Cost and Efficiency Optimizations of ZnO/EG Nanofluids Using Non-dominated Sorting Genetic Algorithm Coupled with a Statistical Method

In this study, optimization of ZnO/EG nanofluids was investigated to increase efficiency and reduce costs. To determine the efficiency of nanofluid, the definition of Mouromtseff number was used. The cost of nanofluid in terms of the volume fraction of nanoparticles (phi) was determined. Then, Mouromtseff functions and costs were calculated by response surface methodology (RSM) with regression up to 96%. To determine the minimum cost and maximum efficiency in terms of Mouromtseff number, a non-dominated sorting genetic algorithm (NSGA II) which is powerful in achieving optimal response was employed. In the end, the Pareto front, optimal values of Mouromtseff, and the minimum corresponding cost were obtained. Also, for achieving an optimal pattern of minimum cost in terms of maximum thermal efficiency, a suitable correlation was presented. The results show that to achieve maximum thermal efficiency, the minimum cost is $ 360 per liter and also the minimum cost to achieve the optimal efficiency coefficient is in phi = 0.5%. Nanofluid optimization can also reduce nanofluid costs by up to 10%.