外文摘要：After rice is harvested, it must be dried before its products can be stored. Therefore, this paper presented a novel and simple method for improving paddy drying process in a column fluidized bed dryer. Additionally, artificial neural network methods were applied to predict the drying behavior. The experiments were conducted under two different drying chamber characteristics (a conventional chamber and a chamber fitted with nozzle) and four different air flow velocities (3.03, 3.52, 4.12 and 4.85 m/s) at a drying air temperature of 60 degrees C. The results showed that the chamber fitted with the nozzle reduced the drying time by approximately 67, 52 and 38 % at the air velocity of 3.52, 4.12 and 4.85 m/s, respectively. The specified experimental conditions and the calculated moisture content of the paddy in this work were used as input and output data for the artificial neural network, respectively, to predict drying characteristics. The artificial neural networks were developed with various parameters, including activation function, sampling type, split ratio, number of neurons and epoch number. The optimal model provided the root mean squared error of 0.111 and the coefficient of determination of 0.999. The best prediction was observed in the model using rectifier activation function with a split ratio of 0.85, epoch number of 1800, and 50 and 90 neurons in the first and second hidden layers, respectively.
外文关键词：artificial neural network;Paddy drying;Fluidized -bed dryer;Nozzle chamber;Vortex flow generator;Energy analysis
作者：Chokphoemphun, Susama;Hongkong, Somporn;Chokphoemphun, Suriya
作者单位：Kasetsart Univ;Rajamangala Univ
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：220
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台