Multi-objective Optimization for Fed-batch Fermentation Process Based on Reinforcement Learning

The features of the Fed-batch fermentation process contain strong nonlinearity, time-varying parameters, large time delays, and complicated real-time measurement of biological state variables. Thus, online controlling the yield, substrate and time consumption directly is difficult. Operating offline optimization becomes a main method to improve various objectives of production. Further, the complex optimization involves multiple, incommensurable

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