Use of Genetic Algorithms for the Optimal Design of Flat Plate Solar Collectors

Abstract

The performance of a flat plate collector depends on the collector efficiency factor (F΄). The value of the collector efficiency factor depends on a number of parameters like the riser pipe diameter, the distance between the riser pipes, the type of materials of construction and thickness, and many others. For a collector of fixed width the efficiency increases by increasing the number of riser tubes. However, by increasing the number of tubes the cost of the collector is also increased. Therefore the objective of the work presented here is to find the optimum number of tubes. For this purpose a genetic algorithm is used which is inspired by the way living organisms adapt to the harsh realities of life in a hostile world. A genetic algorithm is an optimum search technique based on the concepts of natural selection and survival of the fittest. The cost of the extra tubes considered is compared against the extra value of the energy collected by considering average weather conditions for 20 years (mean life of the system) and two types of conventional sources of energy i.e., light fuel oil (LFO) and electricity. The results show that a smaller number of tubes than the traditional number (10-12) have been obtained for the case where light fuel oil is considered and the number is insensitive to the pipe size, whereas an increased number is obtained in the case where electricity is considered. This is because electricity is 3.5 times more expensive than LFO.