Bayesian Regularization Technique for Chemical Reactive Flow Dynamics of Hybrid Nanomaterial Using Xue Model: Chemical Reactor Optimization

This study inspects the flow dynamics of Stefan blowing effects and heat radiation on chemical reactive flow of hybrid nanofluid using a Riga plate in the existence of activation energy. To model and address the intricate nonlinear issue, artificial neural networks (ANNs) that have been trained using the Bayesian regularization back-propagation strategy (ANN-BRS) are utilized. Gyrotactic microbes are integrated with nanoparticles in various applications, including microfluidic platforms, bio-microsystems on chip-scale devices, enzyme-based biosensors, bacteria-driven micromixers, microbial fuel cells, and other micro-engineered systems, to enhance thermal efficacy. This method can also help environmental engineering by improving wastewater treatment procedures by allowing microbes to more effectively degrade pollutants. It raises the production of biofuel in the realm of renewable energy by advancing the creation of more effective photo bioreactors. Material scientists to produce regulated nanostructured materials with consistent compositions and thermal characteristics may use this idea. A higher Stefan blowing factor leads to a higher flow profile. The concentration field decreases as the values of the chemical reaction parameter increases.

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