Multivariate Time-Series Modeling for Forecasting Sintering Temperature in Rotary Kilns Using DCGNet

Abstract

The sintering temperature (ST) is a critical index for condition monitoring and process control of coal-fired equipment and is widely used in the production of cement, aluminum, electricity, steel, and chemicals. The accurate prediction of the ST is important for control systems to anticipate tragedies. In this article, we propose a deep learning model for forecasting the ST using automatic spatiotemporal feature extraction from multivariate thermal time series. A hybrid deep neural network named deep convolutional neural network and gated recurrent unit network (DCGNet) is designed to extract multivariate coupling and nonlinear dynamic characteristics for forecasting the ST. DCGNet uses convolutional neural networks and gated recurrent unit (GRU) to extract the local spatial-temporal dependence patterns among the multivariates, and another parallel GRU using the historical ST data as input is incorporated to more accurately capture the dynamic characteristics of ST time series. Based on the real-world data, application results show that the proposed approach has high forecasting accuracy and robustness, thus having broad application prospects in industrial processes.