Optimization operation of a parabolic trough collector using artificial neural network

The present work describes the thermal efficiency optimization of parabolic trough collectors by combining a model of artificial neural network and computational optimization techniques. A feedforward neural network architecture is trained using experimental database from parabolic trough collector operations. Rim angle, inlet and outlet fluid temperatures, ambient temperature, water flow, direct solar radiation, and wind velocity were used as main input variables within the neural network model to estimate the thermal performance. The optimal operation conditions of parabolic trough collectors are established using the hybridization of optimization technique with neural network model to achieve optimal operation conditions of parabolic trough collector. The result indicated that methodology implemented is a feasible tool for parabolic trough collectors optimization.