Estimation of the Experimental Drying Performance Parameters Using Polynomial SVM and ANN Models

Abstract

The utilization of solar energy in Turkey is very popular because of yearly high solar radiation compared to other countries. One of the common usage area of solar energy is food drying processes. Foods are generally dried under direct sunlight. However, the quality of the dried product exposed to solar radiation reduces. Additionally, the food product dried in outdoors is also exposed to the negative effects of the external environment and thus adversely affects the product quality. In order to overcome these problems, many studies are carried out on solar assisted drying systems. It is very important to calculate or modeling the drying parameters for the design of solar assisted drying systems. In recent years, interest on calculative intelligence methods increases due to the fact that it has high predictive power in modeling of systems. In this study, performance parameters such as solar collector efficiency (ηc), drying rate (DR) and convective heat transfer coefficient (hc) obtained from a solar energy assisted dryer for different products were estimated by Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The accuracy criteria of the predicted results for each model were determined and compared. It was shown from the results that the best converging models of DR and ηc parameters were ANN and SVMC, respectively. However, it was observed that SVML was the best convergent model for hc values obtained from apple product, and ANN model was the best convergent model for hc values obtained from other products.