Research on the Optimization of the Impact of Flame Cutting Costs of Continuous Casting Billets
Bibliographic record
Abstract
本文针对马鞍山钢铁公司长材2#连铸机火焰切割燃气成本较高的问题，通过设备改造与工艺优化实现降本增效。研究分析了燃气单耗的影响因素，提出基于拉瓦尔管割嘴的优化方案。通过改造长明火供气系统及采用收缩扩张结构的拉瓦尔管割嘴，提升燃气燃烧效率与气流速度(超音速)，并结合切割速度优化实验，实现燃气单耗降至0.11 kg/t。实验表明，改造后切割时间缩短13~19秒，表面粗糙度(Ra)由原46.5 μm降至8.7 μm，且挂渣程度显著改善，并通过t检验验证了改进效果的显著性。研究为连铸坯火焰切割的精细化成本控制提供了理论与实践依据，具有工业推广价值。This study addresses the issue of high gas consumption in flame cutting at Ma’anshan Iron and Steel Co. Ltd.’s long-materials No. 2 Continuous Casting Machine by implementing equipment modifications and process optimizations to achieve cost reduction and efficiency improvement. Through analysis of factors affecting gas consumption, an optimization plan based on a Laval nozzle was proposed. By modifying the pilot flame gas supply system and adopting a converging-diverging Laval nozzle, gas combustion efficiency and gas flow velocity (supersonic) were improved. Combined with cutting speed optimization experiments, gas consumption was reduced to 0.11 kg/t. Experimental results showed that cutting time was shortened by 13~19 seconds, surface roughness (Ra) decreased from 46.5 μm to 8.7 μm, and slag adhesion was significantly improved. A t-test further validated the significance of the improvements. This research provides theoretical and practical foundations for refined cost control in continuous casting billet flame cutting and demonstrates industrial promotion value.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".