Artificial neural networks for modelling the starting-up of a solar steam-generator

Abstract

An experimental solar steam generator, consisting of a parabolic trough collector, a high-pressure steam circuit, and a suitable flash vessel has been constructed and tested in order to establish the thermodynamic performance during heat-up. The heat-up energy requirement has a marked effect on the system's performance because solar energy collected during the heating-up period is lost at night due to the diurnal cycle. This depends mostly on the dimensions and the inventory of the flash vessel, and the prevailing environmental conditions. Experimental data were obtained and used to train an artificial neural network in order to implement a mapping between easily measurable features (environmental conditions, water content and vessel dimensions) and the system temperatures. Such mapping may be useful to system designers when seeking to find the optimal vessel-dimensions. The trained network predicted very well the response of the system, as indicated by the statistical R-squared value of 0.999 obtained and a maximum deviation between predicted and actual values being less than 3.9%. This degree of accuracy is acceptable in the design of such systems. The results are important, because the system was tested during its heat-up cycle, under transient conditions, which is quite difficult to model analytically.