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Data-driven prediction of the product formation in industrial 2-keto-l-gulonic acid fermentation

Authors
Journal
Computers & Chemical Engineering
0098-1354
Publisher
Elsevier
Publication Date
Volume
36
Identifiers
DOI: 10.1016/j.compchemeng.2011.06.012
Keywords
  • 2-Keto-L-Gulonic Acid
  • Mixed Culture
  • Neural Networks
  • Product Formation
  • Rolling Learning-Prediction
Disciplines
  • Computer Science

Abstract

Abstract Mixed culture fermentation of Bacillus megaterium and Gluconobacter oxydans is widely used to produce 2-keto- l-gulonic acid (2-KGA), a key precursor for l-ascorbic acid synthesis. For such mixed cultivation, kinetic modelling is difficult because the interactions between the two strains are not well known yet. In this paper, data-driven prediction of the product formation is presented for the purpose of better process monitoring. A rolling learning-prediction approach based on neural networks is practiced to predict 2-KGA formation. Techniques associated with the approach, such as the data pretreatment and the rolling learning-prediction mechanism, are given in more detail. The validation results by using the data from commercial scale 2-KGA cultivation indicate that the prediction error is less than 5% in the later phase of fermentation and the reliable prediction time span is 8 h. The robustness of the prediction approach is further tested by adding extra noises to the process variables.

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