基于区间二型模糊神经网络的出水氨氮软测量

doi:10.11949/j.issn.0438-1157.20161613

化工学报 ›› 2017, Vol. 68 ›› Issue (3): 1032-1040.DOI: 10.11949/j.issn.0438-1157.20161613

基于区间二型模糊神经网络的出水氨氮软测量

韩红桂^1,2, 陈治远^1,2, 乔俊飞^1,2, 张会清^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:2016-11-15 修回日期:2016-11-25 出版日期:2017-03-05 发布日期:2017-03-05
通讯作者: 韩红桂,1307441474@emails.bjut.edu.cn
基金资助:
国家自然科学基金项目（61622301，61533002）；北京市教育委员会科研计划项目（KZ201410005002，km201410005001）。

Soft-sensor method for effluent ammonia nitrogen based on interval type-2 fuzzy neural networks

HAN Honggui^1,2, CHEN Zhiyuan^1,2, QIAO Junfei^1,2, ZHANG Huiqing^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:2016-11-15 Revised:2016-11-25 Online:2017-03-05 Published:2017-03-05
Contact: 10.11949/j.issn.0438-1157.20161613
Supported by:
supported by the National Natural Science Foundation of China (61622301,61533002) and the Beijing Municipal Education Commission Science and Technology Development Program (KZ201410005002,km201410005001).

摘要/Abstract

摘要：

针对污水处理过程出水氨氮（ammonia nitrogen，NH₄-N）难以实时检测的问题，提出了一种基于区间二型模糊神经网络（interval type-2 fuzzy neural networks，IT2FNN）的软测量方法，建立了出水NH₄-N的软测量模型，实现了出水NH₄-N的实时检测。首先，采集和预处理相关过程变量的实际运行数据，通过主元分析法筛选出与出水NH₄-N相关性较强的过程变量。其次，利用IT2FNN建立所选变量与出水NH₄-N的软测量模型，通过梯度下降算法对模型相关参数进行修正。最后，将基于IT2FNN的出水NH₄-N软测量模型应用于实际污水处理过程。实验结果表明，提出的出水NH₄-N软测量方法不仅能够实现污水处理过程出水NH₄-N的实时检测，而且具有较高的检测精度。

关键词: 出水氨氮, 软测量, 区间二型模糊神经网络, 动态建模, 污水处理过程

Abstract:

A soft-sensor method for online detection of effluent ammonia nitrogen (NH₄-N) in waste water treatment process was proposed on the basis of interval type-2 fuzzy neural networks (IT2FNN). First, actual operation data related to pre-treatment process variables was collected and process variables having strong correlation to effluent NH₄-N were selected by principal component analysis (PCA) technique. Second, a self-sensor model between principal component variables and effluent NH₄-N was established via IT2FNN and model parameters were adjusted by gradient algorithm. Finally, the proposed soft-sensor method was used in a real waste water treatment process (WWTP). The experimental results show that the new method can predict effluent NH₄-N online with better accuracy than traditional methods.

Key words: effluent ammonia nitrogen, soft-sensor, interval type-2 fuzzy neural network, dynamic modeling, waste water treatment process

中图分类号:

TP173

韩红桂, 陈治远, 乔俊飞, 张会清. 基于区间二型模糊神经网络的出水氨氮软测量[J]. 化工学报, 2017, 68(3): 1032-1040.

HAN Honggui, CHEN Zhiyuan, QIAO Junfei, ZHANG Huiqing. Soft-sensor method for effluent ammonia nitrogen based on interval type-2 fuzzy neural networks[J]. CIESC Journal, 2017, 68(3): 1032-1040.

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基于区间二型模糊神经网络的出水氨氮软测量

Soft-sensor method for effluent ammonia nitrogen based on interval type-2 fuzzy neural networks

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