Articles & Issues
- Language
- English
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
-
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
All issues
Cost and Effi ciency Optimizations of ZnO/EG Nanofl uids Using Non-dominated Sorting Genetic Algorithm Coupled with a Statistical Method
Abstract
In this study, optimization of ZnO/EG nanofl uids was investigated to increase effi ciency and reduce costs. To determine
the effi ciency of nanofl uid, the defi nition of Mouromtseff number was used. The cost of nanofl uid in terms of the volume
fraction of nanoparticles ( φ ) 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 effi ciency 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 effi ciency, a suitable
correlation was presented. The results show that to achieve maximum thermal effi ciency, the minimum cost is $ 360 per
liter and also the minimum cost to achieve the optimal effi ciency coeffi cient is in φ = 0.5%. Nanofl uid optimization can also
reduce nanofl uid costs by up to 10%.
