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

• 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.