Prediction of Equilibrium Conditions for Hydrate Formation in Binary Gaseous Systems Using Artificial Neural Networks
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Bibliographic record
Abstract
Abstract The present work attempts to indicate the potential of artificial neural networks (ANN) for the fast and reliable estimation of the equilibrium conditions of single, binary, and multiple hydrocarbon gas hydrates. The ANN used in this study was a network with the tangent‐sigmoid ( tansig ) propagation transfer function in the hidden layer and a final layer with the linear ( purelin ) transfer function. The number of hidden neurons has been determined by minimizing the error of the calculation. The obtained results and the ANN model reliability were compared with other predictive methods. Results showed that the ANN method is able to reliably predict the hydrate equilibrium conditions of hydrocarbons, particularly for binary gas systems, using simple input parameters such as the weight fractions and normal boiling points of the components.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it