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基于类脑模块化神经网络的污水处理过程关键出水参数软测量
引用本文:蒙西, 乔俊飞, 韩红桂. 基于类脑模块化神经网络的污水处理过程关键出水参数软测量. 自动化学报, 2019, 45(5): 906-919. doi: 10.16383/j.aas.2018.c170497
作者姓名:蒙西  乔俊飞  韩红桂
作者单位: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
基金项目:国家自然科学基金61622301国家自然科学基金61533002北京市自然科学基金项目4172005
摘    要:针对城市污水处理过程关键出水参数难以实时检测的问题,文中提出了一种基于类脑模块化神经网络(Brain-like modular neural network,BLMNN)的关键出水参数软测量方法.首先,基于互信息和专家知识进行任务分解,分析关键出水参数的相关变量,获取各出水参数的辅助变量.其次,通过模拟大脑皮层模块化分区结构,构建软测量子模型对各水质参数进行同步测量,降低软测量模型复杂度的同时保证了其精度.最后,通过基于实际数据的仿真实验验证了所提出方法的准确性和有效性.

关 键 词:污水处理过程   关键出水参数   软测量   类脑模块化神经网络
收稿时间:2017-09-01

Soft Measurement of Key Effluent Parameters in Wastewater Treatment Process Using Brain-like Modular Neural Networks
MENG Xi, QIAO Jun-Fei, HAN Hong-Gui. Soft Measurement of Key Effluent Parameters in Wastewater Treatment Process Using Brain-like Modular Neural Networks. ACTA AUTOMATICA SINICA, 2019, 45(5): 906-919. doi: 10.16383/j.aas.2018.c170497
Authors:MENG Xi  QIAO Jun-Fei  HAN Hong-Gui
Affiliation:1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124;;2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124
Abstract:With the goal to realize the real-time measurement of key water quality parameters in wastewater treatment process, this paper constructs a novel soft-measurement model based on the brain-like modular neural network (BLMNN). First, based on the mutation information and expert knowledge, the easy-to-measure variables which have strong correlations to the effluent water quality parameters are chosen as the model inputs. Then, simulating the modular structure of brain cortex, the effluent water parameters are measured by different sub-models, improving both the modeling accuracy and modeling speed. The simulation results based on real data verify the accuracy and effectiveness of the proposed method.
Keywords:Wastewater treatment process  key effluent parameters  soft-measurement  brain-like modular neural networks
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