Dynamic soft-sensing model by combining diagonal recurrent neural network with levinson predictor

Authors:
Hui Geng;Zhihua Xiong;Shuai Mao;Yongmao Xu
Affiliations:
Department of Automation, Tsinghua University, Beijng, P.R. China;Department of Automation, Tsinghua University, Beijng, P.R. China;Department of Automation, Tsinghua University, Beijng, P.R. China;Department of Automation, Tsinghua University, Beijng, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

Dynamic soft-sensing model of diesel oil solidifying point (DOSP) in crude distillation unit (CDU) is proposed based on diagonal recurrent neural network (DRNN). Because of long time-delay of the DOSP measurements, multi-step-ahead predictions are obtained recursively by Levinson predictor and then used as input of DRNN. Simulation results on the actual industrial process data show that the proposed dynamic soft-sensing model took good effects practically and significantly diminished the time-delay of output value.