Improving the estimation accuracy of titration-based asphaltene precipitation through power-law committee machine (PLCM) model with alternating conditional expectation (ACE) and support vector regression (SVR) elements
Bibliographic record
Abstract
Asphaltene precipitation/deposition have been longstanding issues in petroleum industry which lead to decline in oil production and economical efficiency. Owing to severe undesirable issues associated with this phenomenon, it is crucial to develop a reliable, accurate, and robust approach for quantitative estimation of asphaltene precipitation. In the first section of this paper, amount of asphaltene precipitation from stock tank oil through titration process was estimated using two predictive methods of Support Vector Regression (SVR) as well as Alternating Conditional Expectation (ACE). A novel predictive method, the so-called Power-Law Committee Machine (PLCM) with constituents of SVR and ACE, was then employed for estimation of the amount of asphaltene precipitation. PLCM model assigns weight factors to each individual sub-model of SVR and ACE to specify the contribution of each particular model in the overall prediction of asphaltene precipitation. Optimal values of these weight factors were extracted by means of Genetic Algorithm (GA) since it was already inserted as the combiner in the structure of the PLCM model. To validate this predictive tool, experimental data collected from open source literature were compared against the model predictions. It was observed that PLCM model can estimate the amount of asphaltene precipitation with very high accuracy and it had more satisfactory prediction performance compared to the other models of SVR and ACE.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".