神经网络自适应动态规划在污水处理过程控制中的应用  

Application of Neural Network-based Adaptive Dynamical Programming in Wastewater Treatment Contol

作　　者：刘文波[1] 王霞[2] 李来鸿[1] 薄迎春[3] 

机构地区：[1]胜利油田电力管理总公司河口供电公司,山东东营257200 [2]北京钢研新冶电气股份有限公司,北京100094 [3]中国石油大学信息与控制工程学院,山东青岛266555

出　　处：《化工自动化及仪表》2012年第11期1437-1441,共5页Control and Instruments in Chemical Industry

摘　　要：针对污水处理过程的溶解氧及硝态氮浓度控制问题,提出一种基于神经网络的自适应动态规划(Neural network-based adaptive dynamical programming,NNADP)方法。该方法采用神经网络逼近当前策略的评价函数以及最优的控制策略。采用梯度下降算法对各神经网络权值进行在线训练。基于污水处理过程国际标准模型BSM1(Benchmark Simulation Model no.1)对NNADP控制性能进行了测试,结果表明:与PID控制相比,NNADP具有较强的解耦能力,控制精度也有较大提高。Considering the control of dissolved oxygen and nitrate nitrogen concentration in wastewater treatment plant(WWTP),a neural network-based adaptive dynamical programming(NNADP) scheme was proposed, which employs neural network to approximate the existing scheme' s evaluation function and the optimal control policy,and makes use of gradient descent-based online algorithm to train the weights of neural networks. Testing NNADP's control performance with BSM1(Benchmark Simulation Model No.1) indicates that NNADP has strong decoupling ability and higher control precision in comparison with PID.

关 键 词：自适应动态规划 污水处理 回声状态网络 解耦能力 

分 类 号：TH862+.6[机械工程—仪器科学与技术]