Predicting three phase (hydrate–liquid–vapour) equilibria of mixed hydrates in guest gas swapping: <scp>AI</scp>‐based approach versus physical modelling
Bibliographic record
Abstract
Abstract Prior to investigating the guest gas replacement characteristics, the estimation of equilibrium condition for the coexisting hydrate–liquid–vapour (HLV) phases is crucial. For this, there are various studies which have reported the physical thermodynamic model for equilibrium estimation. In this contribution, a data‐driven formulation is developed as an alternative approach within the framework of artificial intelligence (AI) to predict the three‐phase equilibrium of binary and ternary mixed hydrates associated with guest swapping at diverse geological conditions. For this, we use the experimental data sets related to guest (pure and mixed CO2) replacement in hydrate structures with and without salts (i.e., single and multiple salts of NaCl, KCl, and CaCl2). Various training algorithms, namely Levenberg–Marquardt (LM), scaled conjugate gradient (SCG), Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi‐Newton, and Bayesian regularization (BR), are employed to formulate the artificial neural network (ANN) model. Performing a systematic comparison between them, we select the best option suited for the hydrate system. The best performing ANN model is compared with an existing physical thermodynamic model for predicting the equilibrium condition in pure water. It is observed that the ANN (BR) model consistently secures the lower percent absolute average relative deviation (i.e., %AARD <2%) than the latest physical model. Finally, the developed AI model is extended to predict the three‐phase HLV equilibrium in presence of salt solutions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".