氨糖发酵过程建模与工艺参数优化研究

Research on Modeling and Process Parameters Optimization of GlcN Fermentation Process

针对目前生物传感器价格昂贵且检测精度低使得在氨糖发酵过程中难以获得准确实时的生物参数的现状,建立了最小二乘支持向量机模型以实现菌体浓度、产物浓度、底物浓度的预测。为了提高预测模型的精度,采用基于Levy飞行的改进多元宇宙算法对最小二乘支持向量机模型的若干参数进行优化。在此模型的基础上,以发酵完成时刻产物浓度最大为目标,通过改进的多元宇宙优化算法对发酵工艺参数进行了优化。仿真实验表明该方法取得了较高建模精度,提高了发酵最终产物浓度。

In view of the high price and low detection accuracy of biosensors,which makes it difficult to obtain accurate and real-time biological parameters in the process of GlcN fermentation,the Least Square Support Vector Machine(LSSVM)model is established to predict the cell concentration,product concentration and substrate concentration.In order to improve the accuracy of the prediction model,the improved multiverse optimization algorithm based on Levy flight is utilized to optimize several parameters of the LSSVM model.On the basis of the model aiming at the maximum product concentration at the time of fermentation completion,the fermentation process parameters are optimized by the improved multiverse optimization algorithm.The simulation results show that the method achieves higher modeling accuracy and improves the final fermentation product concentration.