Optimization and Control of Bioprocesses

Mathematical optimization always requires a deterministic process model to predict the future behavior of a process. However, as previously mentioned, it is difficult to construct mathematical models that can cover the entire range of fermentation due to the complexity of intracellular metabolic reactions. As an alternative to the deterministic mathematical models, Kishimoto et al. proposed a statistical procedirre that uses linear multiple regression models [7], as shown below, instead of a deterministic mathematical model such as a Monod equation. 

and p represent a specific growth rate, a specific substrate consumption rate, and a specific product formation rate, respectively. and are the mean values of data used for regression analysis and a, bp and C are the coefficients in the regression models that are determined based on selected operating data in a database. This model was linked with the dynamic programming method and successfully applied to the simulation and onhne optimization of glutamic acid production and Baker s yeast production. 

Application of Artificial Intelligence (Al) Technology to Bioprocess Control 

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