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Gas hydrate formation and dissociation: predictive, thermodynamic, and dynamic models
(University of Waterloo, 2025-06-27) Hosseini, Mostafa
Gas hydrates are a type of crystalline compound consisting of water and small gas molecules. A wide range of applications of gas hydrates in storing natural gas in the form of artificially created solid hydrates, known as solidified natural gas technology, gas separation processes, and seawater desalination technology, has attracted great interest in scientific and practical studies. Gas hydrate formation may also cause deleterious effects, such as blockage of gas pipelines. Therefore, accurate prediction of equilibrium conditions for gas hydrates is of great interest. In this regard, machine learning-based models were proposed to predict methane-hydrate formation temperature for a wide range of brines. A comprehensive database including 987 data samples covering 15 different brines was gathered from the literature. After data cleaning and preparation, three different models, namely multilayer perceptron (MLP), decision tree (DT), and extremely randomized trees (ET), were trained and tested. The ET model achieved the best performance with a root mean squared error (RMSE) of 0.6248 K for the testing dataset. Moreover, in an additional independent testing with MgBr2 samples, ET achieved an RMSE of 0.3520 K, confirming its strong generalization ability. The order of model accuracy was ET greater than MLP greater than DT. Compared to previous studies, the developed models achieved similar or better accuracy while covering a wider range of brine types. The findings of this study can be used as a reliable tool to predict methane-hydrate formation PT curves for pure water, single-salt brines, and multi-salt brines.
The research further focuses on improving the prediction of equilibrium conditions in methane hydrate systems by incorporating diverse water-soluble hydrate formers and applying advanced machine learning techniques. Methane hydrates, which naturally form under high pressure and low temperature, can be more efficiently formed or dissociated by altering thermodynamic conditions using these hydrate formers. Accurate prediction of these conditions is crucial for optimizing gas storage and energy applications. Molecular descriptors and operational parameters, such as mole fraction and pressure, were used as input variables to predict equilibrium temperature. Machine learning methods, including Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP), were employed, using a novel former-based data-splitting approach rather than traditional sample-based methods. The RF model achieved the best results, with R2 = 0.930, RMSE = 1.71, and AARD = 0.48%. Feature selection, preprocessing, and Shapley Additive Explanations (SHAP) provided valuable insights into variable importance. Additional findings from the reduced model revealed that even less influential features significantly impacted distance-based models such as SVM and MLP. Interaction analysis through SHAP dependency plots highlighted the critical interplay between polar surface area and rotatable bonds in hydrate formation conditions. This work advances methane hydrate research by offering a more accurate and interpretable framework for predicting hydrate equilibrium, addressing key gaps in previous studies, and extending its applicability to a broader range of systems. Moreover, the introduction of a former-based data-splitting method improves generalization across different hydrate formers, while the use of SHAP values for model interpretability offers deeper insights into the relationships between molecular descriptors and hydrate equilibrium conditions. This study paves the way for improved selection of hydrate formers in hydrate systems.
In addition to the phase equilibrium studies, this research also addresses the behavior of gas hydrates under confinement, focusing on hydrate dissociation in porous media. Understanding the dissociation behavior of gas hydrates in confined porous media is crucial for evaluating their stability and potential applications in energy storage, carbon capture, and climate modeling. Two distinct approaches were developed, namely a thermodynamic activity model and machine learning (ML) models, to predict equilibrium dissociation temperatures of gas hydrates in porous media of varying pore sizes. The activity model accounted for capillary effects and surface interactions and was validated against an unfiltered experimental dataset. For CH4 hydrates, the model achieved an AAD% of 0.17%, and for C3H8 hydrates, an AAD% of 0.62%. Complementary machine learning models (DT, RF, SVM, MLP) were trained using pore diameter, pressure, and gas critical properties as features. Group-based data splitting, with propane data reserved for testing, ensured robust evaluation. Among ML models, the SVM achieved the best predictive performance with an AAD% of 0.52%. SHAP analysis revealed that critical temperature, system pressure, and pore size were dominant predictors. The study also noted that experimental scatter was linked to pore structure variability and procedural differences, with larger pores showing convergence to bulk hydrate behavior. The combined modeling framework effectively captures hydrate behavior across a wide range of confined conditions, offering valuable predictive capability for both industrial and geological hydrate systems. In conclusion, the integration of physics-based and data-driven modeling enables accurate prediction of hydrate dissociation temperatures across a range of porous media. These findings support the development of predictive tools for hydrate systems in both geological and industrial applications.
Finally, to complement the thermodynamic and equilibrium predictions, the dynamic transport behavior of hydrate particles in pipelines was investigated through CFD–DEM simulations. The dynamic behavior of hydrate particles suspended in water-dominated horizontal pipe flow using a two-way coupled CFD–DEM framework based on OpenFOAM and LIGGGHTS via CFDEM® coupling was explored. Multiphase flow simulations were conducted across inlet velocities of 0.2, 0.5, and 0.8 m/s and hydrate volume fractions of 2%, 5%, 8%, 15%, and 20%. Pressure drop behavior was quantified by extracting pressure gradients between two axial positions (z = 0.10 m and z = 0.49 m) early in the simulation. Results indicated that pressure drop increases with hydrate volume fraction at all flow velocities, with clustering phenomena becoming more prominent at higher solid loadings. Cross-sectional velocity profiles visualized the early evolution of particle clustering, wall interactions, and domain depletion. Increased flow velocity enhanced particle suspension but reduced domain uniformity over time. Time-resolved analyses of pressure drop, drag force, particle velocity, interparticle forces, and radial migration were conducted to explore flow regime transitions and mechanical resistance. Early clustering near the pipe walls was observed under dense flow conditions, driven by cohesive and frictional forces, leading to partial stratification and localized energy dissipation. The study highlights the importance of considering early-time flow dynamics, where suspension quality and transport resistance are most sensitive to hydrate loading. These findings contribute to a deeper understanding of hydrate slurry transport in multiphase pipeline systems and offer practical guidance for improving flow assurance models and mitigation strategies in subsea energy operations.
Contextual and Individual Factors Associated with the Interpretation and Usage of Prosocial Lies
(University of Waterloo, 2025-06-26) Ong, Shirley
Lying is a complex and multifaceted aspect of human communication, often viewed as a moral or social transgression. Growing up, children are instilled with the message that it is important to be honest. However, not all lies are told for malicious purposes, and there are situations where telling a lie may be socially appropriate and provide beneficial for the recipient. Prosocial lying is defined as a type of lie that is socially beneficial and enhances the quality of social interactions by minimizing harm to others. Within everyday exchanges both children and adults use prosocial lies on a frequent basis. While there may be social benefits to using prosocial lies, overuse or inappropriate use has negative outcomes. Thus, it is important for children to know how and when prosocial lying is more/less appropriate. My doctoral dissertation examines how contextual and individual factors relate to children’s reasoning about prosocial lies. I examined this for both children’s perceptions of lies, namely, how children felt the emotions of both the lie-teller and the lie-listener may be affected by the lie, as well as children’s endorsement of lies (versus truth), that is, how likely they themselves would be to use lies in varying contexts. There were two contextual factors manipulated with my work. First, I examined the listener’s knowledge of the situation, thereby allowing my work to build upon the rich body of work that has examined children’s sensitivity to and use of others’ knowledge to guide their communication. Second, I explored the role of statement content (i.e., whether there was a reference to an opinion or to reality), building on past work that has shown children’s sensitivity to the moral weight of lies based on content. With respect to individual differences, I focused on the role of empathy in relation to perceptions/endorsement of prosocial lies, exploring whether increased sensitivity to others’ emotional states (i.e., empathy) was associated with children’s perception/endorsement of prosocial lying within certain contexts. My central focus was on the performance of school-aged children, a developmental stage chosen as children in this age range would both understand the function of prosocial lies generally and show sensitivity to the content of lies. However, to assist with understanding developmental shifts in performance, I also assessed how adults would respond on similar tasks.
Two studies were conducted with different groups of children and adults. Study 1 focused on children’s (8–11 years, N=80) and adults’ (N=192) perceptions of the emotional impact of prosocial lies (and truths) for both lie-tellers (i.e. speakers) and listeners. Participants read/heard a series of eight vignettes describing a negative event wherein a speaker says either a truth/lie (referring to their opinion or reality) to a listener who was/was not aware of the negative event. Before and after the statement was uttered, participants rated the emotions of both characters. Results demonstrated that the statement content did not affect children’s or adults’ perceptions of listener/speaker emotions. Both children and adults perceived that listeners would feel better after hearing a prosocial lie regardless of their knowledge state, suggesting that there may be a social benefit even when a prosocial lie is unlikely to deceive. However, following a lie, when listeners were unaware of the negative event (versus aware), their emotions were rated as more positive, suggesting that participants were tracking the listener’s knowledge state and using this to gauge emotional outcomes. Children with higher empathy showed better accuracy in detecting lies (when told to ignorant listeners) and adults with better empathy perceived knowledgeable listeners as feeling worse following a prosocial lie. Study 2 focused on children’s (8–11 years, N=81) and adults’ (N=218 endorsement of prosocial lies. Participants were asked to imagine themselves in scenarios involving a negative event that another person either knew or did not know about. They then rated how likely they would be to use the truth/lie statements which varied in content (referring to opinion or reality). Results demonstrated that while children endorsed statements similarly for ignorant/knowledgeable listeners, adults endorsed a greater likelihood of using a prosocial lie when the listener was ignorant of the negative event. Both age-groups indicated higher likelihood of telling a prosocial lie about an opinion versus reality. Empathy was not associated with children’s responses but was associated with adults’ communicative choices.
Across the two studies, findings provide insight into how children (and adults) incorporate information about listener knowledge and statement content into their appreciation of prosocial lies. Findings also highlight the differing role of empathy throughout development within the context of these studies. My results have theoretical implications for children’s communicative development and practical considerations for prosocial lying in general.
Mathematical modeling of whole-body electrolyte homeostasis
(University of Waterloo, 2025-06-24) Stadt, Melissa
Electrolyte balance is crucial for many physiological processes, including cellular signaling, muscle contractions, membrane potentials, hormonal secretion, and bone structure. Disruptions to electrolyte balance, arising from disease, diet, or drugs can have severe health consequences, such as muscle weakness, bone fragility, and life-threatening cardiac arrythmias. Therefore, a comprehensive understanding of these regulatory systems and how they may be disrupted is important for developing effective preventative and therapeutic strategies. Mathematical modeling provides a powerful tool for investigating these systems through simulations and analysis. In this thesis, we present the development and analysis of mathematical models focused on the regulation of key electrolytes, potassium and calcium.
For potassium homeostasis, we first developed a detailed, whole-body model incorporating known regulatory mechanisms. We conducted model simulations to quantify the individual contributions of these regulatory mechanisms on long-term potassium balance and responses to a meal. Additionally, we conducted sensitivity analyses to understand how parameter variations impact potassium levels in the extracellular and intracellular fluid. Furthermore, we integrated recent experimental data on renal adaptations to high potassium intake to analyze these findings from a whole-body perspective.
For calcium homeostasis, we developed mathematical models representing a male, female, late pregnant, and lactating rat to quantify sex-specific differences and maternal adaptations in calcium regulation. These models synthesized literature data to identify key mechanisms that enable females to meet the high calcium demands of pregnancy and lactation. Finally, we developed an integrated model that represents the renin-angiotensin system, calcium regulation, and bone remodeling to investigate the impact of estrogen deficiency in post-menopausal women and common antihypertensive treatments on bone density and calcium regulation.
The research provided in this thesis contributes frameworks for understanding electrolyte homeostasis and predicting the impacts of physiological changes and pharmacological interventions on electrolyte and bone homeostasis.
Role of Social Capital and Relational Well-being in Shaping the Community Level Responses to Tropical Cyclones among the Small-Scale Fisheries Communities in Chilika Lagoon, India
(University of Waterloo, 2025-06-24) PRAKASH SHERLY, GREESHMA
Small scale fisheries (SSFs) are more vulnerable to calamities brought on by natural hazards, changing climatic conditions, and climate change due to their proximity to the seashore. Dealing with these challenges is an added burden to already existing vulnerabilities, injustice and marginalization faced by them. The Indian subcontinent with a vast coastline extending up to 7516 kms (about 4670.23 mi), is vulnerable to world’s 10% tropical cyclones, especially in the places adjacent to Bay of Bengal (BoB). Asia’s largest and world’s second largest brackish water lagoon, adjacent to BoB - Chilika lagoon, situated in Odisha state of India is extremely prone to catastrophic events, causing around 5-6 cyclones hitting the coast annually. SSFs who depend on the lagoon for their livelihood are on the forefront suffering from the repercussions of cyclonic activities. While resilience against events like cyclones is usually analyzed in terms of economic and infrastructure aspects, there is a lack of focus on the intrinsic material aspects contributing to community resilience in the face of climate related disasters. This research fills this gap by analyzing the community resilience of SSF’s in Chilika Lagoon through the lens of social capital and relational well-being. Social capital measures the different links or connections a community has within and outside of their network that helps them build effective response strategies through collective action at the time of crisis. Communities with high social capital can bring community members together for better preparedness, emergency support, response, and recovery efforts. Nevertheless, it is not the existence of all these linkages that matters, but the quality and balance of all these ties are imperative. For instance, the effectiveness of these could be hindered in a community level resilience if it lacks the ability to address the power imbalance, social inequality, and trust.
Thus, relational well-being measures the quality of various networks through characteristics such as trust, reciprocity, support, and network dynamics which create a sense of motivation to work collectively. The study employs a qualitative case study approach and multiple data collection tools such as semi-structured interviews, non-participant observations, and focus group discussions. The key findings present the various challenges faced by the communities in various systems like social, economic, environmental and physical and their interconnectedness, role of social capital and relational well-being in the various community level response to deal with the crises, the lack thereof due to power imbalance, social inequality, caste system and political power and finally providing recommendations to ensure tailored context specific approaches to enhance the community resilience against disasters like tropical cyclone in the future.
Teenage Feminists: High School Students and the Women's Movement in Ontario, 1968-1980
(University of Waterloo, 2025-06-24) Blair, Megan
This thesis examines the involvement of teenage girls in the upsurge of feminist activism between 1968 and 1980. Young women across Ontario engaged in feminism in a variety of ways; some joined high school women’s liberation groups in their communities or started their own feminist groups in their schools, while others reflected a more intimate and subtle feminism, challenging gender discrimination in their everyday lives. Using an age-focused analysis, this thesis argues that teenage girls partook in everyday feminism. Everyday feminism entails the recognition of discrimination and inequalities in intimate, everyday spaces, and the words, actions, and thoughts that challenged these inequalities.
By broadening the definition of feminism beyond adult-oriented women’s groups and issues, this thesis captures the feminist actions of young women. Spaces and issues that mattered to young women such as school, sports, sex education, and fashion were all ways that teenage girls negotiated with discrimination and feminism. While young women sometimes collaborated with adults, at other times their efforts were more individualized, spontaneous, or collaborative with their peers. This thesis contributes to a more complex and varied history of feminism in Canada by taking seriously the issues that mattered to young women and recognizing the reality of their involvement in feminist action.