A river water quality management model for optimising regional wastewater treatment using a genetic algorithm

Area of Science:

• Environmental Engineering
• Water Resource Management
• Computational Intelligence

Background:

• River basin water quality is declining due to heavy pollutant loads.
• Existing wastewater treatment policies in Korea focus on secondary and advanced treatment plants.
• Optimizing treatment strategies is crucial for both environmental protection and economic efficiency.

Purpose of the Study:

• To develop an integrated water quality management model for optimizing wastewater treatment costs and achieving water quality goals.
• To apply the developed model to the Youngsan River basin, a region facing significant pollution challenges.
• To compare the model's performance against traditional scenarios without advanced optimization techniques.

Main Methods:

• Integration of a genetic algorithm (GA) with a mathematical water quality model.
• Development of a geographic information system (GIS) database incorporating pollution sources, land use, and water quality data.
• Scenario analysis comparing GA-integrated model results with non-GA scenarios.

Main Results:

• The GA-integrated model effectively calculated optimal treatment types and costs for wastewater treatment plants.
• Application to the Youngsan River demonstrated significant improvements in achieving water quality targets.
• Results showed superior performance in both water quality achievement and cost optimization compared to traditional methods.

Conclusions:

• The integrated GA-based model offers a superior approach to wastewater management compared to conventional policies.
• This approach provides a cost-effective strategy for improving water quality in river basins.
• The model serves as a valuable tool for policymakers in addressing river pollution and optimizing treatment investments.