Prediction of activated sludge bulking based on recurrent fuzzy neural network

doi:10.3969/j.issn.0438-1157.2013.12.042

CIESC Journal ›› 2013, Vol. 64 ›› Issue (12): 4550-4556.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

XU Shaopeng, HAN Honggui, QIAO Junfei

College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China

Received:2013-08-13 Revised:2013-08-27 Online:2013-12-05 Published:2013-12-05
Supported by:
supported by the National Natural Science Foundation of China (61203099，61034008，61225016)，the Beijing Municipal Natural Science Foundation (4122006)，the Ph.D.Program Foundation from Ministry of Education (20121103120020) and the Beijing Nova Program (Z131104000413007).

基于模糊递归神经网络的污泥容积指数预测模型

许少鹏, 韩红桂, 乔俊飞

北京工业大学电子信息与控制工程学院, 北京 100124

通讯作者: 韩红桂
作者简介:许少鹏（1986- ），男，硕士研究生。
基金资助:
国家自然科学基金项目（61203099，61034008，61225016）；北京市自然科学基金项目（4122006）；教育部博士点新教师基金项目（20121103120020）；北京市科技新星计划项目（Z131104000413007）；香江学者计划项目（XJ2013018）。

Abstract

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.

Key words: sludge bulking, sludge volume index, wastewater treatment process, recurrent fuzzy neural network

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。

关键词: 污泥膨胀, 污泥容积指数, 污水处理过程, 模糊递归神经网络

CLC Number:

X703
TP11

XU Shaopeng, HAN Honggui, QIAO Junfei. Prediction of activated sludge bulking based on recurrent fuzzy neural network[J]. CIESC Journal, 2013, 64(12): 4550-4556.

许少鹏, 韩红桂, 乔俊飞. 基于模糊递归神经网络的污泥容积指数预测模型[J]. 化工学报, 2013, 64(12): 4550-4556.

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Prediction of activated sludge bulking based on recurrent fuzzy neural network

基于模糊递归神经网络的污泥容积指数预测模型

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