基于改进ILC的蛋白酶发酵过程pH值控制方法  

pH value control method of protease MP fermentation process based on improved ILC

作　　者：赵海清 王博[1] 朱湘临[1] 朱熀秋[1] 郝建华 ZHAO Haiqing;WANG Bo;ZHU Xianglin;ZHU Huangqiu;HAO Jianhua(College of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China;Open Laboratory of Marine Enzyme&Enzyme Engineering,Yellow Sea Fisheries Research Institute,Qingdao 266071,China)

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013 [2]中国水产科学研究院黄海水产研究所海洋酶与酶工程开放实验室,山东青岛266071

出　　处：《传感器与微系统》2020年第2期7-10,共4页Transducer and Microsystem Technologies

基　　金：国家自然科学基金面上资助项目(41376175);江苏省自然科学基金资助项目(BK20151345);江苏省高校自然科学研究面上资助项目(14KJB510007)

摘　　要：针对海洋蛋白酶(MP)发酵过程中,产酶菌株YS-80酸碱度难以稳定控制在最优范围内的问题,提出一种基于改进迭代学习控制(ILC)的pH值控制方法。分析MP发酵过程动力学模型,确定控制变量;对BP神经网络进行训练和测试,得出迭代学习控制器的增益参数与控制误差之间的映射关系,采用优化后的人工鱼群算法(AFSA)实现增益参数的动态跟踪;建立MP发酵过程pH值迭代学习控制模型,解决YS-80产酸曲线和生长曲线未知的难题。仿真结果表明:该控制方法对基质浓度、菌体浓度的跟踪误差小于0.669 g/L,对相对酶活的跟踪误差小于0.98%,鲁棒性与响应速度良好。Aiming at the problem that the pH value of enzyme-producing strain YS-80 can not be controlled stably in the optimal range during the fermentation of marine protease(MP),a pH value control method based on improved iterative learning control(ILC)is proposed.Firstly,the dynamic model for MP fermentation process is analyzed and the control variables are determined.Then,the BP neural network is trained and tested to obtain the mapping relationship between the gain parameters and control errors of the iterative learning controller.The optimized artificial fish swarm algorithm(AFSA)is used to realize the dynamic tracking of the gain parameters.Finally,pH iterative learning control model for MP fermentation process is established to solve the problem of YS-80 acid producing curve and unknown growth curve.The simulation results show that the tracking error of this method on matrix concentration and bacterial concentration is less than 0.669 g/L,and the tracking error of relative enzyme activity is less than 0.98%.This control method has good robustness and response speed.

关 键 词：海洋蛋白酶 PH值 迭代学习控制(ILC) 人工鱼群算法(AFSA) 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]