Intelligent Integrated Model for Predicting Burn-Through Point Based on Gas Temperature Distribution
نویسندگان
چکیده
This paper presents an integrated model for predicting the burn-through point (BTP) of the lead-zinc sintering process from the gas temperature distribution (GTD). This process features strong nonlinearity, a large time delay, and time-varying parameters. First, the characteristics of the GTD in the sintering machine are obtained from experiments, and a surface temperature model for the material is established. Based on that model, the current BTP is obtained by a soft-sensing technique. Then, a time-sequence-based model for predicting the BTP is built using grey theory. Since the BTP is affected by variations in the process parameters, a technological-parameter-based prediction model of the BTP is set up using a neural network. Finally, an integrated model for predicting the BTP is implemented using a fuzzy classifier to integrate the time-sequence-based and technological-parameter-based models. The results of actual runs demonstrate the validity of the method.
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