DT-MNLR: a novel hybrid machine learning framework for precise coke strength and reactivity prediction
Bibliographic record
Abstract
Accurately predicting coke strength after reaction (CSR) and coke reactivity index (CRI) is important for optimising coke quality in metallurgical industry, thereby minimising production costs and maximising resource utilisation. This study introduces a novel machine-learning model, the decision tree multi-output non-linear regression (DT-MNLR) model, for accurately predicting both CSR and CRI. The DT-MNLR model leverages the strengths of multiple algorithms: decision trees for efficient coal blend classification, multi-output regression for handling the interrelated nature of CSR and CRI, and a backpropagation neural network for capturing complex non-linear relationships within the data. Recognising the intricate interactions among coal properties that significantly impact coke quality, the model incorporates high-level polynomial features and additional coal property variables, enhancing its predictive accuracy. Rigorous validation using diverse testing samples demonstrates the DT-MNLR model's superior performance across a wide range of CSR and CRI values. Comparative analysis against other machine-learning methods showcases the DT-MNLR model's advantages, including lower prediction errors, improved generalisation to unseen data and enhanced robustness in handling outliers. This research significantly advances the field of coke quality prediction by establishing the DT-MNLR model as a powerful tool for coal blend analysis and quality control. The model's effectiveness paves the way for significant advancements in intelligent systems for industrial applications, promoting optimal resource utilisation and process efficiency.
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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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".