Fermentation process quality prediction using teacher student stacked sparse recurrent autoencoder. (20th September 2021)

• Record Type: Journal Article
• Authors: Gao, Xuejin; Meng, Lingjun; Gao, Huihui; Han, Huayun; Qi, Yongsheng
• Is Part Of: Canadian journal of chemical engineering. Volume 100:Number 10(2022)
• Page Start: 2907
• Page End: 2917
• Publication Date: 2021-09-20
• Subjects: autoencoder -- fermentation process -- knowledge distillation -- LSTM -- sparse; Chemical engineering -- Periodicals; Technology -- Periodicals; 660.05
• DOI: 10.1002/cjce.24303
• Languages: English
• ISSNs: 0008-4034

Abstract

Abstract: For predicting the value of the quality variable in fermentation processes, traditional data‐driven methods do not use information in large amounts of unlabelled data. To solve this data‐rich but information‐poor (DRIP) problem, a teacher student stacked sparse recurrent autoencoder (TS‐SSRAE) model is proposed. Compared with traditional data‐driven methods, the proposed method has three main advantages. First, an autoencoder is an unsupervised method which can effectively extract rich information in unlabelled data. The proposed stacked recurrent autoencoder (SRAE) with long short‐term memory (LSTM) recurrent neural unit is superior to traditional autoencoders when extracting the dynamic correlation information in the fermentation process. Second, sparse constraints can make it much easier for hidden neurons to obtain useful information in a single moment. Finally, the LSTM recurrent neural unit is complex and the inputs of a SRAE must be a sequence, which increases the complexity of the model to a certain extent. So, the knowledge distillation is employed to simplify the model and reduce the computing time. In order to demonstrate its effectiveness, the proposed method is applied to the penicillin fermentation process for a simulation experiment and Escherichia coli production of interleukin‐2. The results show that the proposed method based on TS‐SSRAE can have better performance than conventional methods.