Effect of oxyfuel burner ratio changes on energy efficiency in electric arc furnace at Co-Steel Lasco
Bibliographic record
Abstract
Trials were conducted on Co-Steel Lasco's electric arc furnace (EAF) to evaluate the effect of oxyfuel burner ratio changes on furnace energy efficiency and productivity. Carefully controlled trials with the collection of numerous process and sample data over 331 heats provided statistically significant results. The oxygen available for post-combustion in the furnace was increased by 19%. As the burners were operating at their maximum oxygen constraint, this was achieved by decreasing the natural gas consumption by 43%. Results of this study indicate that decreases in terms of specific electrical energy consumption (4·0%), power on time (5·0%), and tap to tap time (4·5%) were realised. Slag chemistry, electrode consumption, and yield were not affected. Measurement data support the finding that more heat was transferred into the steel with the post-combustion burners: a flat bath was achieved earlier; high bath temperatures were reached more quickly; power on time was reduced, leading to reduced electrical energy consumption. Analysis suggests that furnace thermal energy losses were reduced by less air inleakage, less incomplete methane combustion, and earlier achievement of foamy slag conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".