An approximate modelling method for industrial l-lysine fermentation process

l-lysine is an important chemical, usually produced by fed-batch fermentation process. Usually, feed stock compositions, reactant or product concentrations, and operating conditions vary with different fed-batches in this process. It is difficult to establish a kinetics-based model for an industrial fed-batch fermentation process. In this paper, we proposed a data-based approximate graphical modelling method to model this process. Variables values are treated as correlated Gaussian process. The methodology comprises of two important steps: i) the missing-data imputation within records, and ii) the dynamic Bayesian network learning, including structure learning, using the low order conditional independence method, and parameters learning, using the multivariate auto regressive method. The l-lysine fed-batch fermentation process is studied to demonstrate the effectiveness of this approximate modelling method.