Dynamics of waste-discharge pollutant concentration and heat source/sink effects across a nonlinear stretching surface conveying trihybrid nanofluids: a multi-output MLP-ANN surrogate approach

Abstract

The analysis of nanofluid circulation over a nonlinear stretching surface plays an important role in areas like thermal engineering, pollutant control systems, processing of polymers, coating technology, cooling equipment, and advanced manufacturing processes. Motivated by the above applications, the present study investigates the combined effects of heat source/sink, porous medium, activation energy, pollutant discharge concentration, velocity slip, and thermal slip effects of a tri-hybrid nanofluid flow over a nonlinear extending surface. The governing flow equations are converted into a system of nonlinear ordinary differential equations and solved computationally with the help of the RKF-45 algorithm together with a shooting procedure. A multi-output multilayer perceptron artificial neural network is subsequently developed as a data-driven surrogate for the simultaneous prediction of engineering factors. The proposed model achieved a mean testing NRMSE of 6 1.0705 10−  and a mean testing MAPE of 4 1.2560 10−  %. Comparative evaluation against RBFNN, Gaussian SVR, sparse GPR, and LSBoost models demonstrated that MO-MLP-ANN provided the lowest testing error within the examined one at a time sampled domain.