Comparison of dynamic and static neural networks in predicting performance of parabolic solar desalination

In this research with utilization various neural networks models¡ the relationship between the amount of water production and the temperature of the vapor with different weather conditions¡ time of day and several water debit in desalination system equipped whit linear solar parabolic concentrator was investigated. The results showed that static and dynamic networks can be modeled the process of production fresh water with high accuracy. Static neural network can do the modelling process with higher speed than dynamic neural network. However it seems that the amount of error with using dynamic networks was reduced in process modeling. Coefficient of determination (R2) for training¡ validation and testing in static networks were 0.9898¡ 0.9899 and 0.9889¡ respectively. While coefficient of determination (R2) for training¡ validation and testing in dynamic networks were 0.9922¡ 0.9894 and 0.9901¡ respectively. Also the amount of mean square error (MSE) in static network for training¡ validation and testing was 0.0011¡ 0.0027 and 0.0024¡ respectively and for dynamic networks was 0.0018¡ 0.0007 and 0.0004¡ respectively. Comparison between dynamic and static networks show that the dynamic networks can be predicted the production of fresh water and vapor temperature according to changes in atmospheric parameters accurately than the static networks.