Notional comparison for prognosis the performance of evacuated tubular solar collector via utilizing adaptive fuzzy neural inference system Available to Purchase

In this study, a theoretical comparison was made of three electronic curtains made of different reflective materials (acrylic mirrors, glass mirrors, aluminum foil) by using the ANFIS (Adaptive Neural Fuzzy Inference System) artificial intelligence technology to predict the performance of the evacuated tube solar collector. The main working fluid in the solar collector is a copper nanofluid with a diameter of 25nm at three different volumetric concentrations (1%,3%,5%) and three flow rates (6,12,18 L/min). Three input parameters (solar radiation intensity, ambient temperature, and inlet temperature) were used. Only one output was obtained from the artificial neural network, which is (the values of opening and closing angles of electronic curtains with different reflective materials). The electronic curtain with reflective acrylic mirrors had the best performance, as the error value reached (8.0944e-05), which is the lowest error rate compared to the use of an electronic curtain with glass mirrors and aluminum foil. The purpose of using the electronic reflective curtain in general is to increase the solar radiation reflected on the collector tubes, control the temperature of the nanofluid, and also protect the collector tubes from weather conditions. It was observed that the experimental results are consistent with the prediction results by predicting the performance of the evacuated tube solar collector produced by the ANFIS technology. The results also showed that the type of reflective material plays a major role in improving the performance of the solar collector, and that the electronic curtain with reflective material, acrylic mirrors, gave the best performance, which contributed to improving the performance of the solar collector better than other reflective materials. The reason is due to its high reflectivity, estimated at 99%.