| Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network |
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| 作者姓名： | ZHANG Jun-hong XIE An-guo SHEN Feng-man |
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| 作者单位： | [1]Anshan University of Science and Technology, Anshan 114044, Liaoning, Chinas [2]Institute of Ferrous Metallurgy, Northeastern University, Shenyang 110004, Liaoning, China |
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| 摘 要： | A multi-objective optimization and analysis model of the sintering process based on BP neural network is presented. Genetic algorithms are combined to simplify the BP neural network, which can reduce the learning time and increase the forecasting accuracy of the network model. This model has been experimented in the sintering process, and the production cost, the energy consumption, the quality （revolving intensity）, and the output are considered at the same time. Moreover, the relation between some factors and the multi-objectives has been analyzed, and the results are consistent with the process. Different objectives are emphasized at different practical periods, and this can provide a theoretical basis for the manager.
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| 关 键 词： | 烧结过程 解析模型 多目标优化 BP神经网络 |
| 收稿时间： | 2006-09-19 |
Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network |
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| ZHANG Jun-hong XIE An-guo SHEN Feng-man.Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network[J].Journal of Iron and Steel Research,2007,14(2):1-5. |
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| Authors: | ZHANG Jun-hong XIE An-guo SHEN Feng-man |
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| Affiliation: | 1. Anshan University of Science and Technology, Anshan 114044, Liaoning, China;2. Institute of Ferrous Metallurgy, Northeastern University, Shenyang 110004, Liaoning, China |
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| Abstract: | A multi-objective optimization and analysis model of the sintering process based on BP neural network is presented. Genetic algorithms are combined to simplify the BP neural network, which can reduce the learning time and increase the forecasting accuracy of the network model. This model has been experimented in the sintering process, and the production cost, the energy consumption, the quality (revolving intensity), and the output are considered at the same time. Moreover, the relation between some factors and the multi-objectives has been analyzed, and the results are consistent with the process. Different objectives are emphasized at different practical periods, and this can provide a theoretical basis for the manager. |
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| Keywords: | BP neural network multi-objective optimization sinter |
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