Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks
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 de...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Metals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4701/15/1/62 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587912212905984 |
|---|---|
| author | Riadh Azzaz Mohammad Jahazi Samira Ebrahimi Kahou Elmira Moosavi-Khoonsari |
| author_facet | Riadh Azzaz Mohammad Jahazi Samira Ebrahimi Kahou Elmira Moosavi-Khoonsari |
| author_sort | Riadh Azzaz |
| collection | DOAJ |
| description | 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 (<i>MSE</i>) of 0.000016, a root mean square error (<i>RMSE</i>) of 0.0049998, a coefficient of determination (<i>R</i><sup>2</sup>) 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. |
| format | Article |
| id | doaj-art-33ae47317ac647fcb38e91099a70630d |
| institution | Kabale University |
| issn | 2075-4701 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj-art-33ae47317ac647fcb38e91099a70630d2025-01-24T13:41:33ZengMDPI AGMetals2075-47012025-01-011516210.3390/met15010062Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural NetworksRiadh Azzaz0Mohammad Jahazi1Samira Ebrahimi Kahou2Elmira Moosavi-Khoonsari3Department of Mechanical Engineering, École de Technologie Supérieure (ÉTS), 1100 Notre-Dame Street West, Montréal, QC H3C 1K3, CanadaDepartment of Mechanical Engineering, École de Technologie Supérieure (ÉTS), 1100 Notre-Dame Street West, Montréal, QC H3C 1K3, CanadaSchulich School of Engineering, Department of Electrical and Software Engineering, University of Calgary, 856 Campus Pl NW, Calgary, AB T2N 4V8, CanadaDepartment of Mechanical Engineering, École de Technologie Supérieure (ÉTS), 1100 Notre-Dame Street West, Montréal, QC H3C 1K3, CanadaThe 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 (<i>MSE</i>) of 0.000016, a root mean square error (<i>RMSE</i>) of 0.0049998, a coefficient of determination (<i>R</i><sup>2</sup>) 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.https://www.mdpi.com/2075-4701/15/1/62steelmakingscrap-based electric arc furnaceartificial neural networkmachine learningdephosphorization |
| spellingShingle | Riadh Azzaz Mohammad Jahazi Samira Ebrahimi Kahou Elmira Moosavi-Khoonsari Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks Metals steelmaking scrap-based electric arc furnace artificial neural network machine learning dephosphorization |
| title | Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks |
| title_full | Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks |
| title_fullStr | Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks |
| title_full_unstemmed | Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks |
| title_short | Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks |
| title_sort | prediction of final phosphorus content of steel in a scrap based electric arc furnace using artificial neural networks |
| topic | steelmaking scrap-based electric arc furnace artificial neural network machine learning dephosphorization |
| url | https://www.mdpi.com/2075-4701/15/1/62 |
| work_keys_str_mv | AT riadhazzaz predictionoffinalphosphoruscontentofsteelinascrapbasedelectricarcfurnaceusingartificialneuralnetworks AT mohammadjahazi predictionoffinalphosphoruscontentofsteelinascrapbasedelectricarcfurnaceusingartificialneuralnetworks AT samiraebrahimikahou predictionoffinalphosphoruscontentofsteelinascrapbasedelectricarcfurnaceusingartificialneuralnetworks AT elmiramoosavikhoonsari predictionoffinalphosphoruscontentofsteelinascrapbasedelectricarcfurnaceusingartificialneuralnetworks |