Investigation of Pear Drying Performance by Different Methods and Regression of Convective Heat Transfer Coefficient with Support Vector Machine

Mehmet Das,Ebru Akpinar

doi:10.3390/app8020215

Abstract

In this study, an air heated solar collector (AHSC) dryer was designed to determine the drying characteristics of the pear. Flat pear slices of 10 mm thickness were used in the experiments. The pears were dried both in the AHSC dryer and under the sun. Panel glass temperature, panel floor temperature, panel inlet temperature, panel outlet temperature, drying cabinet inlet temperature, drying cabinet outlet temperature, drying cabinet temperature, drying cabinet moisture, solar radiation, pear internal temperature, air velocity and mass loss of pear were measured at 30 min intervals. Experiments were carried out during the periods of June 2017 in Elazig, Turkey. The experiments started at 8:00 a.m. and continued till 18:00. The experiments were continued until the weight changes in the pear slices stopped. Wet basis moisture content (MCw), dry basis moisture content (MCd), adjustable moisture ratio (MR), drying rate (DR), and convective heat transfer coefficient (hc) were calculated with both in the AHSC dryer and the open sun drying experiment data. It was found that the values of hc in both drying systems with a range 12.4 and 20.8 W/m2 °C. Three different kernel models were used in the support vector machine (SVM) regression to construct the predictive model of the calculated hc values for both systems. The mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative absolute error (RRAE) analysis were performed to indicate the predictive model’s accuracy. As a result, the rate of drying of the pear was examined for both systems and it was observed that the pear had dried earlier in the AHSC drying system. A predictive model was obtained using the SVM regression for the calculated hc values for the pear in the AHSC drying system. The normalized polynomial kernel was determined as the best kernel model in SVM for estimating the hc values.

Highlights

The sun is the largest source of carbon-free energy available throughout human history
Three different kernel models were used in the support vector machine (SVM) regression to construct the predictive model of the calculated hc values for both systems
The drying performances of these two different drying methods were compared and it was observed that the air heated solar collector (AHSC) drying system performed a more efficient drying

Summary

Introduction

The sun is the largest source of carbon-free energy available throughout human history. A lot of research has been done to find out how to use and apply solar energy as a primary energy source [1]. Solar energy application is divided into two basic groups. The first is electricity generation using photovoltaic cells, which convert direct solar energy into electricity, and the other is the thermal application category, which involves solar drying [2]. Drying is defined as a process of removing water from a product and can be applied in two steps. The moisture inside the product is removed to the surface and dried as a water vapor in a constant air. The second stage involves a slow drying rate and the drying process varies according

Objectives

Methods

Results

Conclusion