Multi-objective optimization of a pond aeration system using Taguchi-based gray relational analysis

Abstract

There is a growing interest in high-performance, sustainable, and efficient aeration technologies to ensure the necessary oxygen levels in ponds, wastewater treatment plants, and aquaculture areas. Multiple optimization efforts are mandatory to maximize the aeration performance and energy efficiency of high-aeration performance systems simultaneously. This study aims to optimize the aeration performance of aquaculture systems using the Taguchi-based gray relational analysis (GRA) method. Taguchi-based gray relational analysis method has been preferred due to its capability of enabling optimization with fewer experiments, analyzing interactions among independent variables, and its applicability in both linear and nonlinear systems. The use of the Taguchi-based GRA method to improve efficiency in the aeration process is a novel approach for these systems with multiple variables. In this study, the optimal parameter levels for the conduit aeration system were determined and compared with the initial levels through validation tests. Additionally, the effects of parameters on aeration performance and energy cost were analyzed. As a result, it was determined that the aeration performance of the system increased fourfold compared to the initial level, and the cost decreased sixfold. It was observed that the most effective parameter on the system is the gate opening, and the least effective parameter is the tank diameter.