Optimization of Nosiheptide Fed-Batch Fermentation Process Based on Hybrid Model

Nosiheptide, a sulfur-containing peptide antibiotic obtained through fermentation, is a perfect feed additive, but its yield in industry is not high. Process optimization is a good way to increase nosiheptide yield, maintaining the optimum operating conditions of the fermentation process, while optimization of the process requires a sufficiently accurate and robust process model. In this paper, the mechanism model for nosiheptide fed-batch fermentation is first established. Then, in order to improve performance of the mechanism model, a hybrid model is built using least-squares support vector machines to compensate the errors between the mechanism model and the process. The hybrid model not only overcomes pure black-box model's shortcoming that it often has poor generalization ability but improves the mechanism model's accuracy. A yield optimization model of nosiheptide fed-batch fermentation process is then established based on the hybrid model. An improved particle swarm optimization algorithm is used to solve the optimization model, greatly improving the end nosiheptide production, which also proves the validity of the proposed particle swarm optimization algorithm.