摘要：Abstract Fed-batch fermentation process is an effective method for production. Due to the various feeding streams andoperational conditions in different fed-batches, usually it is difficult to formulate a kinetics-based ordinary differential equations model for industrial fed-batch fermentation process. On the other hand, there are plenty of historical data collected during the fermentation process. In this paper, we firstly applied the graphical modeling method to model the fed-batch fermentation process. In this proposed method, the missing data within records are imputed, and then, the correlations between variables are determined by the low order conditional independence method, after that, the parameters of these related variables are learned by the multivariate auto regressive method. The calculation of L-lysine fed-batch fermentation process demonstrates the effectiveness of the proposed approximate model method.
关键词：Keywordsdynamic Bayesian networkGaussian approximatemissing data imputationprocess modellingfed-batch fermentation
