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

In this paper, an extension of classical waste-load allocation models for river water qualitymanagement is presented to determine the monthly treatment or removal fraction of wastewaterto evaporation ponds. the dimensionality of the problem, which is due to a large numberof decision variables, is tackled by developing a new ga based optimization model, which iscalled a sequential dynamic genetic algorithm (sdga). this is a deterministic multi-objectiveoptimization model, which is linked to an unsteady water quality simulation model. the modelminimizes the total losses incurred during the optimization time horizon, including the treatmentor 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 ofthe karoon river in iran. the results show the proposed model can effectively reduce thecomputational burden of the seasonal waste-load allocation problem. it is also shown that theseasonal waste-load allocation can significantly reduce the number and duration of standardsviolations.