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

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

Xiaowen Wang, Yongjun Zhang, Qiang Guo, Fei Zhang and Tanju Yildirim
2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), pp.13-21
2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) (Hangzhou, China, 19/06/2022–21/06/2022)
07/12/2022
DOI: https://doi.org/10.1109/FAIML57028.2022.00013

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Abstract

Soft sensing prediction Furnace modeling Convolutional neural network Recurrent neural network Gated recurrent unit
Conference Title: 2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML) Conference Start Date: 2022, June 19 Conference End Date: 2022, June 21 Conference Location: Hangzhou, ChinaTubular 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.

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