Prediction of thermal conversion characteristics for co‐combustion of waste tire–lignite coal using machine learning algorithms
Bibliographic record
Abstract
Abstract Co‐combustion of coal with various waste resources is an effective energy recovery strategy that integrates waste‐derived fuels while reducing dependence on fossil fuels. In this study, machine learning algorithms were used to predict thermogravimetric data for the co‐combustion process of waste tire (WT) and lignite coal (LC) blends to improve the understanding of the thermal conversion characteristics. The study analyzed the combustion behaviour of WT, LC, and their mixtures at four different heating rates (10, 20, 30, and 40°C/min) and various mixing ratios (100:0, 20:80, 40:60, 50:50, 60:40, 80:20, and 0:100) using thermogravimetry–derivative thermogravimetry/differential scanning calorimetry (TG‐DTG/DSC) techniques. To improve the prediction accuracy, eight machine learning algorithms—adaptive boosting regression, decision tree regression, k‐nearest neighbour regression, linear regression, multi‐layer perceptron, random forest regression, support vector machine regression, and XGBoost—were applied to model the co‐combustion process. The results showed a strong correlation between experimental data and machine learning predictions, confirming the effectiveness of these models. By enabling accurate real‐time prediction of thermal conversion characteristics, this study reduces the reliance on labour‐intensive thermogravimetric analysis (TGA) and facilitates cost‐effective, adaptive, and scalable optimization of combustion processes for industrial applications.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".