Azzaz, Riadh, Jahazi, Mohammad, Ebrahimi Kahou, Samira et Moosavi-Khoonsari, Elmira.
2025.
« Prediction of final phosphorus content of steel in a scrap-based electric arc furnace using artificial neural networks ».
Metals, vol. 15, nº 1.
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Résumé
The scrap-based electric arc furnace process is expected to capture a significant share of the steel market in the future due to its potential for reducing environmental impacts through steel recycling. However, managing impurities, particularly phosphorus, remains a challenge. This study aims to develop a machine learning model to estimate steel phosphorus content at the end of the process based on input parameters. Data were collected over one year from a steel plant, focusing on parameters such as the chemical composition and weight of the scrap, the volume of oxygen injected, injected lime, and process duration. After preprocessing the data, several machine learning models were evaluated, with the artificial neural network (ANN) emerging as the most effective. The Adam optimizer and non-linear sigmoid activation function were employed. The best ANN model included four hidden layers and 448 neurons. The model was trained for 500 epochs with a batch size of 50. The model achieves a mean square error (MSE) of 0.000016, a root mean square error (RMSE) of 0.0049998, a coefficient of determination (R2) of 99.96%, and a correlation coefficient (r) of 99.98%. Notably, the model was tested on over 200 unseen data points and achieved a 100% hit rate for predicting phosphorus content within ±0.001 wt% (±10 ppm). These results demonstrate that the optimized ANN model offers accurate predictions for the steel final phosphorus content.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Jahazi, Mohammad Ebrahimi-Kahou, Samira Moosavi-Khoonsari, Elmira |
| Affiliation: | Génie mécanique, Génie logiciel et des technologies de l'information, Génie mécanique |
| Date de dépôt: | 13 févr. 2025 16:42 |
| Dernière modification: | 04 mars 2025 14:43 |
| URI: | https://espace2.etsmtl.ca/id/eprint/30532 |
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