Prediction of <scp>BP</scp> neural network and preliminary application for suppression of low‐temperature oxidation of coal stockpiles by pulverized coal covering
Bibliographic record
Abstract
Abstract We have developed a new method to suppress spontaneous combustion of coal piles by covering the surface of coal piles with pulverized coal. Experimental studies of three type of coal samples from China (YJL, CYW, and SW) with particle size ratio of 10:1 were performed to investigate the low‐temperature oxidation of coal pillars. In this work, we have also demonstrated that the distributions of oxygen concentration, the temperature field, as well as the spontaneous combustion of three typical Chinese coal samples can be predicted accurately using back‐propagation neural network (BPNN) by MATLAB. Pearson correlation analysis showed that temperature and oxygen concentration highly depend on the ratio of pulverized coal thickness to coal piles thickness, activation energy, void ratio, wind speed, and low‐temperature oxidation time. Three‐layer BPNN models with five input factors were developed to predict the low‐temperature oxidation process under pulverized coal. The prediction data of BPNN are fitting better with our experimental data, which confirms that BPNN modelling can accurately predict the low temperature oxidation process of coal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".