Simulation of oxytactic microbes in hybrid nanofluid with activation energy and LTNE effects using Bayesian regularization neural network technique.

This model has significant applications in advanced thermal and bioengineering systems where coupled fluid flow, heat transport, and microbial dynamics are vital. It is applicable to the design of bioreactors and microbial fuel cells, where Oxytactic bacteria improve mixing and mass transfer efficiency. The model optimizes heat transport in nanofluid-based cooling systems, particularly under magnetic fields (MHD), by employing the Xue thermal conductivity formulation. The inclusion of LTNE (local thermal non-equilibrium) effects and activation energy makes it applicable to chemical reactors, catalytic processes, and biomedical drug delivery, all of which rely on reaction rates and temperature gradients. This study used a neural network trained using the Bayesian-Regularized approach to inspect the impact of LTNE on oxytactic microorganisms in bioconvection flow of a HNF (hybrid nanofluid) with activation energy. There are several uses for Oxytactic microbes, especially in environments and industries. In wastewater treatment, oxytactic bacteria are frequently utilized to increase oxygen distribution in bioreactors and accelerators for the breakdown of organic contaminants. These bacteria move quickly in response to gradients in oxygen concentration. In many biotechnological applications, Oxytactic microbes must be able to situate themselves within fluid flows in order to efficiently combine and transfer nutrients. The concentration profile increases as the activation energy parameter's values rise.