摘要
针对发酵过程的非线性、时变等特点,建立了基于BP神经网络的产物浓度预估模型,并在此模型的基础上,利用改进的粒子群算法(PSO)实现对L-天冬酰胺酶II发酵过程控制参数的寻优,以确定该过程的优化轨线,通过在线调整实现对优化轨线的跟踪控制,实验应用结果表明效果良好。
In accordance with the feature of non-linear and time varying for ferment procesS, the neural network model is established to estimate the concentration of product. Based on this model, PSO algorithm is applied to reach the optimum and optimal trajectory for the ferment process of L-Asparaginase I1. By online adjustment, the tracing control to the optimal trajectory is implemented. The trial application shows that the effect is well obviously.
出处
《化工自动化及仪表》
CAS
2007年第2期7-9,共3页
Control and Instruments in Chemical Industry
基金
江苏省自然科学基金项目(BK2005012)
关键词
PSO算法
发酵过程
优化控制
PSO algorithm
ferment process
optimization control
