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Research on prediction method of sludge bulking based on ANN and grey Markov model
Yu GP(于广平); Wang JY(王景杨); Yuan MZ(苑明哲); Yu Y(郁洋)
作者部门信息服务与智能控制技术研究室
会议名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
会议日期June 8-12, 2015
会议地点Shenyang, China
会议录名称2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)
出版者IEEE
出版地Piscataway, NJ, USA
2015
页码1622-1627
收录类别EI ; CPCI(ISTP)
EI收录号20161402187766
WOS记录号WOS:000380502300295
产权排序1
ISSN号2379-7711
ISBN号978-1-4799-8730-6
关键词Prediction Of Sludge Bulking Rough Set Theory Artificial Neural Networks Soft Measurement Techniques
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 bulking. Generally, if SVI value is too high, the description is sludge settling performance is poor. It will occur or has occurred sludge bulking. But SVI cannot be 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.

语种英语
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被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/17377
专题信息服务与智能控制技术研究室
作者单位1.Shenyang Institute of Automation, Guangzhou, Chinese Academy of Science, Guangzhou, Guangdong Province, China
2.Shenyang Institute of Automation, Chinese Academy of Science, Shenyang, Liaoning Province, China
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Yu GP,Wang JY,Yuan MZ,et al. Research on prediction method of sludge bulking based on ANN and grey Markov model[C]. Piscataway, NJ, USA:IEEE,2015:1622-1627.
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