A batch process monitoring method based on long short term memory network and support vector data description

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Abstract Batch process is an important operating mode in chemical process. Different from continuous processes, batch process production is not operated in a stable operating point, but a preset trajectory according to raw material ratio and operating conditions. Therefore, process data in batch process display complex characteristics including multi-stage, dynamic time-varying and nonlinear, which means traditional monitoring methods cannot be directly applied to monitor the operating status in batch process. To solve this problem, a novel process monitoring strategy is proposed in this paper. Long short-term memory network is first applied to establish a regression model by extracting the dynamic and nonlinear feature among process variables. Then residuals obtained from the regression model is considered as the monitoring object. Since the multi-stage and dynamic characteristics no longer exist in the obtained residuals, the continuous process monitoring methods can be directly applied to establish a monitoring model for residuals. Finally, the support vector data description is selected to describe the residuals of the normal data set in a hypersphere, and the distance from the test set sample point to the center of the hypersphere is used as a monitoring statistic to detect the faults. The method is applied to a simulated penicillin fermentation process, the results show that complex batch process monitoring can be transformed into a relatively simple continuous process monitoring problem to deal with, and the process faults can be detected in time.

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	Articles by authors
	JI Cheng
	GU Junfa
	WANG Jianhong
	WANG Jingde
	SUN Wei

Keywords： dynamic regression model； residual； real-time monitoring； penicillin fermentation process

Received 2021-08-02;

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JI Cheng, GU Junfa, WANG Jianhong, WANG Jingde, SUN Wei.A batch process monitoring method based on long short term memory network and support vector data description[J] Chemcial Industry and Engineering, 2022,V39(2): 32-36

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