Intelligent control strategy for industrial furnaces based on yield classification prediction using a gray relative correlation-convolutional neural network-multilayer perceptron (GCM) machine learning model

Abstract

Industrial furnaces still play an important role in national economic growth. Owing to the complexity of the production process, the product yield fluctuates, and cannot be executed in real time, which has not kept pace with the development of the intelligent technologies in Industry 4.0. In this study, based on the deep learning theory and operational data collected from more than one year of actual production of a lime kiln, we proposed a hybrid deep network model combining a gray relative correlation, a convolutional neural network and a multilayer perceptron model (GCM) to categorize production processes and predict yield classifications. The results show that the loss and calculation time of the model based on the screened set of variables are significantly reduced, and the accuracy is almost unaffected; the GCM model has the best performance in predicting the yield classification of lime kilns. The intelligent control strategy for non-fault state is then set according to the predicted yield classification. Operating parameters are adjusted in a timely manner according to different priority control sequences to achieve higher yield, ensure high production efficiency, reduce unnecessary waste, and save energy.