Neural-network-based integrated model for predicting burn-through point in lead–zinc sintering process

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.

► We built a surface temperature model of the material (STMM).
► Based on the STMM, we devised a method of estimating the burn-through point (BTP).
► We built a time-sequence-based prediction model (TSBPM) using grey system theory.
► We also built a technological-parameter-based prediction model (TPBPM) using an NN.
► We then constructed an integrated model by combining the TSBPM and TPBPM using a fuzzy classifier.