Prediction of viscosity of blast furnace slag based on NRBO-DNN model
The viscosity of blast furnace slag significantly impacts operations, slag discharge, and heat recovery. However, accurately measuring or calculating viscosity is challenging due to the complex composition, interactions among variables, and experimental difficulties at high temperatures. To address...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001541 |
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| author | Zhe Li Meng Wang Rui Xu Juanjuan Jiang Jie Li Zunqian Zhang Aimin Yang |
| author_facet | Zhe Li Meng Wang Rui Xu Juanjuan Jiang Jie Li Zunqian Zhang Aimin Yang |
| author_sort | Zhe Li |
| collection | DOAJ |
| description | The viscosity of blast furnace slag significantly impacts operations, slag discharge, and heat recovery. However, accurately measuring or calculating viscosity is challenging due to the complex composition, interactions among variables, and experimental difficulties at high temperatures. To address this issue, a prediction model was developed based on slag composition. Data preprocessing included isolation forest outlier detection, missing data imputation, normalization, and Generative Adversarial Network (GAN)-based data augmentation, ensuring high-quality data. Among traditional neural network models, the Deep Neural Network (DNN) demonstrated the best accuracy. Optimizing the DNN with an intelligent swarm algorithm resulted in the NRBO-DNN model, which achieved MAE, MSE, RMSE, and R² values of 0.04050, 0.00305, 0.05527, and 0.97599, respectively. Compared to the unoptimized DNN, MAE, MSE, and RMSE decreased by 53.86 %, 50.30 %, and 29.50 %, while R² improved by 8.11 %. Tests on 100 datasets confirmed the NRBO-DNN’s superior accuracy, with an average error of 4.30 %. This study provides theoretical support and practical guidance for optimizing blast furnace operations. |
| format | Article |
| id | doaj-art-32763a2e8f844e21a23f8ad56a6c6789 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-32763a2e8f844e21a23f8ad56a6c67892025-02-04T04:10:19ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119124137Prediction of viscosity of blast furnace slag based on NRBO-DNN modelZhe Li0Meng Wang1Rui Xu2Juanjuan Jiang3Jie Li4Zunqian Zhang5Aimin Yang6Hebei Engineering Research Center of Iron Ore Optimization and Iron Pre-process Intelligence, North China University of Science and Technology, Tangshan 063210, China; Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China; College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, ChinaHebei Engineering Research Center of Iron Ore Optimization and Iron Pre-process Intelligence, North China University of Science and Technology, Tangshan 063210, China; Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China; College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, ChinaHebei Engineering Research Center of Iron Ore Optimization and Iron Pre-process Intelligence, North China University of Science and Technology, Tangshan 063210, China; Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China; College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, ChinaHebei Engineering Research Center of Iron Ore Optimization and Iron Pre-process Intelligence, North China University of Science and Technology, Tangshan 063210, China; Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China; College of Science, North China University of Science and Technology, Tangshan 063210, China; Corresponding author at: College of Science, North China University of Science and Technology, Tangshan 063210, China.Hebei Engineering Research Center of Iron Ore Optimization and Iron Pre-process Intelligence, North China University of Science and Technology, Tangshan 063210, China; Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China; College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, ChinaHebei Engineering Research Center of Iron Ore Optimization and Iron Pre-process Intelligence, North China University of Science and Technology, Tangshan 063210, China; Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China; College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, ChinaHebei Engineering Research Center of Iron Ore Optimization and Iron Pre-process Intelligence, North China University of Science and Technology, Tangshan 063210, China; Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan 063210, China; College of Science, North China University of Science and Technology, Tangshan 063210, ChinaThe viscosity of blast furnace slag significantly impacts operations, slag discharge, and heat recovery. However, accurately measuring or calculating viscosity is challenging due to the complex composition, interactions among variables, and experimental difficulties at high temperatures. To address this issue, a prediction model was developed based on slag composition. Data preprocessing included isolation forest outlier detection, missing data imputation, normalization, and Generative Adversarial Network (GAN)-based data augmentation, ensuring high-quality data. Among traditional neural network models, the Deep Neural Network (DNN) demonstrated the best accuracy. Optimizing the DNN with an intelligent swarm algorithm resulted in the NRBO-DNN model, which achieved MAE, MSE, RMSE, and R² values of 0.04050, 0.00305, 0.05527, and 0.97599, respectively. Compared to the unoptimized DNN, MAE, MSE, and RMSE decreased by 53.86 %, 50.30 %, and 29.50 %, while R² improved by 8.11 %. Tests on 100 datasets confirmed the NRBO-DNN’s superior accuracy, with an average error of 4.30 %. This study provides theoretical support and practical guidance for optimizing blast furnace operations.http://www.sciencedirect.com/science/article/pii/S1110016825001541ViscosityBlast furnace slagIsolation forestGANDNNNRBO-DNN model |
| spellingShingle | Zhe Li Meng Wang Rui Xu Juanjuan Jiang Jie Li Zunqian Zhang Aimin Yang Prediction of viscosity of blast furnace slag based on NRBO-DNN model Alexandria Engineering Journal Viscosity Blast furnace slag Isolation forest GAN DNN NRBO-DNN model |
| title | Prediction of viscosity of blast furnace slag based on NRBO-DNN model |
| title_full | Prediction of viscosity of blast furnace slag based on NRBO-DNN model |
| title_fullStr | Prediction of viscosity of blast furnace slag based on NRBO-DNN model |
| title_full_unstemmed | Prediction of viscosity of blast furnace slag based on NRBO-DNN model |
| title_short | Prediction of viscosity of blast furnace slag based on NRBO-DNN model |
| title_sort | prediction of viscosity of blast furnace slag based on nrbo dnn model |
| topic | Viscosity Blast furnace slag Isolation forest GAN DNN NRBO-DNN model |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001541 |
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