Data-Driven Hybrid Approach Using Hyperparameter-Optimized Ensemble and Explainable Machine Learning for Assessing Pyrolysis Efficiency of Waste Tires
Bibliographic record
Abstract
Predicting pyrolysis oil yield from waste tires is a complex challenge due to the nonlinear interactions between feedstock composition and process parameters. Therefore, this study suggests the use of Decision Tree, Linear Regression, and XGBoost models to create an interpretable machine learning framework to estimate pyrolysis oil yield based on key features such as pyrolysis temperature, hydrogen, oxygen, nitrogen, volatile matter concentrations, and ash content. As a result, XGBoost outperformed the other models, with R 2 values of 0.965 (training) and 0.914 (testing), low root mean squared errors, and low mean absolute percentage errors. Furthermore, the Shapley Additive ExPlanations study showed that pyrolysis temperature and oxygen concentration were the most important factors. In contrast, Local Interpretable Model-Agnostic Explanations revealed that oxygen was the most important factor in individual forecast cases. A Monte Carlo simulation with 20,000 samples showed that the projected yield distribution had more than one mode, with pronounced peaks at 20, 35, and 48 wt %. Sobol sensitivity indices showed that hydrogen and pyrolysis temperature were the main factors affecting pyrolysis oil yield, followed by oxygen. Generally, this work offered a complete data-driven plan for predicting the efficiency of pyrolysis systems by combining accuracy, uncertainty quantification, and interpretability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".