Phase Identification–Based Prediction of Product Formation for 2-Keto-l-Gulonic Acid Fermentation Processes
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Because of the time-varying characteristics of 2-keto-l-gulonic acid (2-KGA) fermentation processes, phase identification–based prediction of product formation is presented in this paper. Firstly, based on the online measurement data of fermentation processes, fermentation stage is identified with the modified principal component analysis method. The state variable such as the product formation is predicted using the support vector machine (SVM) approach. The obtained stage information is fed back to the state variable prediction model. In different fermentation phases, the training databases and parameters of SVM for the prediction model are applied differently. With the data from industrial 2-KGA fermentation processes, pseudo-online prediction is practiced. The results indicate that the prediction approach has good performance.
KeywordsIndustrial fermentation process Principal component analysis Phase identification State variable prediction
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Conflict of Interest
The authors declare that they have no conflict of interest.
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