Optical and thermal analysis of solar parabolic dish cavity receiver system for hydrogen production using deep learning

Abstract

The solar thermochemical cycle, responsible for converting solar energy to chemical fuel, is an emerging clean hydrogen production technique. The distribution of the concentrated heat flux and temperature on the receiver is crucial in deciding solar-to-fuel conversion efficiency. Thus, the optimum configuration of a solar parabolic dish collector (PDC) is necessary for providing efficient thermal energy. The dish diameter, rim angle, and distance of the receiver aperture from the focal point of the dish characterize the optimization of the PDC. The non-uniform heat flux captured is coupled with a heat transfer model of a solar thermochemical reactor for a thermal dissociation of ZnO involved in a ZnO/Zn thermochemical cycle for hydrogen production. The directly irradiated rotating reactor comprises a cylindrical cavity packed with insulation layers and a quartz glass window. The rotating cavity is loaded with ZnO granular particles held by a centrifugal force. A 3D CFD numerical model is analyzed by coupling the Eulerian multiphase model for ZnO particle dynamics, fluid flow of gaseous mixture, RNG k-ԑ turbulence model, Energy and Discrete Ordinates (DO) radiation model, and Arrhenius reaction rate chemical reaction of ZnO dissociation. A numerical model with non-uniform heat flux input is validated for a cavity temperature, mass fraction of zinc and oxygen, and oxygen molar flow rate at the reactor outlet. An artificial neural network model is adopted to predict the heat flux and uniformity factor values of PDC. Correlation Coefficient (R2), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) error metrices were used to evaluate the model's performance having the receiver diameter (d), the dish diameter (D), the rim angle (φ), and the distance between the focus and receiver (h) as input variables.