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Prediction of product formation in 2-keto-l-gulonic acid fermentation through Bayesian combination of multiple neural networks

Paper ID Volume ID Publish Year Pages File Format Full-Text
34781 45048 2014 7 PDF Available
Title
Prediction of product formation in 2-keto-l-gulonic acid fermentation through Bayesian combination of multiple neural networks
Abstract

•Prediction of product formation is achieved in the industrial 2-KGA fermentation.•High-accuracy prediction is achieved by Bayesian combination of three ANNs.•The combination weights can be interpreted as Bayesian posterior probabilities.•Historical batches are classified into three categories with the proposed algorithm.•Each neural network is featured with its corresponding training database.

As the key precursor for l-ascorbic acid synthesis, 2-keto-l-gulonic acid (2-KGA) is widely produced by the mixed culture of Bacillus megaterium and Ketogulonicigenium vulgare. In this study, a Bayesian combination of multiple neural networks is developed to obtain accurate prediction of the product formation. The historical batches are classified into three categories with a batch classification algorithm based on the statistical analysis of the product formation profiles. For each category, an artificial neural network is constructed. The input vector of the neural network consists of a series of time-discretized process variables. The output of the neural network is the predicted product formation. The training database for each neural network is composed of both the input–output data pairs from the historical bathes in the corresponding category, and all the available data pairs collected from the batch of present interest. The prediction of the product formation is practiced through a Bayesian combination of three trained neural networks. Validation was carried out in a Chinese pharmaceutical factory for 140 industrial batches in total, and the average root mean square error (RMSE) is 2.2% and 2.6% for 4 h and 8 h ahead prediction of product formation, respectively.

Keywords
2-Keto-l-gulonic acid; Mixed culture; Batch classification; Product formation; Bayesian combined neural networks
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Prediction of product formation in 2-keto-l-gulonic acid fermentation through Bayesian combination of multiple neural networks
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Publisher
Database: Elsevier - ScienceDirect
Journal: Process Biochemistry - Volume 49, Issue 2, February 2014, Pages 188–194
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us