Optimization and performance enhancement of parabolic trough collectors using hybrid nanofluids and ANN modeling

• The study involves computational modeling of the LS-2 parabolic trough collector . • ANNim-GA and hybrid nanofluid methodologies were used to enhance PTC performance. • An optimal 6-4-1 ANN model achieves an R² of 0.9995 and an RMSE of around 0.0002. • Thermal performance improves with hybrid nanofluids compared to mono nanofluids . • Maximum energy efficiency occurs at lower temperatures and high Reynolds numbers . Enhancing the performance of renewable energy systems is pivotal to overcoming global energy challenges, with parabolic trough collectors (PTC) emerging as a promising technology for harnessing solar energy. This study focuses on the numerical modeling with various nanofluids of the PTC. Many previously reported studies on hybrid nanofluids have not adequately considered the appropriate Reynolds, Prandtl numbers and temperature, raising concerns about the reliability. This study ensures proper Reynolds numbers and inlet temperature for the investigated heat transfer fluids. The model is tested with mono (Al 2 O 3 , TiO 2 ) and hybrid (Al 2 O 3 +TiO 2 ) nanofluids, with thermal analysis mathematically modelled. ANN and ANNi modeling are applied in to optimize governing parameters for the collector with working fluids. Turbulence boosts heat transfer coefficient (HTC), with the hybrid nanofluid exhibiting the highest increase (86.88 %). Energy efficiency peaks at lower temperatures and fluid Reynolds numbers, while exergy efficiency rises with increasing inlet temperature and Reynolds number. The optimal performance of ANN model is achieved with four neurons in the hidden layer and a 6-4-1 architecture, with highest value of R 2 (0.999513) and lowest RMSE (0.000186). GA optimization shows that lower inlet fluid temperature and Reynolds number enhance thermal efficiency.