Artificial intelligence-driven performance analysis of carbon nanotubes hybrid nanofluid with wastewater treatment applications: an intelligent neuro-computing model

• This study assimilates the effects of activation energy on hybrid nanofluid. • Impacts of Marangoni convection have been emphasized in this study. • Hybrid nanofluid flow over Riga plate with heat generation using artificial neural network has been investigated. • Explore the importance of microorganisms in hybrid nanofluid have been emphasized in this study. • The Bayesian regularization method is applied to assess the hybrid nanofluid training state, regression demonstration, error histograms, and performance. 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.