Title: Prediction of the product formation with the AdaBoost algorithm in bioprocesses

Authors: Lei Cui; Zhifeng Wang; Tao Xu; Haihui Song

Addresses: College of Engineering, Shanghai Polytechnic University, 2360 Jinhai Road, 201209 Shanghai, China ' College of Engineering, Shanghai Polytechnic University, 2360 Jinhai Road, 201209 Shanghai, China ' College of Engineering, Shanghai Polytechnic University, 2360 Jinhai Road, 201209 Shanghai, China ' College of Engineering, Shanghai Polytechnic University, 2360 Jinhai Road, 201209 Shanghai, China

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.

Keywords: SVM; support vector machine; fermentation process; AdaBoost; prediction.

DOI: 10.1504/IJSCIP.2017.089816

International Journal of System Control and Information Processing, 2017 Vol.2 No.2, pp.180 - 189

Received: 19 Oct 2017
Accepted: 25 Oct 2017
Published online: 12 Feb 2018 *

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