Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling
Bibliographic record
Abstract
Abstract Asphaltene precipitation causes different problems in the oil industry. In this study, a large data bank was used to model asphaltene precipitation titration data as a function of temperature, type of solvent, and solvent to crude oil dilution ratio. Three multilayer perceptron (MLP) neural networks and three radial basis function (RBF) neural networks were developed to estimate asphaltene precipitation data. The MLP models were optimized with scaled conjugate gradient (SCG), Levenberg‐Marquardt (LM), and Bayesian regularization (BR) algorithms and the RBF models were optimized with the particle swarm optimization (PSO) algorithm, imperialist competitive algorithm (ICA), and genetic algorithm (GA). All of the proposed models show an acceptable degree of accuracy and have an average absolute percent relative error (AAPRE) less than 4 %. Afterwards, three of the best models including MLP‐LM, MLP‐BR, and RBF‐GA were combined and a committee machine intelligent system (CMIS) was designed, which offers a higher accuracy compared to the other intelligent models. Aside from these intelligent models, the group method of data handling (GMDH) was used to develop an explicit and simple expression for estimating asphaltene precipitation. The proposed CMIS and GMDH models were compared to models developed based on scaling theory and the results show that the proposed models in this study outperform pre‐existing models. The validity and accuracy of the CMIS and GMDH models were proven by statistical and graphical techniques. Finally, a sensitivity analysis suggested that the solvent to crude oil dilution ratio has the largest effect on asphaltene precipitation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".