Life cycle and economic assessment of recycled steel using waste heat in industry
Bibliographic record
Abstract
This study examines the environmental and economic benefits of waste recovery in steel plants using life cycle assessment (LCA), metallurgical analysis, and economic feasibility studies. The LCA, conducted with GaBi software, indicates that recycled steel reduces carbon dioxide emissions by 80%, energy consumption by 70%, and water usage by 60% compared to virgin steel. Waste recovery assessment identified key recyclable fractions from steel melting shops, blooming mills, and rolling mills. A comparative analysis with virgin steel, aluminium, and copper indicates that recycled steel has the lowest carbon footprint and exhibits superior recycling efficiency, particularly in well-managed industrial processes using electric arc furnace technology. The techno-economic study demonstrated 40% cost savings in material procurement, with a return on investment under two years and an internal rate of return of 35%. Long-term durability studies confirmed that recycled T22-grade steel maintains its structural integrity, fatigue strength, and corrosion resistance over time. These findings support global sustainability targets, including the Paris Agreement and India’s National Steel Policy, and highlight the role of artificial intelligence, blockchain, and predictive analytics in optimising waste recovery. This study reinforces the potential of sustainable steel manufacturing to enhance cost efficiency, reduce emissions, and promote circular economy practices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".