Optimizing solar panel performance: a novel algorithm incorporating a duct with helical tape filled with a mixture of water and hybrid nano-powders

This study employs a machine learning methodology, specifically the Random Forest (RF) model, to evaluate and optimize the productivity of a photovoltaic (PV) unit integrated with a cooling duct equipped with helical fins. A thermoelectric generator (TEG) is strategically positioned above the cooling duct to enhance electricity production. The cooling mechanism utilizes confined jets involving of ND-Co₃O₄- water nanomaterial to improve thermal regulation. The key variables considered include the number of fins (N_f), their revolution number (N_r), inlet velocity (V_i), and heat flux intensity (I). The optimization focuses on three primary objectives: maximizing profit, enhancing CO₂ mitigation (CM), and minimizing pumping power (W_p). The RF model showed strong predictive capability, achieving a test RMSE of 0.4590 and an R² of 0.9474 for W_p, an RMSE of 71.8501 and an R² of 0.8421 for Profit, and an RMSE of 2.9472 with an R² of 0.8143 for CM. A multi-objective optimization technique was used to derive Pareto front solutions, balancing trade-offs among these objectives. The results demonstrate that integrating helical fins and nanoparticle-infused cooling jets significantly improves system performance, with optimized solutions reducing pumping power, while enhancing both profit and CO₂ mitigation.