Bayesian-regularized neural network analysis of heat and mass transfer in ternary hybrid nanofluid with motile microorganisms: Thermodynamic optimization

The aim of this endeavor is to apply artificial intelligence to provide original solutions for very complicated and challenging physical model. The results of chemical reaction involving Marangoni convection, gyrotactic microbes and thermo-bioconvection in water-based THNF (trihybrid nanofluid) flow across a sheet with heat source, thermal radiation, thermophoretic particle deposition is assessed in this work using a feed-forward neural network in conjunction with back-propagation Bayesian regularization optimization (FFNN-BPBRO). The suggested Bayesian-Regularized Neural Network model for assessing mass and heat transportation in ternary nanofluids including motile microorganisms can be used to improve thermal performance in biomedical systems, microfluidic devices, energy storage, and cooling technologies. It also offers important insights for wastewater treatment, bio-convective transport, and nanofluid-based industrial processes, where precise prediction and thermodynamic optimization are critical for increasing efficiency, stability, and sustainability. As the Peclet number increases, the microorganism profile decreases.