Modelling and analysis of oxide growth kinetics and thermal effects during heat treatment of a MCLA steel: comparing gas-fired and electric furnaces
Bibliographic record
Abstract
The growth of oxide layers during heat treatment can lead to material deterioration and alter thermal behaviour, particularly in large-scale steel components. Understanding and controlling this oxidation is essential for improving energy efficiency and preserving material integrity. This research aimed to develop predictive oxidation models and quantify the thermal impact of oxide layers on heat transfer and heating time for medium-carbon low-alloy steel. Oxidation behaviour was investigated in gas-fired and electric furnaces through lab – and real-scale experiments, microscopy, and simulations. Eighteen electric furnace tests examined growth kinetics and sublayer formation, followed by industrial tests in a gas-fired furnace using two 7-ton blocks. Models were developed to predict oxide dynamics at 900–1200°C in electric furnaces and at 1200°C in gas-fired ones. Experimental results showed oxide porosity ranging from 9.7% to 12.9% in industrial conditions, and deterioration was up to 60% lower in electric furnaces. The calculated PBR of 1.23 indicated wüstite (FeO) dominance and reflected oxygen availability. Simulations showed that oxide layers delayed heat diffusion, increasing heating time by ≈13.5% in gas furnaces, while electric furnaces reduced it by ≈8.5%. These findings provide new insights into the shift from gas-fired to electric furnaces in industrial heat treatment applications.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".