Intelligent predictive neural network analysis on microbes in bioconvection flow of hybrid nanoliquid under diffusion thermo and thermo diffusion effects

The model optimizes microfluidic and lab-on-a-chip systems by predicting gyrotactic microbe-driven mixing and transport in the presence of linked thermal and concentration gradients. It aids in the design and operation of bioreactors and wastewater treatment units by simulating microbe dispersal, nutrient transport, and nanoparticle-assisted mass transfer for better biodegradation. It aids in targeted medication administration and biomedical experiments by predicting microbe and nanoparticle behavior in thermally heterogeneous solutions. Simulating microbial mobility in thermo-stratified, nanoparticle-laden fluids helps to inform environmental monitoring and cleanup techniques. It improves heat-exchanger and cooling technologies by utilizing bioconvective enhancement and Marangoni surface-tension phenomena. It optimizes biofuel and fermentation processes by improving microbe aggregation and stability in hybrid nanofluids. It aids in the creation of biosensors and advanced materials processing, where thermo-diffusive transport and microbial motility interact to effect performance. Artificial neural network (ANN) modeling optimized with the Bayesian Regularization technique is used in this study. The method avoids over-fitting and improves prediction accuracy. The outcomes of thermo-diffusion and diffusion-thermo properties on bioconvection flow of a hybrid nanofluid around a disk with gyrotactic microbes are investigated using homotopy analysis modeling. The temperature and concentration profiles become more pronounced as the Soret and Dufour numbers increase.