Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering
Abstract As a major burden of blast furnace, sinter mineral with desired quality performance needs to be produced in sinter plants. The tumbler index (TI) is one of the most important indices to characterize the quality of sinter, which depends on the raw materials proportion, operating system param...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88755-1 |
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| author | Senhui Wang |
| author_facet | Senhui Wang |
| author_sort | Senhui Wang |
| collection | DOAJ |
| description | Abstract As a major burden of blast furnace, sinter mineral with desired quality performance needs to be produced in sinter plants. The tumbler index (TI) is one of the most important indices to characterize the quality of sinter, which depends on the raw materials proportion, operating system parameters and the chemical compositions. To accurately predict TI, an integrate model is proposed in this study. First, to decrease the data dimensionality, the sintering production data is addressed through principal component analysis (PCA) and the principal components with the accumulated contribution rate no more than 95% are extracted as the inputs of the predictive model based on Extreme Learning Machine (ELM). Second, the genetic algorithm (GA) has been applied to promote the improvement of the robustness and generalization performance of the original ELM. Finally, the model is examined using actual production data of a year from a sinter plant, and is compared with the algorithms of single ELM, GA-BP and deep learning method. A comparison is conducted to confirm the superiority of the proposed model with two traditional models. The results showed that an improvement in predictive accuracy can be obtained by the GA-ELM approach, and the accuracy of TI prediction is 81.85% for absolute error under 0.7%. |
| format | Article |
| id | doaj-art-2a95b0ae351e45c8abc12afbaa545db3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2a95b0ae351e45c8abc12afbaa545db32025-02-09T12:28:23ZengNature PortfolioScientific Reports2045-23222025-02-0115111910.1038/s41598-025-88755-1Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sinteringSenhui Wang0School of Mechatronic Engineering, Anhui University of Science and TechnologyAbstract As a major burden of blast furnace, sinter mineral with desired quality performance needs to be produced in sinter plants. The tumbler index (TI) is one of the most important indices to characterize the quality of sinter, which depends on the raw materials proportion, operating system parameters and the chemical compositions. To accurately predict TI, an integrate model is proposed in this study. First, to decrease the data dimensionality, the sintering production data is addressed through principal component analysis (PCA) and the principal components with the accumulated contribution rate no more than 95% are extracted as the inputs of the predictive model based on Extreme Learning Machine (ELM). Second, the genetic algorithm (GA) has been applied to promote the improvement of the robustness and generalization performance of the original ELM. Finally, the model is examined using actual production data of a year from a sinter plant, and is compared with the algorithms of single ELM, GA-BP and deep learning method. A comparison is conducted to confirm the superiority of the proposed model with two traditional models. The results showed that an improvement in predictive accuracy can be obtained by the GA-ELM approach, and the accuracy of TI prediction is 81.85% for absolute error under 0.7%.https://doi.org/10.1038/s41598-025-88755-1Genetic algorithmExtreme learning machinePrincipal component analysisTumbler indexSintering |
| spellingShingle | Senhui Wang Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering Scientific Reports Genetic algorithm Extreme learning machine Principal component analysis Tumbler index Sintering |
| title | Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering |
| title_full | Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering |
| title_fullStr | Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering |
| title_full_unstemmed | Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering |
| title_short | Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering |
| title_sort | applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering |
| topic | Genetic algorithm Extreme learning machine Principal component analysis Tumbler index Sintering |
| url | https://doi.org/10.1038/s41598-025-88755-1 |
| work_keys_str_mv | AT senhuiwang applyinggeneticalgorithmtoextremelearningmachineinpredictionoftumblerindexwithprincipalcomponentanalysisforironoresintering |