Artificial neural network analysis for oxytactic microbes in hybrid nanofluid with chemical reaction and thermal radiation

Abstract

The purpose of this investigation is to assess the outcome of Oxytactic microorganism in chemical reactive flow of TiO2 + GO/water based hybrid nanofluid flow across a sheet applying artificial intelligence. TiO2 and GO nanoparticles are combined with the base fluid, water (H2O). There are numerous real-world uses for the concept of artificial intelligence-driven performance improvement of oxytactic microbes in hybrid nanofluid with chemical reaction and thermal radiation in a variety of sectors. By controlling the temperature and chemical conditions for improved performance and focused treatment, it is applicable in biomedical engineering to improve microbial-based drug delivery systems. The model may improve the efficacy of environmental biotechnology's bioremediation processes, which use microorganisms to degrade pollutants under a range of chemical and temperature conditions. Additionally, the model can improve the efficacy of microbial cultures employed in fermentation or other bio-manufacturing processes in industrial processes like cooling systems by optimizing heat transfer in reactors utilizing nanofluids. The ordinary differential equations can alternatively be resolved utilizing an artificial neural network-based technique with Bayesian regularization. State training, performance, fitting plots, model response, and error histograms plots are utilized to explore the resulting network.