Expert system for controlling sinter chemistry based on neural network prediction
LONG Hongming~ 1) , FAN Xiaohui~ 1) , CHEN Xuling~ 1) , JIANG Tao~ 1) , SHI Jun~ 2) , SONG Qingyong~ 2) , YANG Xiaodong~ 2) 1) School of Resources Processing and Bioengineering, Central South University, Changsha 410083, China2) Steelmaking Factory, Panzhihua New Steel & Vanadium Co. Ltd., Panzhihua 617022, China
A sintering predictive model of chemical composition based on many periods was developed by the BP neural network algorithm with appending momentum and adaptive variable step size linear reinforcement. Using knowledge base that was based on database technology and illation with forward inference, an expert system was designed for controlling sinter chemistry. Since the system was plunged into application, the hit ratio of the predictive model is over 90% steadily, and the acceptance of operation suggestion is 92%. The goal of controlling chemical composition steadily is actualized.