High Temperature Softening and Melting Interactions Between Newman Blend Lump and Sinter
Bibliographic record
Abstract
In this work, the softening and melting (S&M) behaviour and whole blast furnace (BF) performance of Newman Blend Lump (NBLL), plant sinter, and sinter-NBLL mixture were studied using S&M under load test and numerical BF modelling. Both physical and chemical interactions between sinter and lump were confirmed in the S&M process. Significant improvements were found in the S&M behaviour of the sinter-NBLL mixture because of the physical and chemical interaction. The physical interaction was examined using X-ray/Neutron Computed Tomography (CT) scanning on the samples from interrupted S&M under load tests. The void fraction in the ferrous layer of the sinter-NBLL mixture was found to be similar to the sinter and was higher than that for NBLL. The chemical interaction was investigated by analysing the Ca transfer from sinter to NBLL, which indicated that Ca transfer started around 1200°C in the S&M process. FactSage was used to assist in the interpretation of the S&M test results. It was found that the NBLL sample starts to melt at a lower temperature compared to other burdens used in the present study, which also agreed well with the CT scan results. The whole BF performance of different ferrous burdens was studied using the experimental results as inputs. The sinter-NBLL mixture behaved more like the sinter than the NBLL; compared with the sinter only burden with the same total basicity, the sinter-NBLL combination formed a more permeable CZ, had a lower total BF pressure drop, and a higher gas utilization rate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".