FORECASTING OF INDUSTRIAL COKE QUALITY AT JSC EVRAZ NTMK BASED ON DATA OF PASSIVE INDUSTRIAL EXPERIMENT
Bibliographic record
Abstract
A methodological approach to the processing of data of a passive industrial experiment on charge coking at the coking plant of EVRAZ NTMK JSC using a sample (general) matrix, rather than the entire data array, which takes into account various multilevel values of the factor of influence on M25 and M10 of coke ‒ a complex indicator of charge coking capacity (CCIVo) is presented, which provides sufficiently wide changes in the response functions (M25/M10) with a symmetric matrix, excluding the predominance of any one range of values of the CCIVo and М25/М10 indices. On the basis of the proposed approach to the processing of the actual reporting data of industrial coking of charges on batteries No. 5‒6 (wet quenching) and No. 9‒10 (dry quenching) (passive industrial experiment), adequate mathematical models were obtained for predicting the quality of industrial coke for wet and dry quenching (M25/M10) on the properties of coal charges (CCIVo) under the existing modes of their preparation and coking in batteries No. 5‒6 and No. 9‒10. Verification of the accuracy of the obtained mathematical models for predicting the quality of wet and dry quenched coke (M25/M10) on a large actual material of industrial coking in batteries No. 5‒6 (62 coking operations) and No. 9‒10 (58 coking operations) showed high enough for practical use accuracy of the forecast of indicators M25 and M10 of industrial coke of wet and dry quenching.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".