Hypolimnetic aeration optimization based on reservoir thermal stratification simulation

Hypolimnetic aeration is an effective management strategy for mitigating oxygen deficiency in dam reservoirs caused by thermal stratification (ThS). However, designing an optimal hypolimnetic aeration system based on dissolved oxygen (DO) concentration evaluation has yet to be considered in the literature. This study proposed an innovative optimization model utilizing the non-dominated sorting genetic algorithm-II (NSGA-II) for up to 4 aerators, considering two main objectives: (i) minimizing DO concentration deviation from the standard limit and (ii) minimizing aerator(s) operation energy. This optimization model was developed based on five primary decision variables: the location of the aerator(s) (length and depth), the aeration rate, and the operating time (start and end date). The NSGA-II optimization approach was coupled with the CE-QUAL-W2 simulation model for each identified period of ThS to simulate DO concentrations. To satisfy the conflicting objectives of the primary beneficiaries involving the Ministry of Energy and the Regional Water Organization, a two-step decision-making framework was introduced, utilizing the Nash bargaining (NB) and weighting method (WM) techniques. In order to show the practicality and effectiveness of the suggested framework in enhancing the DO concentration within a dam reservoir, the novel simulation optimization framework was implemented in a real-world case study of the Karkheh dam reservoir (KDR) in Iran. The findings revealed that applying an optimal hypolimnetic aeration system significantly improved the DO concentration, reaching at least twice the level without an aerator. However, the influence of aerators was not long-lasting, gradually diminishing when their operations ceased. Considering the importance of increasing DO concentration to mitigate the adverse effects of ThS, the four-aerators system was chosen as the optimal compromise design for the KDR.