Waste-Load Allocation Model for Seasonal River Water Quality Management: Application of Sequential Dynamic Genetic Algorithms

Abstract

In this paper, an extension of classical waste-load allocation models for river water quality management is presented to determine the monthly treatment or removal fraction of wastewater to evaporation ponds. The dimensionality of the problem, which is due to a large number of decision variables, is tackled by developing a new GA based optimization model, which is called a Sequential Dynamic Genetic Algorithm (SDGA). This is a deterministic multi-objective optimization model, which is linked to an unsteady water quality simulation model. The model minimizes the total losses incurred during the optimization time horizon, including the treatment or removal fraction costs and the costs associated with the deviation from water quality standards. The proposed model has been used for the water quality management and salinity reduction of the Karoon River in Iran. The results show the proposed model can effectively reduce the computational burden of the seasonal waste-load allocation problem. It is also shown that the seasonal waste-load allocation can significantly reduce the number and duration of standards violations.