Forecasting of industrial coke quality at JSC EVRAZ NTMK based on data of passive industrial experiment. Report 1. Forecasting of CSR and CRI of industrial coke
Bibliographic record
Abstract
A forecast of coke quality takes a special place in the coke production, since it enables to increase efficiency of management of batching process of various by composition and properties coals and production of coke of stably high and required quality with minimal costs. Description of a methodological approach to processing of passive industrial experiment data of blends coking at Coke production department of JSC EVRAZ NTMK presented by application selective (general) matrix. The matrix accounts various multilevel values of influence factor CSR and CRI of coke - a complex index of coking ability of blends K.п.к.Vo. It was shown that the proposed approach provides wide variations of response function (CSR/CRI) at symmetrical enough matrix, excluding predominance of any particular area of values of indices K.п.к.Vo and CSR/CRI. By applying the passive industrial experiment, based on processing of actual report data of industrial blends coking at the coke batteries No. 5-6 (wet quenching) and No. 9-10 (dry quenching) by selective matrix, mathematical models of forecast of quality of industrial coke by wet and dry quenching (CSR/CRI) were elaborated depending on coal charges properties (K.п.к.Vo) at the existing modes of their preparation and coking at the coke batteries No. 5-6 and 9-10. Verification of accuracy of the mathematical models of coke quality forecast at wet and dry quenching (CSR/CRI) processing a large actual material of industrial coking (62 coking operations in the coke batteries No. 5-6 and 58 coking operations in the coke batteries No. 9-10 showed accuracy good enough for practical application of forecasting indices CSR and CRI of industrial coke of wet and dry quenching.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".