River water quality management using an integrated multi-objective optimization-simulation approach based on...

Area of Science:

• Environmental Engineering
• Water Resource Management
• Operations Research

Background:

• Rivers possess a natural self-purification capacity, which is the acceptance capacity for pollution.
• Fairly allocating this capacity among pollutant sources is crucial for effective water quality management.
• Bankruptcy theory offers a framework for equitable resource distribution in such scenarios.

Purpose of the Study:

• To apply bankruptcy theory for fair allocation of river self-purification capacity.
• To integrate water quality simulation with optimization algorithms to evaluate different allocation rules.
• To minimize polluter wastewater treatment costs while preventing biochemical oxygen demand (BOD) standard violations.

Main Methods:

• Utilized bankruptcy rules (CAE, CEL, P, TAL) linked to the QULA2Kw water quality model.
• Employed a multi-objective imperialist competition algorithm (MOICA) for optimization.
• Evaluated scenarios based on reducing treatment costs and BOD violations.

Main Results:

• The CEL scenario achieved the best compromise between treatment costs and BOD standard compliance for the Dez River.
• For maximum treatment cost scenarios, the CEA rule was favorable, showing lower costs and higher discharge within acceptable limits.
• The study demonstrates the practical application of bankruptcy theory in river pollution management.

Conclusions:

• Bankruptcy theory provides a robust framework for equitable pollution load allocation.
• The CEL rule is recommended for balancing environmental and economic objectives in river management.
• Optimization algorithms coupled with water quality models enhance decision-making for sustainable water resource utilization.