Thermal and solutal analysis of oxytactic microbes in bioconvection slip flow of trihybrid nanofluid with activation energy using artificial neural network

In this work, the intelligent Levenberg-Marquardt optimization approach is applied to evaluate the activation energy influence on thermo-bioconvection flow of a trihybrid nanofluid including oxytactic microbes via a plate using integrated numerical computation. This idea is frequently applied in industrial and bioengineering operations where complicated fluid conditions and microbial activity interact. The addition of oxytactic microorganisms and a trihybrid nanofluid allows the model to simulate bioconvection behavior relevant to wastewater treatment, biofuel generation, and bioreactors, all of which require efficient mixing and oxygen supply. It is more advantageous to improve biochemical reactions, microbial growth conditions, and nutrient distribution by using activation energy and ANN-based predictive analysis. As a result, the model makes it easier to create and refine advanced biotechnological systems, environmental monitoring setups, and microfluidic devices that use microbe-nanofluid interactions. The algorithm's reliability is further confirmed using histogram and function fitness. For fluid dynamics, numerical approaches and artificial neural networks work well together, potentially leading to new discoveries in a range of domains. The findings of this study could help optimize fluid systems, increasing production and efficiency in a range of technological domains. As the bioconvection Schmidt number increases, the oxytactic microbe profile decreases.