than eight parameters: length, width, and height of the regenerator, number of magnetocaloric material (MCM) layers, individual layer thicknesses, Curie temperature per layer, porosity of MCM layers, applied magnetic field, and void spaces. The surrogate model approximates a computationally expensive 1-D thermodynamic AMR model, reducing evaluation time and paving the way to multi-dimensional complex optimization AMR problems. The results demonstrate that with sequential model-based optimization (SMBO), the model can predict with higher accuracy the efficiency of each design, eventually leading to various design configurations with high efficiency and within the desired cost limits. The effect of SMBO is captured every training round by the mean absolute error (MAE) metric.
The optimal AMR configuration identified through this framework for a 15 K temperature span features 8 MCM layers with Curie temperatures spanning 274.8 K to 291.8 K, a regenerator geometry of 68 mm × 25 mm × 29 mm (length × width × height), 23% porosity for the MCM blocks, and operates under a 1.3 T magnetic field. This configuration represents a practical balance between hermodynamic performance and manufacturing feasibility for near-room-temperature magnetic refrigeration applications.
Molecular Simulations for Hydrogen Storage and Production
From quantum to force field-based methods
In this thesis, molecular simulations are performed to design and assess novel 2D materials for H2 storage applications (chapters 2-3) and to predict thermodynamic and transport properties of H2 in aqueous electrolyte solutions for storage and production of H2 (chapters 4-8). Both ab-initio and force field-based methods are used in this thesis.....
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In this thesis, molecular simulations are performed to design and assess novel 2D materials for H2 storage applications (chapters 2-3) and to predict thermodynamic and transport properties of H2 in aqueous electrolyte solutions for storage and production of H2 (chapters 4-8). Both ab-initio and force field-based methods are used in this thesis.....
De-carbonizing aviation is necessary for a sustainable future, and using hydrogen in a fuel cell, that produces water, can greatly reduce greenhouse gas emissions. Achieving higher gravimetric energy density with hydrogen compared to conventional jet fuels, involves storing it in cryogenic liquid form. Along with it, its cryogenic temperature range not only enables its use as chemical energy storage but also as a potential heat sink. However, rapid vaporization, known as boil-off, limits the long-term storage of liquid hydrogen in fuel tanks, requiring regular venting due to self-pressurization over time. Additionally, hydrogen needs to be heated to the fuel cell’s operating temperature before being used as a reactant. Understanding these requirements, this thesis focuses on three areas: predicting the maximum boil-off rate of liquid hydrogen while charging the fuel tank using a MATLAB simulation and the boil-off rate during the flight journey using a validated software called BoilFAST to understand the feasibility of retrieving the boil-off for its integration with the fuel supply; designing a fuel supply system that integrates the boil-off gas with the vaporized liquid hydrogen supply line to the fuel cell system; and integrating the cryogenic energy of hydrogen with a ram air-cooled vapor compression refrigeration system (VCRS) based thermal management of fuel cell, with the intention of reducing its parasitic load and improving system compactness. Two methods were used for the thermal integration: VCRS involving fuel cooled heat exchangers that function as an intercooler and as a separate de-superheater before a ram air-cooled condenser; and VCRS with a separate single-phase, 52\% ethylene-glycol based serial cooling circuit with multiple fuel cooled heat exchangers (FCHX). This resulted in a significant reduction of parasitic load by 13.4% and 26% when integrated with the intercooler system and single-phase serial cooling system, respectively. The study also examined the expected additional component weight, considering the aviation sector's preference for lighter systems. The findings demonstrate that the holding time of the fuel for minimum 13 minutes after tank filling and before the start of the propulsion system unit can allow a controlled amount of boil-off gas to be integrated with the fuel supply. Utilizing cryogenic energy for thermal management can significantly enhance the system's coefficient of performance by 15.3% and 33.3% respectively. Future work should involve experiments to obtain actual boil-off rates at different ambient exposures of fuel tank, tests on sloshing effect due to turbulence during flight journeys, analysis of thermal stress effects in cryogenic heat exchangers due to high temperature gradients, and testing new compatible mixed refrigerants with improved thermal properties for optimum cryogenic heat exchange.
...
De-carbonizing aviation is necessary for a sustainable future, and using hydrogen in a fuel cell, that produces water, can greatly reduce greenhouse gas emissions. Achieving higher gravimetric energy density with hydrogen compared to conventional jet fuels, involves storing it in cryogenic liquid form. Along with it, its cryogenic temperature range not only enables its use as chemical energy storage but also as a potential heat sink. However, rapid vaporization, known as boil-off, limits the long-term storage of liquid hydrogen in fuel tanks, requiring regular venting due to self-pressurization over time. Additionally, hydrogen needs to be heated to the fuel cell’s operating temperature before being used as a reactant. Understanding these requirements, this thesis focuses on three areas: predicting the maximum boil-off rate of liquid hydrogen while charging the fuel tank using a MATLAB simulation and the boil-off rate during the flight journey using a validated software called BoilFAST to understand the feasibility of retrieving the boil-off for its integration with the fuel supply; designing a fuel supply system that integrates the boil-off gas with the vaporized liquid hydrogen supply line to the fuel cell system; and integrating the cryogenic energy of hydrogen with a ram air-cooled vapor compression refrigeration system (VCRS) based thermal management of fuel cell, with the intention of reducing its parasitic load and improving system compactness. Two methods were used for the thermal integration: VCRS involving fuel cooled heat exchangers that function as an intercooler and as a separate de-superheater before a ram air-cooled condenser; and VCRS with a separate single-phase, 52\% ethylene-glycol based serial cooling circuit with multiple fuel cooled heat exchangers (FCHX). This resulted in a significant reduction of parasitic load by 13.4% and 26% when integrated with the intercooler system and single-phase serial cooling system, respectively. The study also examined the expected additional component weight, considering the aviation sector's preference for lighter systems. The findings demonstrate that the holding time of the fuel for minimum 13 minutes after tank filling and before the start of the propulsion system unit can allow a controlled amount of boil-off gas to be integrated with the fuel supply. Utilizing cryogenic energy for thermal management can significantly enhance the system's coefficient of performance by 15.3% and 33.3% respectively. Future work should involve experiments to obtain actual boil-off rates at different ambient exposures of fuel tank, tests on sloshing effect due to turbulence during flight journeys, analysis of thermal stress effects in cryogenic heat exchangers due to high temperature gradients, and testing new compatible mixed refrigerants with improved thermal properties for optimum cryogenic heat exchange.
Steel manufacturing is a carbon intensive process, that is responsible for approximately 7% of the total global CO2 emissions. Therefore, TATA Steel IJmuiden aims to lower its carbon emissions. One way to bring down emissions, is to replace the existing blast furnace (BF) CO reduction process with the H2-based direct reduction of iron ore (DRI). The two most widely applied H2-DRI processes around the world are the low pressure MIDREX, abbreviated as MLP, and medium pressure HYL-Energiron, abbreviated as EMP. Since TATA Steel IJmuiden wants to study the switch from BF to H2-DRI steelmaking, it is relevant to gain insight into which gas phase reactions are dominant for both processes, into the direct reduction process itself and into the behaviour of the carburization reactions that improve the steel quality. In the gas phase reactions, it was seen that for the MLP process in situ reforming of natural gas can be a viable option before switching to a 100% H2 process. This may prove to be worthwhile in the early stages of H2-DRI steel production, when green H2 is still scarce and expensive. For EMP, internal reforming seems less of a possibility due to the high reaction rate for the reverse water gas-shift. When comparing both MLP and EMP, reaction rates are generally higher for EMP than for MLP and hence smaller reactor volumes are required for the EMP process to acquire the same amount of output. Direct reduction with H2 has a higher reaction rate than reduction with CO, while for the carburization reactions methane cracking was found to be the dominant reaction. Techno-economic scenarios for 100% H2-based DRI in which green H2 is imported are only feasible when H2-prices fall below €1.80/kg. Meanwhile, a scenario with an on-site electrolyzer powered by grey grid electricity only proves to be worthwhile for electricity prices lower than €20/MWh. The most promising techno-economic scenario, which includes an on-site electrolyzer and the construction of a wind farm just off the coast from the TATA Steel IJmuiden site, assumes an electricity price of €40/MWh.
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Steel manufacturing is a carbon intensive process, that is responsible for approximately 7% of the total global CO2 emissions. Therefore, TATA Steel IJmuiden aims to lower its carbon emissions. One way to bring down emissions, is to replace the existing blast furnace (BF) CO reduction process with the H2-based direct reduction of iron ore (DRI). The two most widely applied H2-DRI processes around the world are the low pressure MIDREX, abbreviated as MLP, and medium pressure HYL-Energiron, abbreviated as EMP. Since TATA Steel IJmuiden wants to study the switch from BF to H2-DRI steelmaking, it is relevant to gain insight into which gas phase reactions are dominant for both processes, into the direct reduction process itself and into the behaviour of the carburization reactions that improve the steel quality. In the gas phase reactions, it was seen that for the MLP process in situ reforming of natural gas can be a viable option before switching to a 100% H2 process. This may prove to be worthwhile in the early stages of H2-DRI steel production, when green H2 is still scarce and expensive. For EMP, internal reforming seems less of a possibility due to the high reaction rate for the reverse water gas-shift. When comparing both MLP and EMP, reaction rates are generally higher for EMP than for MLP and hence smaller reactor volumes are required for the EMP process to acquire the same amount of output. Direct reduction with H2 has a higher reaction rate than reduction with CO, while for the carburization reactions methane cracking was found to be the dominant reaction. Techno-economic scenarios for 100% H2-based DRI in which green H2 is imported are only feasible when H2-prices fall below €1.80/kg. Meanwhile, a scenario with an on-site electrolyzer powered by grey grid electricity only proves to be worthwhile for electricity prices lower than €20/MWh. The most promising techno-economic scenario, which includes an on-site electrolyzer and the construction of a wind farm just off the coast from the TATA Steel IJmuiden site, assumes an electricity price of €40/MWh.
Hydrogen is seen as a clean energy carrier that will aid in phasing out fossil fuels, and liquid hydrogen will likely play a role in the global hydrogen economy. However, Life Cycle Assessment researchers and process engineers widely report having insufficient means to accurately assess the environmental impacts of hydrogen liquefaction. This thesis presents an LCA study of hydrogen liquefiers based on current public data and literature. Additionally, a simplified model for hydrogen liquefaction was developed to aid future LCA researchers. An original Life Cycle Assessment of hydrogen value chains incorporating liquefaction is also included. The environmental harm was calculated in the form of direct monetary prevention costs, using the increasingly popular Eco-cost method. The environmental impacts of various sizes and types of hydrogen liquefiers are compared, while hotspots of environmental harm were determined. The main findings indicate that the primary source of environmental impact for hydrogen liquefaction is power consumption, with refrigerant leakage being insignificant as long as hydrocarbons are used for the precooling mixture.
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Hydrogen is seen as a clean energy carrier that will aid in phasing out fossil fuels, and liquid hydrogen will likely play a role in the global hydrogen economy. However, Life Cycle Assessment researchers and process engineers widely report having insufficient means to accurately assess the environmental impacts of hydrogen liquefaction. This thesis presents an LCA study of hydrogen liquefiers based on current public data and literature. Additionally, a simplified model for hydrogen liquefaction was developed to aid future LCA researchers. An original Life Cycle Assessment of hydrogen value chains incorporating liquefaction is also included. The environmental harm was calculated in the form of direct monetary prevention costs, using the increasingly popular Eco-cost method. The environmental impacts of various sizes and types of hydrogen liquefiers are compared, while hotspots of environmental harm were determined. The main findings indicate that the primary source of environmental impact for hydrogen liquefaction is power consumption, with refrigerant leakage being insignificant as long as hydrocarbons are used for the precooling mixture.
As the global energy transition accelerates, transitioning away from fossil fuels has become critical to assist the mitigation of climate change. Hydrogen, with its dual role as both an energy carrier and fuel, holds great potential for this transition. However, its large-scale production, particularly for industries like chemicals and transportation, still relies heavily on fossil fuels, posing a challenge for sustainable development.
This research focuses on advancing green hydrogen production, specifically through alkaline water electrolysis, a technology associated with zero greenhouse gas emissions. A key aspect of this work is addressing a gap in large-scale electrolysis modeling, with an emphasis on a modular system design. Modularity, as opposed to traditional single-unit scaling, offers improved operational flexibility and safety. This approach is especially relevant when electrolysis systems are powered by renewable energy sources, another critical component of the energy transition.
This thesis presents an investigation into the performance optimization of a modular alkaline water electrolysis system, designed to handle fluctuating renewable energy inputs. A physics-based numerical model was developed in Python, to simulate a large-scale AWE system composed of multiple modular units, capturing critical parameters such as temperature evolution, gas purity, and energy losses. The model was built progressively, starting from the cell level, incorporating a thermal model for temperature development and a mass transfer model for gas purity estimation. These combined elements formed a robust tool for simulating and optimizing the performance of modular electrolysis systems.
After validating the model against existing numerical and experimental data at the cell level, it demonstrated a strong ability to accurately capture the behavior of a single-cell system across all modeled parameters, including cell potential, temperature, and gas impurities. The differences between the simulated values and experimental data were minimal, further confirming the model's accuracy and reliability. This validation provided confidence in the model's predictive capabilities and laid the foundation for extending it for a larger modular system.
Following this, a scaling analysis was conducted to evaluate the model's performance when applied to a modular system. The simulations were carried out under both steady and varying power inputs, reflecting realistic operational conditions, particularly when coupled with renewable energy sources. The results highlighted the model's capacity to predict temperature evolution and gas impurity levels in such a scaled system. These findings indicated that the model not only captured the thermal and mass transfer behavior but also provided valuable insights into the effect of system scaling on overall performance and safety.
The outcomes of this research demonstrate that the developed model could act as a valuable tool for optimizing the performance of modular alkaline water electrolysis systems. The model successfully predicts the thermal behavior, gas purity, and energy losses across a range of operational conditions, including fluctuating power inputs typical of renewable energy sources. By enabling the fine-tuning of operational parameters prior to system deployment, this model provides a significant advantage in designing safe, efficient, and scalable hydrogen production systems. Future work could extend the model's capabilities by incorporating additional factors such as degradation mechanisms and detailed component-level interactions, ensuring even more robust predictions over long-term operation. ...
This research focuses on advancing green hydrogen production, specifically through alkaline water electrolysis, a technology associated with zero greenhouse gas emissions. A key aspect of this work is addressing a gap in large-scale electrolysis modeling, with an emphasis on a modular system design. Modularity, as opposed to traditional single-unit scaling, offers improved operational flexibility and safety. This approach is especially relevant when electrolysis systems are powered by renewable energy sources, another critical component of the energy transition.
This thesis presents an investigation into the performance optimization of a modular alkaline water electrolysis system, designed to handle fluctuating renewable energy inputs. A physics-based numerical model was developed in Python, to simulate a large-scale AWE system composed of multiple modular units, capturing critical parameters such as temperature evolution, gas purity, and energy losses. The model was built progressively, starting from the cell level, incorporating a thermal model for temperature development and a mass transfer model for gas purity estimation. These combined elements formed a robust tool for simulating and optimizing the performance of modular electrolysis systems.
After validating the model against existing numerical and experimental data at the cell level, it demonstrated a strong ability to accurately capture the behavior of a single-cell system across all modeled parameters, including cell potential, temperature, and gas impurities. The differences between the simulated values and experimental data were minimal, further confirming the model's accuracy and reliability. This validation provided confidence in the model's predictive capabilities and laid the foundation for extending it for a larger modular system.
Following this, a scaling analysis was conducted to evaluate the model's performance when applied to a modular system. The simulations were carried out under both steady and varying power inputs, reflecting realistic operational conditions, particularly when coupled with renewable energy sources. The results highlighted the model's capacity to predict temperature evolution and gas impurity levels in such a scaled system. These findings indicated that the model not only captured the thermal and mass transfer behavior but also provided valuable insights into the effect of system scaling on overall performance and safety.
The outcomes of this research demonstrate that the developed model could act as a valuable tool for optimizing the performance of modular alkaline water electrolysis systems. The model successfully predicts the thermal behavior, gas purity, and energy losses across a range of operational conditions, including fluctuating power inputs typical of renewable energy sources. By enabling the fine-tuning of operational parameters prior to system deployment, this model provides a significant advantage in designing safe, efficient, and scalable hydrogen production systems. Future work could extend the model's capabilities by incorporating additional factors such as degradation mechanisms and detailed component-level interactions, ensuring even more robust predictions over long-term operation. ...
As the global energy transition accelerates, transitioning away from fossil fuels has become critical to assist the mitigation of climate change. Hydrogen, with its dual role as both an energy carrier and fuel, holds great potential for this transition. However, its large-scale production, particularly for industries like chemicals and transportation, still relies heavily on fossil fuels, posing a challenge for sustainable development.
This research focuses on advancing green hydrogen production, specifically through alkaline water electrolysis, a technology associated with zero greenhouse gas emissions. A key aspect of this work is addressing a gap in large-scale electrolysis modeling, with an emphasis on a modular system design. Modularity, as opposed to traditional single-unit scaling, offers improved operational flexibility and safety. This approach is especially relevant when electrolysis systems are powered by renewable energy sources, another critical component of the energy transition.
This thesis presents an investigation into the performance optimization of a modular alkaline water electrolysis system, designed to handle fluctuating renewable energy inputs. A physics-based numerical model was developed in Python, to simulate a large-scale AWE system composed of multiple modular units, capturing critical parameters such as temperature evolution, gas purity, and energy losses. The model was built progressively, starting from the cell level, incorporating a thermal model for temperature development and a mass transfer model for gas purity estimation. These combined elements formed a robust tool for simulating and optimizing the performance of modular electrolysis systems.
After validating the model against existing numerical and experimental data at the cell level, it demonstrated a strong ability to accurately capture the behavior of a single-cell system across all modeled parameters, including cell potential, temperature, and gas impurities. The differences between the simulated values and experimental data were minimal, further confirming the model's accuracy and reliability. This validation provided confidence in the model's predictive capabilities and laid the foundation for extending it for a larger modular system.
Following this, a scaling analysis was conducted to evaluate the model's performance when applied to a modular system. The simulations were carried out under both steady and varying power inputs, reflecting realistic operational conditions, particularly when coupled with renewable energy sources. The results highlighted the model's capacity to predict temperature evolution and gas impurity levels in such a scaled system. These findings indicated that the model not only captured the thermal and mass transfer behavior but also provided valuable insights into the effect of system scaling on overall performance and safety.
The outcomes of this research demonstrate that the developed model could act as a valuable tool for optimizing the performance of modular alkaline water electrolysis systems. The model successfully predicts the thermal behavior, gas purity, and energy losses across a range of operational conditions, including fluctuating power inputs typical of renewable energy sources. By enabling the fine-tuning of operational parameters prior to system deployment, this model provides a significant advantage in designing safe, efficient, and scalable hydrogen production systems. Future work could extend the model's capabilities by incorporating additional factors such as degradation mechanisms and detailed component-level interactions, ensuring even more robust predictions over long-term operation.
This research focuses on advancing green hydrogen production, specifically through alkaline water electrolysis, a technology associated with zero greenhouse gas emissions. A key aspect of this work is addressing a gap in large-scale electrolysis modeling, with an emphasis on a modular system design. Modularity, as opposed to traditional single-unit scaling, offers improved operational flexibility and safety. This approach is especially relevant when electrolysis systems are powered by renewable energy sources, another critical component of the energy transition.
This thesis presents an investigation into the performance optimization of a modular alkaline water electrolysis system, designed to handle fluctuating renewable energy inputs. A physics-based numerical model was developed in Python, to simulate a large-scale AWE system composed of multiple modular units, capturing critical parameters such as temperature evolution, gas purity, and energy losses. The model was built progressively, starting from the cell level, incorporating a thermal model for temperature development and a mass transfer model for gas purity estimation. These combined elements formed a robust tool for simulating and optimizing the performance of modular electrolysis systems.
After validating the model against existing numerical and experimental data at the cell level, it demonstrated a strong ability to accurately capture the behavior of a single-cell system across all modeled parameters, including cell potential, temperature, and gas impurities. The differences between the simulated values and experimental data were minimal, further confirming the model's accuracy and reliability. This validation provided confidence in the model's predictive capabilities and laid the foundation for extending it for a larger modular system.
Following this, a scaling analysis was conducted to evaluate the model's performance when applied to a modular system. The simulations were carried out under both steady and varying power inputs, reflecting realistic operational conditions, particularly when coupled with renewable energy sources. The results highlighted the model's capacity to predict temperature evolution and gas impurity levels in such a scaled system. These findings indicated that the model not only captured the thermal and mass transfer behavior but also provided valuable insights into the effect of system scaling on overall performance and safety.
The outcomes of this research demonstrate that the developed model could act as a valuable tool for optimizing the performance of modular alkaline water electrolysis systems. The model successfully predicts the thermal behavior, gas purity, and energy losses across a range of operational conditions, including fluctuating power inputs typical of renewable energy sources. By enabling the fine-tuning of operational parameters prior to system deployment, this model provides a significant advantage in designing safe, efficient, and scalable hydrogen production systems. Future work could extend the model's capabilities by incorporating additional factors such as degradation mechanisms and detailed component-level interactions, ensuring even more robust predictions over long-term operation.
Molecular simulations predict the thermodynamic and transport properties by computing the interactions between the molecules in a system. These simulations offer practical alternatives to address challenges arising from experimental limitations in measuring Vapor-Liquid Equilibria (VLE) of acid gases at very low partial pressures and the diffusivities in reactive solutions.
In this thesis, we investigated how force field-based molecular simulations can be used to compute reaction equilibria and transport properties, relevant for absorption-based CO2 and H2S removal. We introduced novel features to the Brick-CFCMC code and developed a versatile chemical reaction equilibria solver, called CASpy, to compute the concentration of species in any reactive liquid-phase absorption system, including CO2 and H2S absorption in aqueous alkanolamine solutions. We also investigated transport properties of CO2 and H2S in aqueous solutions of two commonly used alkanolamines, MEA and MDEA.
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In this thesis, we investigated how force field-based molecular simulations can be used to compute reaction equilibria and transport properties, relevant for absorption-based CO2 and H2S removal. We introduced novel features to the Brick-CFCMC code and developed a versatile chemical reaction equilibria solver, called CASpy, to compute the concentration of species in any reactive liquid-phase absorption system, including CO2 and H2S absorption in aqueous alkanolamine solutions. We also investigated transport properties of CO2 and H2S in aqueous solutions of two commonly used alkanolamines, MEA and MDEA.
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Molecular simulations predict the thermodynamic and transport properties by computing the interactions between the molecules in a system. These simulations offer practical alternatives to address challenges arising from experimental limitations in measuring Vapor-Liquid Equilibria (VLE) of acid gases at very low partial pressures and the diffusivities in reactive solutions.
In this thesis, we investigated how force field-based molecular simulations can be used to compute reaction equilibria and transport properties, relevant for absorption-based CO2 and H2S removal. We introduced novel features to the Brick-CFCMC code and developed a versatile chemical reaction equilibria solver, called CASpy, to compute the concentration of species in any reactive liquid-phase absorption system, including CO2 and H2S absorption in aqueous alkanolamine solutions. We also investigated transport properties of CO2 and H2S in aqueous solutions of two commonly used alkanolamines, MEA and MDEA.
In this thesis, we investigated how force field-based molecular simulations can be used to compute reaction equilibria and transport properties, relevant for absorption-based CO2 and H2S removal. We introduced novel features to the Brick-CFCMC code and developed a versatile chemical reaction equilibria solver, called CASpy, to compute the concentration of species in any reactive liquid-phase absorption system, including CO2 and H2S absorption in aqueous alkanolamine solutions. We also investigated transport properties of CO2 and H2S in aqueous solutions of two commonly used alkanolamines, MEA and MDEA.
The utilization of water electrolysis for hydrogen production is expected to increase significantly in the upcoming decades. Modular system design offers great advantages for emerging markets, whilst providing the necessary flexibility in capacity that intermittent renewable energy sources require. Literature research reveals little information regarding modular water electrolysis systems. However, their availability on the consumer market proves that there is demand for such systems. To facilitate the emerging hydrogen market, a modular engineering design approach is used to develop a containerized alkaline water electrolysis system. This containerized system achieves a high degree of modularity by utilizing multiple modular sections. Each section functions as a stand-alone hydrogen production station, containing all necessary components required for safe hydrogen production. These components include heat exchangers, gas-liquid separators, modular alkaline electrolyzers, and a novel component called flow panels. In conjunction with the Dutch company XINTC, the design of the modular sections and their components is presented. Significant capital expenditure reduction is achieved by operating at atmospheric pressure, allowing for cost-efficient material selection and their corresponding manufacturing methods; both of which are discussed. Thermal, hydrodynamic, and particle dynamic models from literature are presented and evaluated. The proposed hydrogen separator is expected to achieve a separation efficiency of over 99.8%. A thermal system analysis concludes that the system is capable of operating at maximum capacity for ambient temperatures of up to 35 C. Additionally, a hydrodynamic system analysis shows that the pressure drop inside the system is dominated by that of the heat exchanger, and is in conformity with the Pressure Equipment Directive.
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The utilization of water electrolysis for hydrogen production is expected to increase significantly in the upcoming decades. Modular system design offers great advantages for emerging markets, whilst providing the necessary flexibility in capacity that intermittent renewable energy sources require. Literature research reveals little information regarding modular water electrolysis systems. However, their availability on the consumer market proves that there is demand for such systems. To facilitate the emerging hydrogen market, a modular engineering design approach is used to develop a containerized alkaline water electrolysis system. This containerized system achieves a high degree of modularity by utilizing multiple modular sections. Each section functions as a stand-alone hydrogen production station, containing all necessary components required for safe hydrogen production. These components include heat exchangers, gas-liquid separators, modular alkaline electrolyzers, and a novel component called flow panels. In conjunction with the Dutch company XINTC, the design of the modular sections and their components is presented. Significant capital expenditure reduction is achieved by operating at atmospheric pressure, allowing for cost-efficient material selection and their corresponding manufacturing methods; both of which are discussed. Thermal, hydrodynamic, and particle dynamic models from literature are presented and evaluated. The proposed hydrogen separator is expected to achieve a separation efficiency of over 99.8%. A thermal system analysis concludes that the system is capable of operating at maximum capacity for ambient temperatures of up to 35 C. Additionally, a hydrodynamic system analysis shows that the pressure drop inside the system is dominated by that of the heat exchanger, and is in conformity with the Pressure Equipment Directive.
The long-distance maritime transport sector plays one of the most relevant roles in the transport sector. Therefore, this project designs a modular solid hydrogen solution, studying the assumptions made in the literature and including new simplifications that reduce the computational costs of its simulation. Moreover, this project compares the viability of this solution with the different prototypes found on the market.
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The long-distance maritime transport sector plays one of the most relevant roles in the transport sector. Therefore, this project designs a modular solid hydrogen solution, studying the assumptions made in the literature and including new simplifications that reduce the computational costs of its simulation. Moreover, this project compares the viability of this solution with the different prototypes found on the market.
Quantum to Transport
Modeling Transport Properties of Aqueous Potassium Hydroxide by Machine Learning Molecular Force Fields from Quantum Mechanics
In this work, the added value of machine learning (ML) molecular force fields (FF) for the community of molecular simulations is showcased by successfully calculating transport properties of aqueous potassium hydroxide (KOH (aq)). Classical FFs use relatively simple interatomic potentials to simulate the nano scale. These simulations can predict macroscopic properties, such as density, heat of evaporation, viscosity, and self-diffusivity of the modeled materials. However, these FFs struggle to model materials in which more complicated interactions are relevant for the macroscopic behavior. Examples of such interactions are three-body interactions and chemical reactions. Quantum scale simulation methods are able to compute properties of materials in which these challenging interactions occur, although these methods are limited in length and time scales that can be modeled with realistic computational costs. Transport properties, such as viscosity, self-diffusivity and electric conductivity need these larger length and time scales to be determined accurately. ML can be used for a multi scale approach, bridging the gap between the quantum and the nano scale by training coefficients of general interatomic potentials. This provides the possibility of reaching the time and length scales of traditional molecular simulations with the accuracy of quantum mechanic models. KOH (aq) is selected to highlight the prospects of these multi scale techniques, as the self-diffusion of OH- in this electrolyte is dominated by proton transfer reactions, which has not been modeled successfully with classical FFs.
Results of structure properties produced with ab initio molecular dynamics (AIMD, at quantum scale) simulations are compared with machine learning molecular dynamics (MLMD, at multi scale) simulations. There are no significant differences in the calculated shortest typical atomic distances and coordination numbers for both KOH (aq) and pure water systems. The determined transport properties are in the same order of magnitude as experimental results, although the calculated viscosity is overestimated and the self-diffusion of H2O and K+ are underestimated. This is because the system is simulated at a higher than experimental density and hydrogen bonding is overestimated with the selected quantum mechanics model. The proton transfer reactions are captured in the MLMD simulations, calculating the enhanced self-diffusion of OH- to be (6±2)e-9 m squared per second, which matches experimental results at infinite dilution. ...
Results of structure properties produced with ab initio molecular dynamics (AIMD, at quantum scale) simulations are compared with machine learning molecular dynamics (MLMD, at multi scale) simulations. There are no significant differences in the calculated shortest typical atomic distances and coordination numbers for both KOH (aq) and pure water systems. The determined transport properties are in the same order of magnitude as experimental results, although the calculated viscosity is overestimated and the self-diffusion of H2O and K+ are underestimated. This is because the system is simulated at a higher than experimental density and hydrogen bonding is overestimated with the selected quantum mechanics model. The proton transfer reactions are captured in the MLMD simulations, calculating the enhanced self-diffusion of OH- to be (6±2)e-9 m squared per second, which matches experimental results at infinite dilution. ...
In this work, the added value of machine learning (ML) molecular force fields (FF) for the community of molecular simulations is showcased by successfully calculating transport properties of aqueous potassium hydroxide (KOH (aq)). Classical FFs use relatively simple interatomic potentials to simulate the nano scale. These simulations can predict macroscopic properties, such as density, heat of evaporation, viscosity, and self-diffusivity of the modeled materials. However, these FFs struggle to model materials in which more complicated interactions are relevant for the macroscopic behavior. Examples of such interactions are three-body interactions and chemical reactions. Quantum scale simulation methods are able to compute properties of materials in which these challenging interactions occur, although these methods are limited in length and time scales that can be modeled with realistic computational costs. Transport properties, such as viscosity, self-diffusivity and electric conductivity need these larger length and time scales to be determined accurately. ML can be used for a multi scale approach, bridging the gap between the quantum and the nano scale by training coefficients of general interatomic potentials. This provides the possibility of reaching the time and length scales of traditional molecular simulations with the accuracy of quantum mechanic models. KOH (aq) is selected to highlight the prospects of these multi scale techniques, as the self-diffusion of OH- in this electrolyte is dominated by proton transfer reactions, which has not been modeled successfully with classical FFs.
Results of structure properties produced with ab initio molecular dynamics (AIMD, at quantum scale) simulations are compared with machine learning molecular dynamics (MLMD, at multi scale) simulations. There are no significant differences in the calculated shortest typical atomic distances and coordination numbers for both KOH (aq) and pure water systems. The determined transport properties are in the same order of magnitude as experimental results, although the calculated viscosity is overestimated and the self-diffusion of H2O and K+ are underestimated. This is because the system is simulated at a higher than experimental density and hydrogen bonding is overestimated with the selected quantum mechanics model. The proton transfer reactions are captured in the MLMD simulations, calculating the enhanced self-diffusion of OH- to be (6±2)e-9 m squared per second, which matches experimental results at infinite dilution.
Results of structure properties produced with ab initio molecular dynamics (AIMD, at quantum scale) simulations are compared with machine learning molecular dynamics (MLMD, at multi scale) simulations. There are no significant differences in the calculated shortest typical atomic distances and coordination numbers for both KOH (aq) and pure water systems. The determined transport properties are in the same order of magnitude as experimental results, although the calculated viscosity is overestimated and the self-diffusion of H2O and K+ are underestimated. This is because the system is simulated at a higher than experimental density and hydrogen bonding is overestimated with the selected quantum mechanics model. The proton transfer reactions are captured in the MLMD simulations, calculating the enhanced self-diffusion of OH- to be (6±2)e-9 m squared per second, which matches experimental results at infinite dilution.
In this thesis, the first part will be a review of the literature that includes as much of the commercially available information on carbon capture and mainly, Direct Air Capture (DAC). What are the difficulties surrounding capturing CO2 from gas mixtures and what are the possible solutions that have been investigated? What type of negative emission technologies have been proposed, what are the important parameters for the design of carbon removal technologies and what are the biggest obstacles that need to be overcome? Gathering sufficient data and comparing the different finding will be of importance, given that DAC technologies are still in developing stages and information is limited. The focus of this thesis will be on chemically amine based solid sorbents, as this can be argued that this is the most promising technology for a viable DAC system.
The modelling part of the thesis will be focused on the design of and development of a DAC model in the open source software ’Python’. The amine based sorbent that is investigated and used for the creation of the model is Lewatit VP OC 1065. Using experimental data gathered from the literature together with feasible assumptions, a model will be built to recreate the the whole DAC process and analyse the system. The main focus will be on acquiring a flexible model for both the adsorption and desorption parts of the DAC process to make further investigation of the system parameters possible. This model will be used after this thesis for further development and, for instance, analysing different possible sorbents. ...
The modelling part of the thesis will be focused on the design of and development of a DAC model in the open source software ’Python’. The amine based sorbent that is investigated and used for the creation of the model is Lewatit VP OC 1065. Using experimental data gathered from the literature together with feasible assumptions, a model will be built to recreate the the whole DAC process and analyse the system. The main focus will be on acquiring a flexible model for both the adsorption and desorption parts of the DAC process to make further investigation of the system parameters possible. This model will be used after this thesis for further development and, for instance, analysing different possible sorbents. ...
In this thesis, the first part will be a review of the literature that includes as much of the commercially available information on carbon capture and mainly, Direct Air Capture (DAC). What are the difficulties surrounding capturing CO2 from gas mixtures and what are the possible solutions that have been investigated? What type of negative emission technologies have been proposed, what are the important parameters for the design of carbon removal technologies and what are the biggest obstacles that need to be overcome? Gathering sufficient data and comparing the different finding will be of importance, given that DAC technologies are still in developing stages and information is limited. The focus of this thesis will be on chemically amine based solid sorbents, as this can be argued that this is the most promising technology for a viable DAC system.
The modelling part of the thesis will be focused on the design of and development of a DAC model in the open source software ’Python’. The amine based sorbent that is investigated and used for the creation of the model is Lewatit VP OC 1065. Using experimental data gathered from the literature together with feasible assumptions, a model will be built to recreate the the whole DAC process and analyse the system. The main focus will be on acquiring a flexible model for both the adsorption and desorption parts of the DAC process to make further investigation of the system parameters possible. This model will be used after this thesis for further development and, for instance, analysing different possible sorbents.
The modelling part of the thesis will be focused on the design of and development of a DAC model in the open source software ’Python’. The amine based sorbent that is investigated and used for the creation of the model is Lewatit VP OC 1065. Using experimental data gathered from the literature together with feasible assumptions, a model will be built to recreate the the whole DAC process and analyse the system. The main focus will be on acquiring a flexible model for both the adsorption and desorption parts of the DAC process to make further investigation of the system parameters possible. This model will be used after this thesis for further development and, for instance, analysing different possible sorbents.
Acid-gas capture systems are used to remove acid-gasses from waste gas streams from combustion or other chemical processes. A commonly used solvent is an aqueous solution with the primary amine monoethanolamine (MEA). Removing the acid gasses typically involves heating the solvent to approximately 378-383K in the stripping reactor. Using methyldiethanolamine (MDEA) instead of MEA can reduce the heating energy consumption of the stripper due to its lower reaction heat. The design of new reactors using aqueous MDEA solvents requires more data describing the properties of this solvent. Literature reporting thermophysical properties of aqueous MDEA solvents and transport properties of acid gasses in these solvents is exceedingly scarce. This work fills that information gap. Molecular dynamics were employed to compute density, viscosity and diffusivity of MDEA, CO2 and H2S in aqueous MDEA ranging in 0-50 wt% MDEA and 288-323K. The simulations were conducted using fully atomistic force fields for all species. The charges of MDEA were computed using Gaussian09 and scaled to achieve optimal agreement with experimental density and viscosity data. It has become clear that N-C-C-O dihedral in MDEA is crucial to reproduce experimental data of the viscosity and the diffusivity of MDEA. Two dihedrals were tested to achieve the best results. The resulting computed density, viscosity and diffusivity of MDEA are in good agreement with experimental data. The mixing rules between MDEA and CO2 were adjusted to increase accuracy of the prediction of diffusivity of CO2. The results are in good agreement with experimental data at 0-10wt% MDEA or 288K. The deviations become larger with higher wt% MDEA or higher temperatures.
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Acid-gas capture systems are used to remove acid-gasses from waste gas streams from combustion or other chemical processes. A commonly used solvent is an aqueous solution with the primary amine monoethanolamine (MEA). Removing the acid gasses typically involves heating the solvent to approximately 378-383K in the stripping reactor. Using methyldiethanolamine (MDEA) instead of MEA can reduce the heating energy consumption of the stripper due to its lower reaction heat. The design of new reactors using aqueous MDEA solvents requires more data describing the properties of this solvent. Literature reporting thermophysical properties of aqueous MDEA solvents and transport properties of acid gasses in these solvents is exceedingly scarce. This work fills that information gap. Molecular dynamics were employed to compute density, viscosity and diffusivity of MDEA, CO2 and H2S in aqueous MDEA ranging in 0-50 wt% MDEA and 288-323K. The simulations were conducted using fully atomistic force fields for all species. The charges of MDEA were computed using Gaussian09 and scaled to achieve optimal agreement with experimental density and viscosity data. It has become clear that N-C-C-O dihedral in MDEA is crucial to reproduce experimental data of the viscosity and the diffusivity of MDEA. Two dihedrals were tested to achieve the best results. The resulting computed density, viscosity and diffusivity of MDEA are in good agreement with experimental data. The mixing rules between MDEA and CO2 were adjusted to increase accuracy of the prediction of diffusivity of CO2. The results are in good agreement with experimental data at 0-10wt% MDEA or 288K. The deviations become larger with higher wt% MDEA or higher temperatures.
Molecular dynamics simulations of Krytox oil - CO2 gas mixture
Computation of transport and thermodynamic properties
The power plant, steel and petrochemical industries are large sources of carbon emissions worldwide. For meeting the climate goals of 2030 and 2050, efforts are underway towards pioneering novel technologies. Gas separation by supported liquid membranes is an attractive option for its energy-efficient, continuous and low cost of operation. The separation of gases takes via a solution diffusion mechanism where the gases are separated based on their
selectivity. Krytox oil is a high performance perfluoropolyether polymeric lubricant, originally aimed for its application to supersonic transport aircraft. The lubricant is known for its chemical and thermal stability leading to a longer usable life. The oil has shown an affinity for carbon dioxide (CO2) gas, a weak Lewis acid, owing to the fluorine and oxygen atoms in the polymeric oil which act as Lewis base and is thus envisioned for use in supported liquid membranes for gas separation.
Molecular dynamics simulations serve as a powerful tool of study, overcoming the shortcomings of experimental methods such as the difficulty of measurements at elevated temperatures, pressures or handling dangerous chemicals. This project covers the study of transport properties of Krytox oil namely the oil viscosity and diffusivity of CO2 in the oil for varying conditions of temperature, pressure and polymer chain length using equilibrium molecular dynamics simulations. The suitability of various atomistic force field models for this particular study has been tested, proceeding with the Universal force field (UFF) as the model of choice for studying the oil properties. To shorten the simulation time and study long time scales, coarse-grained simulations have been carried out using state-of-the-art MARTINI force field. In addition to transport properties, Henry’s constant for the solubility of CO2 in Krytox oil
has been predicted via alchemical free energy calculations by molecular dynamics simulations. ...
selectivity. Krytox oil is a high performance perfluoropolyether polymeric lubricant, originally aimed for its application to supersonic transport aircraft. The lubricant is known for its chemical and thermal stability leading to a longer usable life. The oil has shown an affinity for carbon dioxide (CO2) gas, a weak Lewis acid, owing to the fluorine and oxygen atoms in the polymeric oil which act as Lewis base and is thus envisioned for use in supported liquid membranes for gas separation.
Molecular dynamics simulations serve as a powerful tool of study, overcoming the shortcomings of experimental methods such as the difficulty of measurements at elevated temperatures, pressures or handling dangerous chemicals. This project covers the study of transport properties of Krytox oil namely the oil viscosity and diffusivity of CO2 in the oil for varying conditions of temperature, pressure and polymer chain length using equilibrium molecular dynamics simulations. The suitability of various atomistic force field models for this particular study has been tested, proceeding with the Universal force field (UFF) as the model of choice for studying the oil properties. To shorten the simulation time and study long time scales, coarse-grained simulations have been carried out using state-of-the-art MARTINI force field. In addition to transport properties, Henry’s constant for the solubility of CO2 in Krytox oil
has been predicted via alchemical free energy calculations by molecular dynamics simulations. ...
The power plant, steel and petrochemical industries are large sources of carbon emissions worldwide. For meeting the climate goals of 2030 and 2050, efforts are underway towards pioneering novel technologies. Gas separation by supported liquid membranes is an attractive option for its energy-efficient, continuous and low cost of operation. The separation of gases takes via a solution diffusion mechanism where the gases are separated based on their
selectivity. Krytox oil is a high performance perfluoropolyether polymeric lubricant, originally aimed for its application to supersonic transport aircraft. The lubricant is known for its chemical and thermal stability leading to a longer usable life. The oil has shown an affinity for carbon dioxide (CO2) gas, a weak Lewis acid, owing to the fluorine and oxygen atoms in the polymeric oil which act as Lewis base and is thus envisioned for use in supported liquid membranes for gas separation.
Molecular dynamics simulations serve as a powerful tool of study, overcoming the shortcomings of experimental methods such as the difficulty of measurements at elevated temperatures, pressures or handling dangerous chemicals. This project covers the study of transport properties of Krytox oil namely the oil viscosity and diffusivity of CO2 in the oil for varying conditions of temperature, pressure and polymer chain length using equilibrium molecular dynamics simulations. The suitability of various atomistic force field models for this particular study has been tested, proceeding with the Universal force field (UFF) as the model of choice for studying the oil properties. To shorten the simulation time and study long time scales, coarse-grained simulations have been carried out using state-of-the-art MARTINI force field. In addition to transport properties, Henry’s constant for the solubility of CO2 in Krytox oil
has been predicted via alchemical free energy calculations by molecular dynamics simulations.
selectivity. Krytox oil is a high performance perfluoropolyether polymeric lubricant, originally aimed for its application to supersonic transport aircraft. The lubricant is known for its chemical and thermal stability leading to a longer usable life. The oil has shown an affinity for carbon dioxide (CO2) gas, a weak Lewis acid, owing to the fluorine and oxygen atoms in the polymeric oil which act as Lewis base and is thus envisioned for use in supported liquid membranes for gas separation.
Molecular dynamics simulations serve as a powerful tool of study, overcoming the shortcomings of experimental methods such as the difficulty of measurements at elevated temperatures, pressures or handling dangerous chemicals. This project covers the study of transport properties of Krytox oil namely the oil viscosity and diffusivity of CO2 in the oil for varying conditions of temperature, pressure and polymer chain length using equilibrium molecular dynamics simulations. The suitability of various atomistic force field models for this particular study has been tested, proceeding with the Universal force field (UFF) as the model of choice for studying the oil properties. To shorten the simulation time and study long time scales, coarse-grained simulations have been carried out using state-of-the-art MARTINI force field. In addition to transport properties, Henry’s constant for the solubility of CO2 in Krytox oil
has been predicted via alchemical free energy calculations by molecular dynamics simulations.
The current industrial application of carbon capture utilization and storage (CCUS) is limited due to technological drawbacks such as high energy demand and environmental pollution. Ionic liquids (ILs) and deep eutectic solvents (DESs) are considered promising alternative solvents for the capture of carbon dioxide (CO2). DESs are often characterized by high viscosities, which hinders industrial application. This problem might be solved by mixing the DES with an organic solvent. This study aims to assess the DESs choline chloride-ethylene glycol (ethaline) and choline chloride-urea (reline) mixed with methanol and propylene carbonate (PC) for their suitability as a medium for the combined capture and electrochemical conversion of CO2. Molecular dynamics (MD) simulations are performed to obtain the densities, the viscosities, the self-diffusivities, the ionic conductivities and insight into the molecular interactions of these mixtures. Independent MD simulations are performed of these mixtures with low concentrations of the solutes CO2, oxalic acid and formic acid. Complementary studies within the Bio-cel project are conducted to characterize the solubility and electrochemical reaction of CO2 and the techno-economics.
The viscosities of the mixtures monotonically decrease for an increase of mole fraction of organic solvent, which is benign for the application of CCUS. The self-diffusivities of all constituents increase monotonically for an increase of mole fraction of organic solvent. The ionic conductivity is calculated based on the ion self-diffusivities. Ionic conductivity optima are found at a mole fraction of DES of approximately 0.6 for ethaline-PC and approximately 0.2 for ethaline-methanol and reline-methanol. For higher mole fractions of organic solvent, the ionic conductivity decreases due to a depletion of ions. Radial distribution functions (RDFs) are used to analyse the intermolecular interactions. RDF peaks between chloride-choline and chloride-ethylene glycol show an increase for an increasing mole fraction of organic solvent, which was unexpected. The numbers of hydrogen bonds decrease for addition of methanol to pure deep eutectic solvent. For addition of propylene carbonate, this decrease is less pronounced. The depletion of hydrogen bonds at low mole fractions of deep eutectic solvent is in correspondence with the decrease in viscosity and increase in self-diffusivities. The results indicate that, for the studied properties, deep eutectic solvents mixed with organic solvents are more favourable than pure deep eutectic solvents for the absorption and electrochemical conversion of CO2. ...
The viscosities of the mixtures monotonically decrease for an increase of mole fraction of organic solvent, which is benign for the application of CCUS. The self-diffusivities of all constituents increase monotonically for an increase of mole fraction of organic solvent. The ionic conductivity is calculated based on the ion self-diffusivities. Ionic conductivity optima are found at a mole fraction of DES of approximately 0.6 for ethaline-PC and approximately 0.2 for ethaline-methanol and reline-methanol. For higher mole fractions of organic solvent, the ionic conductivity decreases due to a depletion of ions. Radial distribution functions (RDFs) are used to analyse the intermolecular interactions. RDF peaks between chloride-choline and chloride-ethylene glycol show an increase for an increasing mole fraction of organic solvent, which was unexpected. The numbers of hydrogen bonds decrease for addition of methanol to pure deep eutectic solvent. For addition of propylene carbonate, this decrease is less pronounced. The depletion of hydrogen bonds at low mole fractions of deep eutectic solvent is in correspondence with the decrease in viscosity and increase in self-diffusivities. The results indicate that, for the studied properties, deep eutectic solvents mixed with organic solvents are more favourable than pure deep eutectic solvents for the absorption and electrochemical conversion of CO2. ...
The current industrial application of carbon capture utilization and storage (CCUS) is limited due to technological drawbacks such as high energy demand and environmental pollution. Ionic liquids (ILs) and deep eutectic solvents (DESs) are considered promising alternative solvents for the capture of carbon dioxide (CO2). DESs are often characterized by high viscosities, which hinders industrial application. This problem might be solved by mixing the DES with an organic solvent. This study aims to assess the DESs choline chloride-ethylene glycol (ethaline) and choline chloride-urea (reline) mixed with methanol and propylene carbonate (PC) for their suitability as a medium for the combined capture and electrochemical conversion of CO2. Molecular dynamics (MD) simulations are performed to obtain the densities, the viscosities, the self-diffusivities, the ionic conductivities and insight into the molecular interactions of these mixtures. Independent MD simulations are performed of these mixtures with low concentrations of the solutes CO2, oxalic acid and formic acid. Complementary studies within the Bio-cel project are conducted to characterize the solubility and electrochemical reaction of CO2 and the techno-economics.
The viscosities of the mixtures monotonically decrease for an increase of mole fraction of organic solvent, which is benign for the application of CCUS. The self-diffusivities of all constituents increase monotonically for an increase of mole fraction of organic solvent. The ionic conductivity is calculated based on the ion self-diffusivities. Ionic conductivity optima are found at a mole fraction of DES of approximately 0.6 for ethaline-PC and approximately 0.2 for ethaline-methanol and reline-methanol. For higher mole fractions of organic solvent, the ionic conductivity decreases due to a depletion of ions. Radial distribution functions (RDFs) are used to analyse the intermolecular interactions. RDF peaks between chloride-choline and chloride-ethylene glycol show an increase for an increasing mole fraction of organic solvent, which was unexpected. The numbers of hydrogen bonds decrease for addition of methanol to pure deep eutectic solvent. For addition of propylene carbonate, this decrease is less pronounced. The depletion of hydrogen bonds at low mole fractions of deep eutectic solvent is in correspondence with the decrease in viscosity and increase in self-diffusivities. The results indicate that, for the studied properties, deep eutectic solvents mixed with organic solvents are more favourable than pure deep eutectic solvents for the absorption and electrochemical conversion of CO2.
The viscosities of the mixtures monotonically decrease for an increase of mole fraction of organic solvent, which is benign for the application of CCUS. The self-diffusivities of all constituents increase monotonically for an increase of mole fraction of organic solvent. The ionic conductivity is calculated based on the ion self-diffusivities. Ionic conductivity optima are found at a mole fraction of DES of approximately 0.6 for ethaline-PC and approximately 0.2 for ethaline-methanol and reline-methanol. For higher mole fractions of organic solvent, the ionic conductivity decreases due to a depletion of ions. Radial distribution functions (RDFs) are used to analyse the intermolecular interactions. RDF peaks between chloride-choline and chloride-ethylene glycol show an increase for an increasing mole fraction of organic solvent, which was unexpected. The numbers of hydrogen bonds decrease for addition of methanol to pure deep eutectic solvent. For addition of propylene carbonate, this decrease is less pronounced. The depletion of hydrogen bonds at low mole fractions of deep eutectic solvent is in correspondence with the decrease in viscosity and increase in self-diffusivities. The results indicate that, for the studied properties, deep eutectic solvents mixed with organic solvents are more favourable than pure deep eutectic solvents for the absorption and electrochemical conversion of CO2.
Nitrogen oxides (NOx) are significant sources of air pollution. Nitrogen oxides like Nitric oxide (NO) and Nitrogen dioxide (NO2) are mainly responsible for the acid rain and smog. Nitrous oxide (N2O), also known as the laughing gas, is the major greenhouse gas that is responsible for the ozone layer's damage in the troposphere. According to the Environmental Protection Agency (EPA) report, one pound of N2O is 300 times more potent greenhouse gas than one pound of CO2. The significant emitters of Nitrogen oxides (NOx) are automobiles, agricultural sources, thermal power plants, and chemical processes like Nitric acid production plants, paint manufacturing, etc. This study mainly focuses on the tail gas emitted from the Nitric acid production facility. The tail gas emitted during the HNO3 production consists of almost 2% of O2, 200-400 ppm of NO2, and NO, whereas 800 ppm of N2O. As N2O is the most emitted gas from the Nitric acid production facility, it is followed by NO2 and NO, so it is essential to reduce these pollutants from the tail gas. Selective catalytic reduction (SCR) is a well-known technique currently involved in reducing NOx via the adsorption process from the Nitric acid production facility. But the costs involved in these methods are quite high. Nanoporous materials like zeolite exhibit uniform pore size and high thermal stability are said to be the promising adsorbents of NOx. The availability of a large number of zeolites makes it impossible to identify the proper zeolite for NOx adsorption experimentally. In such situations, molecular simulations are a powerful tool that can help identify the perfect zeolite. The time and cost involved in the process of molecular simulations are very low. In this work, Monte Carlo simulations involving reaction ensemble are implemented to obtain the equilibrium composition of NOx components at desired operating conditions in the Brick molecular simulation package. This is followed by Grand Canonical Monte Carlo simulations (GCMC) and Reactive Grand Canonical Monte Carlo simulations (RXMC-GCMC) for pure and quaternary NOx gas mixture adsorption in five different zeolites (FAU, FER, MOR, MFI, and TON) using simulation package RASPA. The composition results from the reaction ensemble are validated with the composition results obtained using the Gibbs minimization technique in the MATLAB model, and the results are in good agreement. The quaternary gas mixture adsorption results in five different frameworks from RXMC-GCMC simulations are then validated in Ideal adsorbed solution theory in the Python model, and the results are in good agreement at the given operating conditions. ...
Nitrogen oxides (NOx) are significant sources of air pollution. Nitrogen oxides like Nitric oxide (NO) and Nitrogen dioxide (NO2) are mainly responsible for the acid rain and smog. Nitrous oxide (N2O), also known as the laughing gas, is the major greenhouse gas that is responsible for the ozone layer's damage in the troposphere. According to the Environmental Protection Agency (EPA) report, one pound of N2O is 300 times more potent greenhouse gas than one pound of CO2. The significant emitters of Nitrogen oxides (NOx) are automobiles, agricultural sources, thermal power plants, and chemical processes like Nitric acid production plants, paint manufacturing, etc. This study mainly focuses on the tail gas emitted from the Nitric acid production facility. The tail gas emitted during the HNO3 production consists of almost 2% of O2, 200-400 ppm of NO2, and NO, whereas 800 ppm of N2O. As N2O is the most emitted gas from the Nitric acid production facility, it is followed by NO2 and NO, so it is essential to reduce these pollutants from the tail gas. Selective catalytic reduction (SCR) is a well-known technique currently involved in reducing NOx via the adsorption process from the Nitric acid production facility. But the costs involved in these methods are quite high. Nanoporous materials like zeolite exhibit uniform pore size and high thermal stability are said to be the promising adsorbents of NOx. The availability of a large number of zeolites makes it impossible to identify the proper zeolite for NOx adsorption experimentally. In such situations, molecular simulations are a powerful tool that can help identify the perfect zeolite. The time and cost involved in the process of molecular simulations are very low. In this work, Monte Carlo simulations involving reaction ensemble are implemented to obtain the equilibrium composition of NOx components at desired operating conditions in the Brick molecular simulation package. This is followed by Grand Canonical Monte Carlo simulations (GCMC) and Reactive Grand Canonical Monte Carlo simulations (RXMC-GCMC) for pure and quaternary NOx gas mixture adsorption in five different zeolites (FAU, FER, MOR, MFI, and TON) using simulation package RASPA. The composition results from the reaction ensemble are validated with the composition results obtained using the Gibbs minimization technique in the MATLAB model, and the results are in good agreement. The quaternary gas mixture adsorption results in five different frameworks from RXMC-GCMC simulations are then validated in Ideal adsorbed solution theory in the Python model, and the results are in good agreement at the given operating conditions.