Decision model for coagulant dosage using genetic programming and multivariate statistical analysis for coagulation/flocculation at water treatment process
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- @Article{park:2008:KJCE,
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author = "Sooyee Park and Hyeon Bae and Changwon Kim",
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title = "Decision model for coagulant dosage using genetic
programming and multivariate statistical analysis for
coagulation/flocculation at water treatment process",
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journal = "Korean Journal of Chemical Engineering",
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year = "2008",
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volume = "25",
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number = "6",
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pages = "1372--1376",
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keywords = "genetic algorithms, genetic programming, Coagulant
Dosage, Multivariate Statistical Analysis, Water
Treatment Plan",
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URL = "http://link.springer.com/article/10.1007/s11814-008-0225-9",
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DOI = "doi:10.1007/s11814-008-0225-9",
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size = "5 pages",
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abstract = "In this research, genetic programming and multivariate
statistical analysis techniques have been applied for
decision support on the coagulant dosage and the mixing
ratio as two kinds of coagulants have been injected at
the same time in the coagulating sedimentation process
of water treatment. The coagulant dosage has typically
been determined through the Jar-test, which requires a
long experiment time in a field-water treatment plant.
It is difficult to efficiently determine the coagulant
dosage since water quality changes with time. As there
are no human experts who have sufficient knowledge and
experience in the field, coagulants may be injected
with an improper mixing ratio, which causes poor
performance in the coagulating sedimentation process.
In this study, a model for the approximation of
coagulant dosage has been developed using genetic
programming (GP). The performance of this model was
evaluated through validation. A guideline on the
optimal mixing ratio between PACS (Poly Aluminum
Chloride Silicate) and PAC (Poly Aluminum Chloride) has
been provided through statistical analysis.",
- }
Genetic Programming entries for
Sooyee Park
Hyeon Bae
Changwon Kim
Citations