Design oriented modeling of squeezed MHD convective flow of hybrid nanoliquid over a sensor surface with chemical reaction advanced sensor technology using artificial neural network

This design-oriented methodology has significant uses in industrial process optimization, and advanced sensor technologies. The same framework is also relevant to mining and mineral-processing environments, where sensor-assisted monitoring is required for ore-slurry transport, chemically reactive leaching systems, tailings process streams, and mine-water treatment units operating under coupled thermal and mass-transfer conditions. Hybrid nanofluid increase sensitivity and response time under convective and magnetic control, which helps improve the thermal and mass transfer performance of chemical and biosensors. To improve the analysis and prediction accuracy of such fluid flows, we used a supervised neural network with back-propagated Levenberg-Marquardt technique (SNN-BLM). The model is helpful in biomedical engineering for creating drug delivery systems, lab-on-a-chip systems, and bio-reactors, where microorganism bioconvection promotes efficient reaction rates and uniform mixing. It facilitates the improvement of chemical reactors, coating procedures, and micro-cooling devices in energy and manufacturing systems, allowing for improved control of heat, flow stability, and reaction efficiency through the use of magnetic fields and nanofluid characteristics. This study presents a computational analysis of squeezed bioconvection hybrid nanofluid flow over a sensor surface, incorporating a heat source. The interplay between bioconvection, hybrid nanoparticles and chemical reaction is of utmost importance in fluid dynamics and heat and mass transmission. The result is gained using the Homotopy analysis method (HAM). The velocity profile increases by enhancing the values of magnetic parameter and squeezed flow index parameter. The temperature profile decreases by enhancing the values of suction/injection parameter and squeezed flow index parameter. Quantitatively, increasing the magnetic parameter from 0 to 0.9 enhances the skin friction coefficient by approximately 76%, while the Nusselt number increases with both M and suction parameter [Formula: see text].