Gas Hydrate Dissociation Temperature Prediction in Porous Media: Effects of Pore Size and Modeling Approach
Bibliographic record
Abstract
Understanding the dissociation behavior of gas hydrates in confined porous media is crucial for assessing their stability and potential applications in energy storage, carbon capture, and climate modeling. In this study, we develop two distinct approaches to predict the equilibrium dissociation temperature of gas hydrates in porous materials with varying pore sizes: a thermodynamic model based on the activity approach and a suite of machine learning (ML) models. The thermodynamic model explicitly accounts for the effects of confinement on hydrate phase stability and was validated using an unfiltered data set for methane (CH 4 ) and propane (C 3 H 8 ) hydrates, achieving low average absolute deviations (AAD%) of 0.17 and 0.62%, respectively. To complement and generalize these predictions, we trained four ML models: Decision Tree, Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron. These models used input features such as pore diameter, system pressure, and critical gas properties. A group-based data splitting strategy was applied, with propane data exclusively reserved for testing to assess true generalization. The SVM model exhibited the highest predictive performance on unseen data, with an AAD% of 0.52%. To enhance interpretability, SHapley Additive exPlanations (SHAP) analysis was employed. The results confirmed alignment between the ML model’s decision logic and known physical principles and identified critical temperature, pressure, and pore size as the most influential features. While group-based splitting improved robustness, discrete SHAP patterns suggest that a broader variety of gases in the training data could further enhance generalizability. Overall, this integration of physics-based and data-driven modeling provides accurate and interpretable predictions of hydrate dissociation behavior in porous systems, supporting future developments in both geological and industrial applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".