基于区间二型模糊神经网络污水处理过程溶解氧浓度控制

doi:10.11949/j.issn.0438-1157.20171454

化工学报 ›› 2018, Vol. 69 ›› Issue (3): 1182-1190.DOI: 10.11949/j.issn.0438-1157.20171454

基于区间二型模糊神经网络污水处理过程溶解氧浓度控制

韩红桂^1,2, 刘峥^1,2, 乔俊飞^1,2

1 北京工业大学信息学部, 北京 100124;
2 计算智能与智能系统北京市重点实验室, 北京 100124

收稿日期:2017-11-01 修回日期:2017-11-26 出版日期:2018-03-05 发布日期:2018-03-05
通讯作者: 韩红桂
基金资助:
国家自然科学基金项目（61622301，61533002）；北京市自然科学基金项目（4172005）；科技部水专项（2017ZX07104）；中国博士后科学基金资助项目（2014M550017）；北京市教委项目（km201410005001，KZ201410005002）。

Control dissolved oxygen in wastewater treatment by interval type-2 fuzzy neural networks

HAN Honggui^1,2, LIU Zheng^1,2, QIAO Junfei^1,2

1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2 Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China

Received:2017-11-01 Revised:2017-11-26 Online:2018-03-05 Published:2018-03-05
Supported by:
supported by the National Natural Science Foundation of China (61622301, 61533002), the Beijing Natural Science Foundation (4172005), the Major National Science and Technology Project (2017ZX07104), the China Postdoctoral Science Foundation funded project (2014M550017) and the Beijing Municipal Education Commission Science and Technology Development Program (km201410005001, KZ201410005002).

摘要/Abstract

摘要：

针对城市污水处理过程溶解氧浓度难以精确控制的问题，提出了一种基于区间二型模糊神经网络（interval type-2 fuzzy neural networks，IT2FNN）的溶解氧浓度控制方法。先将IT2FNN应用在城市污水处理过程溶解氧浓度控制器的设计，获得了一种IT2FNN溶解氧浓度控制器。后采用自适应学习算法在线调整控制器的参数，提高了控制器的自适应能力。最后将提出的IT2FNN溶解氧浓度控制器应用于基准仿真2号模型（benchmark simulation model no.2，BSM2）平台，结果表明，IT2FNN控制器能够实现第5分区溶解氧浓度精确控制，具有较好的控制效果。

关键词: 污水处理过程, 溶解氧, 过程控制, 神经网络, 区间二型神经网络, 实验验证, 基准仿真2号模型

Abstract:

An intelligent controller, based on interval type-2 fuzzy neural networks (IT2FNN) was proposed for controlling dissolved oxygen (DO) concentration in municipal wastewater treatment processes. First, IT2FNN was applied to design a DO concentration controller. Second, an adaptive learning algorithm was used to online adjust controller parameters such that self-adaptability of the IT2FNN-based DO controller could be improved. Finally, IT2FNN-based DO controller was tested in the benchmark simulation model no. 2 (BSM2). The experimental results demonstrate that the controller is able to accurately monitor DO concentration in the fifth unit and maintain excellent control.

Key words: wastewater treatment process, dissolved oxygen, process control, neural network, interval type-2 fuzzy neural network, experimental validation, benchmark simulation model no.2

中图分类号:

TP173

韩红桂, 刘峥, 乔俊飞. 基于区间二型模糊神经网络污水处理过程溶解氧浓度控制[J]. 化工学报, 2018, 69(3): 1182-1190.

HAN Honggui, LIU Zheng, QIAO Junfei. Control dissolved oxygen in wastewater treatment by interval type-2 fuzzy neural networks[J]. CIESC Journal, 2018, 69(3): 1182-1190.

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基于区间二型模糊神经网络污水处理过程溶解氧浓度控制

Control dissolved oxygen in wastewater treatment by interval type-2 fuzzy neural networks

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