Multiobjective Optimal Waste Load Allocation Models for Rivers Using Nondominated Sorting Genetic Algorithm-II

Abstract

A multiobjective optimization framework for optimal waste load allocation in rivers is proposed, considering (1) the total treatment cost, (2) the equity among the waste dischargers, and (3) a comprehensive performance measure that reflects the dissolved oxygen (DO) violation characteristics. This framework consists of an embedded river water quality simulator that has a gradually varied flow module and a pollutant transport module, which simulates the transport process including reaction kinetics (in terms of biochemical oxygen demand-DO). The outer shell of the framework consists of the two nonseasonal, deterministic, multiobjective waste load allocation planning models, namely, cost-performance model and cost-equity-performance model. These models are solved using a powerful and recently developed multiobjective genetic algorithm technique known as the Nondominated Sorting Genetic Algorithm-II. The practical utility of the multiobjective framework in decision-making is illustrated through a realistic example of the Willamette River in the state of Oregon.