MOISTURE CONTENT PREDICTION OF BERGAMOT FRUIT FROM DRYING PROCESS WITH ARTIFICIAL NEURAL NETWORK (ANNs)

摘要：

In this study thin-layer drying of bergamot was modelled using artificial neural network (ANN).An experimental dryer was used.Thin-layer of bergamot slices at five air temperatures (40, 50, 60, 70 & 80 oC), one thickness (4 mm) and three air velocities (0.5, 1 & 2 m/s) were artificially dried.Initial moisture content (M.C.) during all experiments was between 5.2 to 5.8 (g.g) (d.b.).Mass of samples were recorded and saved every 5 sec.using a digital balance connected to a PC.MLP with momentum and levenberg-marquardt (LM) were used to train the ANNS.In order to develop ANNs models, temperatures, air velocity and time are used as input vectors and moisture ration as the output.Results showed a 3-6-1 topology for thickness of 4 mm, with LM algorithm and TANSIG activation function was able to predict moisture ratio with 2 R of 0.99925.The corresponding MSE for this topology was 0.00011.

关键词： bergamot thin-layer artificial neural network levenberg-marquardt momentum

作者: Mohammad Sharifi Shahin Rafiee Mahmoud Omid Seyyed Ahmad Tabatabaeifar Masoud Rezaee

作者单位: Department of Agricultural Machinery Engineering,Faculty of Agricultural Engineering and Technology,University of Tehran,Karaj,31587-77871,Iran

会议类型: 国际会议

会议名称: The 7th Asia-Pacific Drying Conference(第七届亚太地区干燥会议 ADC2011)

会议地点: 天津

会议语种:英文

页码: 1-7

在线出版日期: 2011-09-18（万方平台首次上网日期，不代表论文的发表时间）