シャ　キンカ   Jinhua She   佘　錦華    所属   工学部 機械工学科    職種   教授
言語種別	英語
発行・発表の年月	2012/05
形態種別	学術論文
査読	査読あり
標題	Neural-network-based integrated model for predicting burn-through point in lead-zinc sintering process
執筆形態	共著
掲載誌名	Journal of Process Control
出版社・発行元	Elsevier
巻・号・頁	22(5),pp.925-934
著者・共著者	Min Wu, Chenghua Xu, Jinhua She, and Weihua Cao
概要	This paper presents an integrated neural-network-based model for predicting the burn-through point (BTP) of a lead–zinc sintering process. This process features strong nonlinearity and time-varying parameters. First, experiments were carried out to establish a model of the gas temperature distribution (GTD) in the sintering machine; and based on the GTD model, a surface temperature model of the material (STMM) was established. Second, based on the STMM, a method of estimating the BTP that uses a soft-sensing technique was devised. In order to improve the estimation precision, a time-sequence-based model for predicting the BTP was built using grey system theory. Since the BTP is also affected by process parameters, a technological-parameter-based model for predicting the BTP was then built using a neural network. Finally, an integrated model for predicting the BTP was constructed by combining the time-sequence-based and the technological-parameter-based models using a fuzzy classifier. The result of actual runs shows that, compared to the manual control, the integrated prediction model reduced the variation in BTP by about 50%. This guarantees the improvement of the quality and quantity of the sinter.
DOI	10.1016/j.jprocont.2012.03.007