Heng Zhou, Yifan Hu, Bingjie Wen, Shengli Wu, M. Kou, Yinsheng Luo
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BP neural network prediction for Si and S contents in hot metal of COREX process based on mathematical analysis and Deng’s correlation
In COREX operation, the Si and S contents in hot metal are relatively high and easy-fluctuating, which is one of the problems affecting the practical operation. Accurate predictions of Si and S contents can provide theoretical references for stabilizing the fluctuations and decreasing the contents of Si and S in hot metal. Therefore, the present work established the prediction model of Si and S contents in hot metal in COREX based on BP neural network. The results show that the root-mean-square errors between the predicted value and actual value for Si and S are 0.098 and 0.0037, respectively. They are 0.070 and 0.0040 when the time-sequence lapse method is adopted, which turns out to be better. Therefore, the model is accurate and reliable to predict the Si and S contents in hot metal in COREX.
期刊介绍:
Metallurgical Research and Technology (MRT) is a peer-reviewed bi-monthly journal publishing original high-quality research papers in areas ranging from process metallurgy to metal product properties and applications of ferrous and non-ferrous metals and alloys, including light-metals. It covers also the materials involved in the metal processing as ores, refractories and slags.
The journal is listed in the citation index Web of Science and has an Impact Factor.
It is highly concerned by the technological innovation as a support of the metallurgical industry at a time when it has to tackle severe challenges like energy, raw materials, sustainability, environment... Strengthening and enhancing the dialogue between science and industry is at the heart of the scope of MRT. This is why it welcomes manuscripts focusing on industrial practice, as well as basic metallurgical knowledge or review articles.