Research on Prediction Method of Sludge Bulking Based on ANN and Grey Markov Model

被引：0
|
作者：
Yu Guang-ping [1 ,2 ]
Wang Jing-yang [1 ]
Yuan Ming-zhe [1 ,2 ]
Yu Yang [1 ]
机构：
[1] Chinese Acad Sci, Shenyang Inst Automat, Guangzhou, Guangdong, Peoples R China
[2] Chinese Acad Sci, Shenyang Inst Automat, Shenyang, Liaoning Provin, Peoples R China
来源：
2015 IEEE INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER) | 2015年
关键词：
Prediction of sludge bulking; Rough set theory; Artificial Neural Networks; Soft measurement Technique; NEURAL-NETWORK;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Sludge volume index (SVI) can evaluate and reflect the aggregation of activated sludge sediment properties accurately. It is an important parameter to predict sludge hulking. Generally, if SVI value is too high, the description is sludge settling performance is poor. It will occur or has occurred sludge hulking. But SVI cannot he online measurement, offline assay data obtained for a long time or other issues. To solve this problem, this paper has applied soft -sensing technology for the sludge volume index that reflects sludge bulking, using rough set to reduce the instrumental variables then construct the soft sensing model with RBF neural network to complete the dataset of sludge volume index, and then, employed the grey Markov model to predict the dataset to collect the important information of sludge bulking in the quantitative respect, in order to achieve real-time prediction of sludge bulking.
引用
收藏
页码：1622 / 1627
页数：6
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