Multifactorial Analysis of Ternary Magnetized Radiative Nanofluid with Waste Discharge Concentration and Microorganism Behavior: Multi-Hidden Layer Mechanism

This study investigates a multifactorial analysis of the ternary infinite shear rate viscosity of a magnetized radiative Cross nanofluid with consideration of the waste discharge concentration of nanoparticles and of the microorganism behavior over a paraboloid surface. A three-hidden-layer supervised neural network mechanism is used in the analysis. Furthermore, this investigation includes the unique aspects of orthogonal/inclined magnetic field, thermal radiation, Brownian motion, thermophoresis, activation energy, and external source variation, with particular attention to the impact of waste discharge concentrations on viscosity and microbial dynamics. A set of partial differential equations is constructed with assumed physical assumptions and conversion is then made to ODEs on the basis of similarity variables. The MATLAB bvp4c program is used in combination with an artificial neural network (ANN) scheme scaled conjugate gradient neural network (SCG-NN) to predict the solution. It is found that the velocity power index, Rayleigh number, and volumetric percentage increase the magnitude of the velocity profile and that the Lorentz force tends to reduce the velocity magnitude of the nanofluid due to electromagnetic drag and resistive effect. Also, the temperature of nanofluid increases with radiation parameter and heat source/sink. The local pollutant external source parameter and local external pollution source variation parameter increase the concentration profile. Low mass transport of microorganisms can be seen for the bio-convection Lewis number, Peclet number and high local pollutant external source parameter.