CIESC Journal ›› 2016, Vol. 67 ›› Issue (3): 954-959.DOI: 10.11949/j.issn.0438-1157.20151898

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Wastewater treatment control method based on recurrent fuzzy neural network

HAN Gaitang1,2, QIAO Junfei1,2, HAN Honggui1,2   

  1. 1. College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China;
    2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
  • Received:2015-12-14 Revised:2015-12-24 Online:2016-01-12 Published:2016-03-05
  • Contact: 67
  • Supported by:

    supported by the National Natural Science Foundation of China (61533002), the National Natural Science Foundation for Distinguished Young Scholars of China (61225016), the First Class Program Foundation from China Postdoctoral Science Foundation (2014M550017), Beijing Municipal Education Commission Science and Technology Development Program (KZ201410005002, km201410005001), the Collaborative Innovation Program (ZH14000177) and the Specialized Research Fund for the Doctoral Program of China (20131103110016).

基于递归模糊神经网络的污水处理控制方法

韩改堂1,2, 乔俊飞1,2, 韩红桂1,2   

  1. 1. 北京工业大学电子信息与控制工程学院, 北京 100124;
    2. 计算智能与智能系统北京市重点实验室, 北京 100124
  • 通讯作者: 韩改堂
  • 基金资助:

    国家自然科学基金重点项目(61533002);国家自然科学基金杰出青年项目(61225016);中国博士后科学基金一等资助项目(2014M550017);北京市教育委员会科研计划项目(KZ201410005002,km201410005001);北京市朝阳区协同创新项目(ZH14000177);高等学校博士学科点专项科研基金(20131103110016)。

Abstract:

Due to the nonlinear and highly time-varying issues of wastewater treatment processes, a kind of multi-variable control method based on the recurrent fuzzy neural network (RFNN) is proposed. The proposed RFNN can obtain self-adaptive control accuracy of operating variables. The controller uses the learning rate on the basis of conventional BP learning algorithm on adaptive learning algorithm and the introduction of momentum to train network parameters, can avoid falling into local optimum network, which improved network control of the system accuracy. Finally, based on the benchmark simulation model (BSM1), experiments validate the effectiveness of the method that control the dissolved oxygen concentration in the fifth partition and nitrate nitrogen concentration in the second partition. Compared to PID, forward neural network and conventional recurrent neural network, the experimental results show that this control method can improve the adaptive control precision of the system.

Key words: wastewater treatment process, recurrent fuzzy neural network, control, adaptive learning algorithm, dissolved oxygen, nitrate nitrogen, dynamic simulation

摘要:

针对污水处理过程具有非线性、大时变等问题,提出了一种基于递归模糊神经网络的多变量控制方法。该方法通过递归模糊神经网络控制器自适应地获得对操作变量的控制精度,控制器在常规BP学习算法的基础上采用学习率自适应学习算法且引入了动量项来训练网络参数,避免网络陷入局部最优,提高了网络对系统的控制精度。最后,基于仿真基准模型(BSM1)平台对第五分区中的溶解氧和第二分区中的硝态氮控制进行动态仿真实验,结果表明,与PID、前馈神经网络和常规递归神经网络相比,该方法能有效提高系统的自适应控制精度。

关键词: 污水处理过程, 递归模糊神经网络, 控制, 自适应学习算法, 溶解氧 , 硝态氮, 动态仿真

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