Publication

Abstract : Purpose The purpose of this research is to investigate the peristaltic transport of a chemically reactive nanofluid under dual-diffusive convection within an inclined flexible microchannel. The goal is to accurately predict coupled heat and mass transfer behaviors through an artificial neural network (ANN)-based predictive framework, offering a computationally efficient alternative for modeling environmentally responsive fluidic systems. Design/methodology/approach A Casson nanofluid model is formulated, incorporating magnetohydrodynamic forces, thermal radiation, viscous dissipation, double diffusion and cross-diffusion effects. The governing equations are simplified using lubrication theory, transformed into non-dimensional form and solved numerically via a bvp5c solver in MATLAB. Numerical datasets are then used to train a bayesian-optimized ANN (BAN-ANN) model across nine key parameter variations. Model performance is validated through regression metrics, error histograms, fitness plots and mean squared error evaluation. Findings Stronger magnetic fields and higher solutal Grashof numbers reduce fluid velocity, while temperature profiles are strongly influenced by Brownian motion and Dufour effects. Increasing the porosity parameter from 0.1 to 0.3 raises skin friction by 36.57%, whereas raising the Brinkman number from 0.1to 0.2 results in a 23.4% reduction in the Nusselt number. ANN predictions for heat and mass transfer rates closely align with numerical results, demonstrating excellent accuracy and generalization capability. Practical implications The study offers a framework for ANN-driven optimization of microscale fluid systems, contributing to improved design strategies for pollutant removal, energy-efficient cooling technologies and bioinspired microdevices operating under multiphysics conditions. Originality/value This work presents a novel BAN-ANN approach to simulate and predict chemically reactive nanofluid transport in magneto-thermal environments.