A Deep Learning-Based Soft Sensing Prediction Model for Tubular Furnace

Abstract

Tubular furnaces are necessary in petrochemical industry, whose high-level automation has been hampered by the complicated inner thermal mechanism. To realize the high-accuracy prediction of key parameters of furnace thermal state, including thermal efficiency, which cannot be measured directly by sensors, in this paper, a soft sensing prediction model for tubular furnace is proposed. Based on the traditional CNN-GRU network, which is composed by the convolutional neural network (CNN) and the gated recurrent neural network (GRU), that the two designed feature extraction modules are embed, ultimately compose the proposed Conv-GRU network. Comparative experiments demonstrate that the proposed combinational network with two well-designed modules outperforms general convolution networks and shallow neural networks in terms of prediction accuracy. The results prove that the proposed GRU-Conv can accurately model the tubular furnace inner state with low computational cost, providing improvements room for the performance of combustion optimization control systems for tubular heating furnaces.