A New Robust Molding of Heat and Mass Transfer Process in MHD Based on Adaptive-Network-Based Fuzzy Inference System

Abstract

This study concerns with the Process intensification deal with the complex fluids in mixing processes of many industries and its performance is based on the flow of fluid, magnetohydrodynamic (MHD) heat and mass transfer. This paper proposes a dynamic control model based on adaptive-network-based fuzzy inference system (ANFIS), weighted logistic regression and robust relevance vector machine (RRVM). 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 microorganism’s density. The hybrid-learning algorithm comprised of gradient descent and least-squares method is employed for training the ANFIS. A novel RRVM is presented to predict the endpoint. RRVM solves the problem of sensitivity to outlier characteristic of classical relevance vector machine (RVM), thus obtaining higher prediction accuracy. The key idea of the proposed RRVM is to introduce individual noise variance coefficient to each training sample. In the process of training, the noise variance coefficients of outliers gradually decrease so as to reduce the impact of outliers and improve the robustness of the model. To compare the proposed RRVM and other methods with outliers, the Monte Carlo simulation study has been performed. The simulation results showed that, based on mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R^2) criteria, the proposed RRVM give better performance than other methods when the data contain outliers. While when the dataset does not contain outliers, the results showed that the classical RVM is more efficient than other methods.