Integration of ANN and NSGA-II for Optimization of Nusselt Number and Pressure Drop in a Coiled Heat Exchanger via Water-Based Nanofluid Containing Alumina and Ag Nanoparticles

In this research study, minimizing the pressure drop and maximizing the Nusselt number of water-based nanofluid containing alumina and Ag in a coiled heat exchanger have been considered. The artificial neural network has been implemented to predict nanofluid’s pressure drop as well as Nusselt number as a function of nanoparticles volume fraction and Reynolds number. The obtained correlation was found properly accurate as most of the values were accumulated around the line. The value of R, mean error, and STD for the Nusselt number as well as pressure drop were found (0.9996 and 0.9993), (0.007 and − 6.4*10^–5), and (0.172 and 0.157), respectively. The obtained correlations using ANN are used as input into the NSGA-II optimization algorithm to obtain the optimum pressure drop as well as Nusselt number as a function of nanoparticles volume fraction and Reynolds number. The optimum values were determined in the range of Nusselt number of 8–24 and pressure drop of 2–16 Pa. The optimal point can be chosen according to the process requirement. For the lowest and highest optimum pressures (8.566 and 23.691 Pa), the optimum Nusselt numbers were 2.502 and 115.35, respectively.