Modeling and optimization of flat plate solar air collectors: An integrated fuzzy method

Abstract

The modeling and optimization of a flat plate solar air collector are investigated experimentally under the climatic conditions of Northeastern India using the integrated fuzzy method (IFM). The IFM consists of a combined subtractive clustering with a fuzzy (Takagi-Kang) method. The subtractive technique is used to find the most favorable IF-THEN rules while the fuzzy method is to optimize/predict the solar air collector parameters. Various governing parameters, such as the mass flow rate of air, collector tilt angle, solar radiation, and ambient temperature, are used as the input parameters while the thermal efficiency, exergetic efficiency, temperature rise, and pressure drop are the output parameters. First, experiments on solar air collectors are performed by varying the input parameters. Then, optimization, prediction, and parametric analyses are conducted. Finally, the proposed results are validated using confirmatory tests with the experimental data, published data, and artificially generated data. The accuracy of the obtained result for the solar air collector is found to be ∼97.5% and the best possible set of governing parameters are a mass flow rate of 0.00785 kg/s, tilt angle of 45°, solar radiation of 450 W/m2, and a temperature of 27 °C. The corresponding outputs are the efficiency at 28.88% and the exergetic efficiency at 5.15%.