Intensification of heat and mass transfer process in MHD carreau nanofluid flow containing gyrotactic microorganisms

Abstract

Process intensification deal with the complex fluids in mixing processes of many industries and its performance is based on the flow of fluid, heat and mass transfer. This paper presents the mathematical and Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the unsteady two-dimensional bio-convection flow of Carreau nanofluid incorporating gyrotactic micro-organisms over a slendering stretching sheet with the presence of magnetic field, thermal radiation and multiple slip conditions. Suitable similarity variables are applied to convert the flow equations into higher order ordinary differential equations and solved numerically. The surface-contour plots are utilized to visualize the influence of active parameters on velocity, thermal, nanoparticles concentration and motile microorganisms’ density. The hybrid-learning algorithm comprised of gradient descent and least-squares method is employed for training the ANFIS. The optimal ANFIS models are achieved with root mean squared error (RMSE) and coefficient of determination (R2) values of (0.0338, 0.996487), (0.033607, 0.973544), (0.075168, 0.990476) and (0.051256, 0.996073) for Cf, Nu, Sh and Nn, respectively. The proposed ANFIS models are efficient and predicted the results with higher accuracy.