Intelligent computing neural network-driven analysis of maxwell trihybrid nanomaterial with chemical reactor optimization using STG model

This paper inspects the outcome of viscous dissipation on chemical reactive flow of Maxwell trihybrid nanoliquid with Darcy Forchheimer porous medium and Marangoni convection. The current model has considerable applications in progressive chemical and thermal engineering systems that demand improved heat and mass transfer. It has numerous applications, including electronic cooling devices, heat exchangers, chemical reactors, hydrometallurgical leaching systems, mineral slurry transport, and metallurgical activities. The addition of viscous dissipation and Joule heating makes the model applicable to electrically conducting fluids and high-temperature industrial processes such as electrowinning and smelting, while the Maxwell trihybrid nanoliquid increases thermal efficiency. Furthermore, the artificial neural network (ANN) structure allows for rapid prediction and enhancement of flow and heat transfer properties, making the model valuable for the design and control of modern energy and manufacturing systems. To train, test, and validate the model for precise flow behavior and thermal characteristic prediction, a reference dataset is created.The results demonstrate that the LMB-ANN technique provides fast convergence, high accuracy, and robust performance, making it a reliable and effective tool for optimizing complex industrial convection systems, including hydrometallurgical reactors and mineral slurry transport operations. The concentration profile for both trihybrid and hybrid nanofluids decreases as the chemical reaction parameter values increase.Clinical trial Number: Not applicable.