Machine learning techniques for the prediction of polymerization kinetics and polymer properties
Bibliographic record
Abstract
Abstract In the current study, the ability of two data‐driven machine learning tools, the extreme learning machine (ELM) and the adaptive neuro‐fuzzy inference system (ANFIS), to predict the polymerization rate and melt flow index of linear low‐density polyethylene produced in a gas phase process was investigated. The level of interaction between the input variables (ethylene, 1‐butene, isopentane pressures, and reaction temperature) on the outputs (melt flow index and activity) was also examined. It was found that both outputs are impacted by the presence of isopentane as an induced condensing agent. Various statistical indicators, including the coefficient of correlation ( R 2 ) and root mean square error (RMSE), were used to quantitatively evaluate both developed models. The ANFIS model outperformed the developed ELM model in terms of predicting the MFI and the catalyst activity. A sensitivity analysis of the ANFIS and ELM models showed that all the input variables under investigation had a sizable impact on the responses and none of them could have been discarded. The present study showed that machine learning tools could be employed to adequately develop empirical models to predict polymerization kinetics as well as the final polymer properties.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".