Predicting Fresh Water of Single Slope Solar Still Using a Fuzzy Inference System
According to the Cyprus Demographic Report, about 55% of households in Northern Cyprus do not have access to treated water. For treating water, distillation is utilized. Although there are many distillation methods, they are either energy intensive or contribute to environment. This research we use experimental data of a solar distillation system that produces potable water for drinking purposes from sea or salty water. The solar energy heats the water in the tank the water temperature will increase then evaporate and condensate on the glass cover, this condensate drops down the fresh water collector at the bottom, which settles the mass of salt in the bottom of distillation basin. The results showed that the thermal performance, improvements can be made to achieve still water production rates. The water (polluted sea water and salty well water) laboratory test results show that the distillation process eliminated the bacteria, being appropriated for human use. In this work, we developed a fuzzy inference system (FIS) to predict the fresh water of single slope solar still distillation. In the used FIS, we only utilize angle, water temperature, surface temperature and amount of fresh water of the solar still distillation. Evaluations show that predicted values are correlating with the experimental data with 29.40, 4.75, root mean square error and average forecasting error respectively. Therefore, the soft computing approach can be very useful for predicting fresh water of single slope solar still distillation with speed and simplicity.
KeywordsBasin still Experimental performance Fresh water Fuzzy inference system
- 1.Howell, J.R., Bannerot, R.B., Vliet, G.C.: Solar Thermal Energy Systems Analysis and Design. McGraw Hill, New York City (1985)Google Scholar
- 2.Ghosh, G.K.: Solar Energy: The Infinite Source. South Asia Books, Columbia (1992)Google Scholar
- 3.Mashaly, A.F., Alazba, A.A.: Membership function comparative investigation on productivity forecasting of solar still using adaptive neuro-fuzzy inference system approach. Environ. Prog. Sustain. Energy 15(5), 555–578 (2017)Google Scholar