Fault Diagnosis for Pichia pastoris Fermentation Based on a Hybrid Support Vector Machine and Fuzzy Reasoning Technique

doi:10.3969/j.issn.1673-1689.2014.11.010

Home > Archive>Volume 33, Issue 11, 2014 >1182-1190. DOI:10.3969/j.issn.1673-1689.2014.11.010

Fault Diagnosis for Pichia pastoris Fermentation Based on a Hybrid Support Vector Machine and Fuzzy Reasoning Technique
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                        10.3969/j.issn.1673-1689.2014.11.010
                    
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In heterologous protein production by methylotrophic Pichia pastoris, the methanol concentration variation in culture medium severely affects fermentation stability. An intelligent pattern recognition based fault diagnosis system was thus proposed to solve the problem. A support vector machine classifier(SVM) was firstly used to categorize the on-line measurable parameters(fermentation time, agitation rate, methanol consumption rate, O2 uptake rate OUR and CO2 evolution rate CER) within a moving-window, and then the SVM was combined with fuzzy reasoning technique to construct an unique intelligent fault diagnosis system, which could classify the fermentation physiological states into 3 catagories of methanol "in shortage", "medium", and "in excess". In the cases of improper methanol concentration, the system could accurately identify the faults and their type. Then those failure-likelihood fermentations could be rescued by adding methanol or glycerol.

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GAO MinJie, DING Jian, ZHANG Xu, GAO Peng. Fault Diagnosis for Pichia pastoris Fermentation Based on a Hybrid Support Vector Machine and Fuzzy Reasoning Technique[J]. Journal of Food Science and Biotechnology,2014,33(11):1182-1190.

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Online: December 19,2014
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