Investigating thermodynamics and kinetics of hydrate phase change phenomena using experimental and machine learning tools
Creators: name: Acharya, Palash Vadiraj
Publisher: name: The University of Texas at Austin
Year: 2021
Subjects: Hydrates
Thermodynamics
Kinetics
Phase change
Machine learning
Experiments
Nucleation
Metal foam
Electronucleation
Electrode
Carbon capture and storage
Surface chemistry
Droplets
Hydrate phase equilibria
Thermodynamic activity
Decision Tree
Predictive modeling
Random Forest
Extra Trees
XGBoost
Amino acids
Techno-economic analysis
Steam methane reforming

description: Hydrates are ice-like crystalline solids which form under high pressure and low-temperature conditions from water (forming a cage of host molecules) and another liquid or gas (guest molecule). Hydrates can enable numerous industrial applications in the fields of carbon capture and sequestration (CCS), flow assurance, natural gas transportation/storage and desalination. A significant technological barrier to many hydrates-related applications is the slow rate of formation of hydrates, which is a result of thermodynamic and kinetics-related limitations. This dissertation investigates the role of electric field and surface chemistry in accelerating the nucleation kinetics of clathrate hydrates (CO₂, tetrahydrofuran). It also investigates the role of amino acids in inhibiting the nucleation kinetics and thermodynamics of CO₂ hydrate formation. It also evaluates the utility of machine learning models in predicting the thermodynamic formation conditions for gas hydrates. In addition to the focus on fundamental investigations, this dissertation also evaluates the utility of hydrates as a carbon capture tool when coupled with a steam reforming system to generate blue hydrogen from landfill gas. The content of the dissertation work is motivated by three objectives, as described ahead. Objective 1 investigates the influence of electric fields and surface chemistry on nucleation kinetics of hydrate formation for two kinds of hydrate forming systems (considered as two separate subtasks): miscible liquid-liquid systems (Tetrahydrofuran-water) and gas-liquid (CO₂-water) systems. As background, it is noted that the role of electric field has been widely studied for accelerating freezing of water. Subtask 1-1 investigates the role of electric field when used in conjunction with open-cell aluminum metal foam-based electrodes in accelerating the formation of THF hydrates. It is demonstrated that aluminum foam electrodes trigger near-instantaneous nucleation (in only tens of seconds) of THF hydrates at very low voltages (~20V). The promotion effect can be ascribed to two distinct interfacial mechanisms at play: namely, electrolytic bubble generation and the formation of metal ion complex-based coordination compounds. While THF hydrates form under atmospheric pressure, CO₂ gas hydrates form at much higher pressures and are therefore studied using a custom-built high-pressure cell. Subtask 1-2 highlights the role of aluminum in accelerating nucleation kinetics of CO₂ gas hydrates. Statistically meaningful measurements of induction times for CO₂ hydrate nucleation are undertaken using water droplets as individual microsystems for hydrate formation. The influence of various metal surfaces, droplet size, CO₂ dissolution time, and the presence of salts in water on nucleation kinetics have been characterized. It is observed that Al metal significantly accelerates the nucleation kinetics of CO₂ hydrates (the effect of which cannot be replicated by salts of Al) with nucleation initiating from the Al-water interface. Prediction of thermodynamic conditions of hydrate formation is critical to their synthesis and Objective 2 is centered around developing modeling and experimental tools for effective prediction of thermodynamic phase equilibria for hydrates. Subtask 2-1 demonstrates the utility of machine learning models to predict hydrate dissociation temperature (HDT) as a function of constituent hydrate precursors and salt inhibitors. Importantly, and in contrast to most previous studies, thermodynamic variables such as the activity-based contribution due to electrolytes, partial pressure of individual gases, and specific gravity of the overall mixture have been used as input features in the prediction algorithms. Using such features results in more physics-aware ML algorithms, which can capture the individual contributions of gases and electrolytes in a more fundamental manner. Three ML algorithms: Random Forest (RF), Extra Trees (ET), and Extreme Gradient Boosting (XGBoost) are trained and their performance is evaluated on an extensive experimental dataset comprising of more than 1800 experimental data points. The overall coefficient of determination (R²) is greater than 97% for all the three ML models with XGBoost exhibiting the best prediction performance with an R² metric of 99.56%. Subtask 2-2 investigates the role of amino acids on the kinetics and thermodynamics of CO₂ hydrate formation using droplet-based microsystems. Amino acids are environmentally friendly and inexpensive hydrate formation inhibitors. Nucleation kinetics as well as the depression in thermodynamic hydrate formation temperature for CO₂ hydrates in the presence of five amino acids containing non-polar side chains have been evaluated. All the amino acids inhibit nucleation with tryptophan exhibiting the slowest nucleation rate. Isoleucine exhibits the highest thermodynamic inhibition effect with the highest depression in freezing point temperature corresponding to 0.2 K for the concentrations studied in the present analysis. Landfills produce significant amounts of methane, which is a potent greenhouse gas. Steam reforming of the landfill gas generates CO₂ + H₂ as byproducts. The generated hydrogen can be used in refineries, to produce fertilizers or to produce electricity in a fuel cell. Objective 3 investigates the techno-economic factors associated with a facility coupling a sorption-enhanced steam methane reforming system with a hydrates-based capture system for landfills across Texas. The electrical energy requirements, water use, operating and capital costs required to set up and keep such a facility running have been evaluated in detail. The cost of producing hydrogen for all counties is about $0.5/kg of H₂ (excluding the cost for natural gas). The total carbon capture cost lies in the range of $96-$145/metric ton of CO2 with the lowest/highest cost corresponding to Harris/Brazoria county producing the highest/lowest amount of CO₂. A minimum cost of $0.9(2.4)/kg of H₂ would be required for Harris (Brazoria) county for a positive 30-year net present value; a 5-year payback period would require a minimum cost of $1.35(4.95)/kg of H₂. In summary, this dissertation significantly advances the current understanding of hydrate formation by introducing novel techniques (consuming ultra-low energy as well as passive tools) for enhancing hydrate formation kinetics. It also develops novel ML and experimental tools for predicting thermodynamic formation conditions of hydrates in the presence of various inhibitors. Finally, this work assesses the technical and economic viability of a hydrates-centered future for the natural gas industry.