An Optimal Control Strategy Combining SVM with RGA for Improving Fermentation Titer

An optimal control strategy is proposed to improve the fermentation titer, which combines the support vector machine (SVM) with real code genetic algorithm (RGA). A prediction model is established with SVM for penicillin fermentation processes, and it is used in RGA for fitting function. A control pattern is proposed to overcome the coupling problem of fermentation parameters, which describes the overall production condition. Experimental results show that the optimal control strategy improves the penicillin titer of the fermentation process by 22.88%, compared with the routine operation.