Prediction of activated sludge bulking based on recurrent fuzzy neural network

XU Shaopeng; HAN Honggui; QIAO Junfei

doi:10.3969/j.issn.0438-1157.2013.12.042

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Prediction of activated sludge bulking based on recurrent fuzzy neural network

更新时间：2025-12-10

- Prediction of activated sludge bulking based on recurrent fuzzy neural network
- CIESC Journal Issue 12, Pages: 4550-4556(2013)
- 作者机构：
  
  北京工业大学电子信息与控制工程学院,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2013.12.042
  CLC： 703TP11
- Online First：05 December 2013，
  
  Published：05 December 2013
- 稿件说明：
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XU Shaopeng, HAN Honggui, QIAO Junfei. Prediction of activated sludge bulking based on recurrent fuzzy neural network[J]. CIESC Journal, 2013, (12): 4550-4556.
DOI：

XU Shaopeng, HAN Honggui, QIAO Junfei. Prediction of activated sludge bulking based on recurrent fuzzy neural network[J]. CIESC Journal, 2013, (12): 4550-4556. DOI： 10.3969/j.issn.0438-1157.2013.12.042.

摘要

污泥容积指数（SVI），一个关键的污泥沉降性能评价指标。针对污水处理过程中污泥膨胀关键水质参数污泥容积指数难以准确在线测量，且实验室取样测量方法时间久、精度低，提出了一种改进型的模糊递归神经网络（HRFNN）用来预测污泥容积指数的变化，通过在网络第三层加入含有内部变量的反馈连接来实现输出信息的反馈。实验结果表明，与其他模糊神经网络相比，该网络的规模小、精度高，处理动态信息的能力明显加强。

Abstract

Sludge volume index (SVI)

a key sludge sedimentation performance evaluation index

is difficult to be obtained accurately online and the conventional approaches are time-consuming

tedious and complicated.A new recurrent fuzzy neural network (HRFNN) method is proposed in this paper to predict the evolution of the sludge volume index (SVI).HRFNN is constructed by adding feedback connections with the internal variable in the third layer of the fuzzy neural network

so it achieves output information feedback.Finally

the results of simulation indicate the efficiency of the modeling method.And compared with other fuzzy neural networks

the scale of network can be simplified and its capability of dealing with dynamic information can be strengthened

it also has better accuracy.

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