A robust temperature prediction model of shuttle kiln based on ensemble random vector functional link network

Abstract In the sintering process for shuttle kiln, the sintering temperature is the most important thermal parameter, which plays a critical role in stable and efficient operation of the production process. However, due to the complex structure of shuttle kiln, this poses a great challenge for accurate and reliable online prediction of sintering temperature. Therefore, in this paper, a novel prediction model based on ensemble random vector functional link network (AB-RRVFLN) is proposed to predict the temperature. First, the mutual information method is proposed to select the optimal variables for prediction model. Then, considering the outlier influence of industrial data, a robust random vector functional link network (RRVFLN) based on the iteratively reweighted least squares is developed to build prediction model. Moreover, AdaBoost strategy is integrated with RRVFLN model to enhance prediction performance. Finally, the model output is further corrected by offset compensation technique. Based on industrial data from shuttle kiln, the application results on sintering temperature prediction demonstrate that the proposed ensemble method achieves better accuracy and shows stronger robustness than other methods.