The current study examines the properties of heat radiation on the Darcy Forchheimer flow of carbon nanotube/water based hybrid nanofluid across a Riga plate in the occurrence of oxytactic microbes, employing a novel intelligent numerical computing paradigm based on the legacy of neural networks with the intelligent Bayesian regularization (NN-IBR) method. The AI-driven neuro-computing model for improving the thermal behavior of a carbon nanotube (CNT) hybrid nanofluid in wastewater treatment has a wide range of applications. It has the potential to dramatically improve thermal management efficiency in wastewater treatment plants, improve pollutant removal through optimal heat and mass transfer, and minimize energy consumption in treatment operations. This model can also be used in sustainable water recycling, industrial effluent treatment, and smart environmental management systems, where intelligent prediction and control of nanofluid performance is critical for accomplishing environmentally friendly and cost-effective operations. The Homotopy analysis approach is used to classify the obtained equations. The concentration profile increases as the activation energy parameter values upsurge.

This study examines the outcome of heat generation on thermophoretic particle deposition in Co3O4/HFE-7100 nanofluid flowing past a vertical cylinder in a permeable media under local thermal non-equilibrium conditions employing an ANN coupled with a Bayesian-regularized back-propagation algorithm. For contrast, a simplified mathematical formulation is also used to examine the thermal behavior without local thermal equilibrium assumptions, that is, without LTNE limitations. The solid matrix and fluid phase are treated as distinct temperature fields in the LTNE framework, necessitating unique thermal gradients for each phase. Optimizing heat transfer performance using ANN-based regression modeling is the main goal of this work. Well-structured training and testing datasets are used to guarantee numerical stability and prediction resilience, and the Bayesian-regularization back-propagation technique is applied to enhance generalization capacity. The impact of dominating governing factors is illustrated graphically, and an error evaluation is provided to gauge model correctness. The ANN model was trained using the obtained dataset and then tested against numerical values of important engineering variables. Tables and graphs are utilized to show how new factors affect the flow’s dynamics. The rate of liquid phase heat transmission declines as the inter-phase heat transport values increase.