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A river water quality management model for optimising regional wastewater treatment using a genetic algorithm [An article from: Journal of Environmental Management]

Author J.H. Cho, K. Seok Sung, S. Ryong Ha
Publisher Elsevier
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Book Details
PublisherElsevier
ISBN / ASINB000RR0O6G
ISBN-13978B000RR0O64
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸

Description

This digital document is a journal article from Journal of Environmental Management, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
To achieve water quality goals and wastewater treatment cost optimisation in a river basin, a water quality management model has been developed through the integration of a genetic algorithm (GA) and a mathematical water quality model. The developed model has been applied to the Youngsan River, where water quality has decreased due to heavy pollutant loads from Kwangju City and surrounding areas. Pollution source, land use, geographic features and measured water quality data of the river basin were incorporated into the Arc/View geographic information system database. With the database, the management model calculated treatment type and treatment cost for each wastewater treatment plant in the river basin. Until now, wastewater treatment policy for polluted rivers in Korea has been, first of all, to construct secondary treatment plants for untreated areas, and secondarily, to construct advanced treatment plants for the river sections whose water quality is impaired and for which the water quality goal of the Ministry of Environment is not met. Four scenarios that do not use the GA were proposed and they were compared with the results of the management model using the GA. It became clear that the results based on the GA were much better than those for the other four scenarios from the viewpoint of the achievement of water quality goals and cost optimisation.