A combined approach of generalized additive model and bootstrap with small sample sets for fault diagnosis in fermentation process of glutamate

Microbial Cell Factories

Research

Feng Pan¹  Chunbo Liu²  Yun Li³ 

[1]Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, 1800 Lihu Avenue, 214122, Wuxi, Jiangsu, China

[2]Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, 1800 Lihu Avenue, 214122, Wuxi, Jiangsu, China

[3]Mathematics, Informatics and Statistics Leeuwin Centre, Commonwealth Scientific and Industrial Research Organization (CSIRO), 65 Brockway Road, 6014, Floreat, WA, Australia

[4]Mathematics, Informatics and Statistics Leeuwin Centre, Commonwealth Scientific and Industrial Research Organization (CSIRO), 65 Brockway Road, 6014, Floreat, WA, Australia

关键词: Fermentation process;    Glutamate;    Generalized additive model;    Bootstrap;    Small samples;    Fault diagnosis;   

DOI  :  10.1186/s12934-016-0528-1

 received in 2016-04-28, accepted in 2016-07-21,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundGlutamate is of great importance in food and pharmaceutical industries. There is still lack of effective statistical approaches for fault diagnosis in the fermentation process of glutamate. To date, the statistical approach based on generalized additive model (GAM) and bootstrap has not been used for fault diagnosis in fermentation processes, much less the fermentation process of glutamate with small samples sets.ResultsA combined approach of GAM and bootstrap was developed for the online fault diagnosis in the fermentation process of glutamate with small sample sets. GAM was first used to model the relationship between glutamate production and different fermentation parameters using online data from four normal fermentation experiments of glutamate. The fitted GAM with fermentation time, dissolved oxygen, oxygen uptake rate and carbon dioxide evolution rate captured 99.6 % variance of glutamate production during fermentation process. Bootstrap was then used to quantify the uncertainty of the estimated production of glutamate from the fitted GAM using 95 % confidence interval. The proposed approach was then used for the online fault diagnosis in the abnormal fermentation processes of glutamate, and a fault was defined as the estimated production of glutamate fell outside the 95 % confidence interval. The online fault diagnosis based on the proposed approach identified not only the start of the fault in the fermentation process, but also the end of the fault when the fermentation conditions were back to normal. The proposed approach only used a small sample sets from normal fermentations excitements to establish the approach, and then only required online recorded data on fermentation parameters for fault diagnosis in the fermentation process of glutamate.ConclusionsThe proposed approach based on GAM and bootstrap provides a new and effective way for the fault diagnosis in the fermentation process of glutamate with small sample sets.

【 授权许可】

CC BY   
© The Author(s) 2016

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