Kai Feng, Hong-bing Wang, An-jun Xu, and Dong-feng He, Endpoint temperature prediction of molten steel in RH using improved case-based reasoning, Int. J. Miner. Metall. Mater., 20(2013), No. 12, pp. 1148-1154. https://doi.org/10.1007/s12613-013-0848-7
Cite this article as:
Kai Feng, Hong-bing Wang, An-jun Xu, and Dong-feng He, Endpoint temperature prediction of molten steel in RH using improved case-based reasoning, Int. J. Miner. Metall. Mater., 20(2013), No. 12, pp. 1148-1154. https://doi.org/10.1007/s12613-013-0848-7
Kai Feng, Hong-bing Wang, An-jun Xu, and Dong-feng He, Endpoint temperature prediction of molten steel in RH using improved case-based reasoning, Int. J. Miner. Metall. Mater., 20(2013), No. 12, pp. 1148-1154. https://doi.org/10.1007/s12613-013-0848-7
Citation:
Kai Feng, Hong-bing Wang, An-jun Xu, and Dong-feng He, Endpoint temperature prediction of molten steel in RH using improved case-based reasoning, Int. J. Miner. Metall. Mater., 20(2013), No. 12, pp. 1148-1154. https://doi.org/10.1007/s12613-013-0848-7
An improved case-based reasoning (CBR) method was proposed to predict the endpoint temperature of molten steel in Ruhrstahl Heraeus (RH) process. Firstly, production data were analyzed by multiple linear regressions and a pairwise comparison matrix in analytic hierarchy process (AHP) was determined by this linear regression’s coefficient. The weights of various influencing factors were obtained by AHP. Secondly, the dividable principles of case base including “0–1” and “breakpoint” were proposed, and the case base was divided into several homogeneous parts. Finally, the improved CBR was compared with ordinary CBR, which is based on the even weight and the single base. The results show that the improved CBR has a higher hit rate for predicting the endpoint temperature of molten steel in RH.
Search
DownLoad: