Artificial Neural Networks for the Estimation of the Performance of a Parabolic Trough Collector Steam Generation System

Abstract

The parabolic trough collector (PTC) is the most preferred type of solar collecting system employed for steam generation. This is due to the fact that the collector can work with high efficiencies at high temperature. Such a system consists of a PTC, a flash vessel and an associated circulation and control system. The amount of steam produced depends on the solar radiation available, ambient air temperature, collector area, total system water capacity, and the diameter and height of the flash vessel. Data for a number of cases were used to train an artificial neural network in order to generate a mapping between the above easily measurable inputs and the desired output system performance. The collectors used had areas varying from 3.5 m2 to 2160 m2. Different neural architectures have been used in order to find the one with the best possible performance. The multilayer feedforward architecture using the standard backpropagation learning algorithm have been the most successful so far. Many other architectures are still under investigation. The results obtained for the training set are such that they yield a statistical R2 = 0.999. The network was used subsequently for predictions of the performance of cases other than the ones used for training, both within and outside the above range. Typical value of the accuracy obtained was 95% (at an R2 = 0.999). This is considered as acceptable for such estimations