Prediction of the product formation with the AdaBoost algorithm in bioprocesses. (2017)

• Record Type: Journal Article
• Authors: Cui, Lei; Wang, Zhifeng; Xu, Tao; Song, Haihui
• Is Part Of: International journal of system control and information processing. Volume 2:Number 2(2017)
• Page Start: 180
• Page End: 189
• Publication Date: 2017
• Subjects: SVM -- support vector machine -- fermentation process -- AdaBoost -- prediction; System design -- Data processing -- Periodicals; Information technology -- Periodicals; 003.5
• Languages: English
• ISSNs: 1759-9334

Abstract

The state variables such as product formation could provide important information for the optimisation of fermentation processes. Since the kinetic modelling is difficult for bioprocesses, the product formation is predicted by integrating support vector machine (SVM) with the AdaBoost algorithm. The AdaBoost algorithm is used for adaptively boosting the performance of SVM weak learners. The prediction approach is tested by using 2-keto-L-gulonic acid (2-KGA) cultivation as an example. The validation results using the data from industrial 2-KGA cultivation demonstrate that the prediction approach has good generalisation performance and noise tolerance.