Artificial neural networks for predicting the local concentration ratio of parabolic trough collectors

Abstract

The concentrated radiant flux on receiver surfaces of parabolic trough collectors is not uniform but exhibits a “bell” type shape as presented in this paper. This flux is given in terms of the local concentration ratio (LCR) being in effect the ratio of the incoming to the concentrated value of solar radiation at the periphery of the receiver. Knowledge of the LCR is required not only at normal conditions but also at various incident angles. This is useful to a designer who wants to calculate the intercept factor and the optical efficiency of the concentrator at those angles. In the work presented here actual values of LCRs, at ten degree intervals around the receiver, for two different incident angles (15° and 60°) are used to train an artificial neural network. Subsequently the network is used to predict the LCR values at other incident angles.The matching of the data for the 15° and 60° is very good with an R2- value equal to 0.9997. For the unknown data at incident angles of 0°, 30° and 45° the R2-values are 0.9966, 0,9867 and 0.9765 respectively which indicates that the estimation is performed with adequate accuracy. By using the predicted LCR values, the intercept factor is estimated with a maximum deviation of 3.2% from the value estimated with the actual LCR values which is very adequate.