Drying of Nettle Using Concentrated Air Collector and Concentrated Photovoltaic Thermal Supported Drying System and Modeling with Machine Learning

Authors : Mehmet Onur Karaagac

Pages : 1913-1929

Doi:10.29130/dubited.1460576

View : 42 | Download : 66

Publication Date : 2024-10-23

Article Type : Research Paper

Abstract :This study examines the performance of a solar assisted drying system in the nettle drying process. The drying process works by using thermal energy obtained from solar air collectors and PV modules. The experimental were carried out in October 2022, and the room temperature, total efficiency and moisture content parameters were investigated. The data obtained from the drying system were modelled using machine learning algorithms such as artificial neural networks (ANN), support vector machines (SVM), and gradient boosting decision trees (GBDT). The average thermal energy transferred to the drying cabin was calculated as 154 W, with 77% of this energy was obtained from the air collector and the remaining 23% from the PV module. The stinging nettle was dried from an initial moisture content of 11.18 g water/g dry matter to a final moisture content of 1.18 g water/g dry matter. The average total efficiency of the drying system was found to be 16.8%. Additionally, the results show that the SVM algorithm exhibits the best performance in estimating important parameters such as chamber temperature, moisture content, and total efficiency. Especially in total efficiency prediction. The SVM algorithm has a significant advantage over other algorithms. As a result, it was concluded that the SVM algorithm can be used effectively utilized in solar energy-supported drying systems and can be a precious choice for the optimization of the drying process.
Keywords : Güneş enerjisi destekli kurutma sistemleri, Makine öğrenme algoritmaları, Kurutma prosesi optimizasyonu