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A Neural Network Model ( NNM) for sintering process was investigated in this paper.According to raw material characteristics and its blending proportion in sintering plant,this intelligent model can effectively predict sintering performance indexes ( including productivity,fuel consumption,TI,particle size,,RDI and RI).Before building this Neural Network Model,it is important to understand the relationship between sintering performance indexes and iron ore characteristics.First,iron ore characteristics ( including chemical composition,physical quality,microstructure,and high temperature properties) of 41 kinds of Chinese and overseas ores were experimented.Second,70 different schemes of sinter pot tests were conducted.Third,basing on the result of the lst and 2nd steps,a software named TIBERIUS was used to build the model correlating sintering performance with iron ore characteristics.Finally,the effectiveness of the model was tested by using actual iron ore mix characteristics of a Chinese and a Brazilian steel mill.The model output predicted sintering performance indexes very close to the real production results,showing good efficiency and applicability of the model.