DocumentCode
2914329
Title
Research of sludge compost maturity degree modeling method based on classify support vector machine for sewage treatment
Author
Tian, Jingwen ; Gao, Meijuan ; Zhou, Hao
Author_Institution
Beijing Union Univ., Beijing
fYear
2007
fDate
18-20 Nov. 2007
Firstpage
1122
Lastpage
1127
Abstract
Because of the complicated interaction of the sludge compost components, it makes the judging system of sludge compost maturity degree appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a sludge compost maturity degree modeling method based on support vector machine (SVM) is presented. We select the index of sludge compost maturity degree and take the high temperature duration, moisture content, volatile solids, the value of fecal bacteria, and germination index as the judgment parameters. We construct the structure of SVM network that used for the maturity degree judgment of sludge compost, and use the genetic algorithm (GA) to optimize SVM parameters. With the ability of strong self-learning and well generalization of SVM, the modeling method can truly judge the sludge compost maturity degree by learning the index information of sludge compost maturity degree. The experimental results show that this method is feasible and effective.
Keywords
environmental science computing; genetic algorithms; learning (artificial intelligence); sewage treatment; sludge treatment; support vector machines; genetic algorithm; maturity degree judgment; self-learning; sewage treatment; sludge compost maturity degree modeling method; support vector machine; Artificial neural networks; Microorganisms; Moisture; Organisms; Pathogens; Sewage treatment; Soil; Support vector machine classification; Support vector machines; Temperature;
fLanguage
English
Publisher
ieee
Conference_Titel
Grey Systems and Intelligent Services, 2007. GSIS 2007. IEEE International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-1294-5
Electronic_ISBN
978-1-4244-1294-5
Type
conf
DOI
10.1109/GSIS.2007.4443447
Filename
4443447
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