Abstract
An algorithm for the optimization of a fermentation process was investigated using the combination of dynamic programming and a linear predictive procedure by regression analysis. It was applied to the fed-batch culture for glutamic acid production with ethanol feeding and was proved to be effective for solving the optimization problems including on-line adaptive control. The effect of linear predictive error on the optimum policy was discussed by use of Fishman and Biryukov's model of penicillin production. The prediction method was modified for application to processes including unmeasurable state variables. The use of time-series data was proved to be effective for prediction of a fed-batch culture of baker's yeast.
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