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

HAN Honggui; CHEN Zhiyuan; QIAO Junfei; ZHANG Huiqing

doi:10.11949/j.issn.0438-1157.20161613

您当前的位置：

首页 >

文章列表页 >

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

更新时间：2025-12-10

- Soft-sensor method for effluent ammonia nitrogen based on interval type-2 fuzzy neural networks
- CIESC Journal Issue 3, Pages: 1032-1040(2017)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20161613
  CLC： P173
- Online First：05 March 2017，
  
  Published：05 March 2017
- 稿件说明：
移动端阅览
HAN Honggui, CHEN Zhiyuan, QIAO Junfei, et al. Soft-sensor method for effluent ammonia nitrogen based on interval type-2 fuzzy neural networks[J]. CIESC Journal, 2017, (3): 1032-1040.
DOI：

HAN Honggui, CHEN Zhiyuan, QIAO Junfei, et al. Soft-sensor method for effluent ammonia nitrogen based on interval type-2 fuzzy neural networks[J]. CIESC Journal, 2017, (3): 1032-1040. DOI： 10.11949/j.issn.0438-1157.20161613.

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。

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.

关键词

Keywords

references

Views

299

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

TTPA-LSTM soft sensor modeling for multi-sampling rate data

Soft-sensor method based on JIT-MOSVR and its application

Soft-sensor modeling for ethylene distillation product quality based on vector projection metabolism support vector machine

Physics-informed spatio-temporal graph convolutional network for fermentation processes

Performance study of gas-liquid vortex separators under low cross-sectional gas velocity

Related Author

WANG Fazheng

SUI Lin

XIONG Weili

WANG Shijie

WANG Zhenlei

WANG Xin

ZHENG Boyuan

SU Chengli

Related Institution

School of Internet of Things Engineering, Jiangnan University

Key Laboratory of Advanced Process Control for Industry, Ministry of Education, Jiangnan University

Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology

Center of Electrical & Electronic Technology, Shanghai Jiao Tong University

School of Information and Control Engineering, Liaoning Shihua University

⁰