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Application of artificial neural network for the prediction of jaggery mass during drying inside the natural convection greenhouse dryer

机译:人工神经网络在自然对流温室干燥机干燥过程中粗大质量预测中的应用

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In this paper, an attempt is made to predict the hourly mass of jaggery during the process of drying inside greenhouse dryer under the natural convection mode. Jaggery was dried until the constant variation in the mass of jaggery. Artificial neural network (ANN) is used to predict the mass of the dried jaggery on hourly basis. Solar radiation, ambient temperature and relative humidity are input parameters for the prediction of jaggery mass in each hour in the ANN modelling. The results of the ANN model are also validated with experimental drying data of jaggery mass. The statistical parameters such as root mean square error and correlation coefficient (R~2) are used to measure the difference between values predicted by the ANN model and the values actually observed from the experimental study. It was found that the results of the ANN model and experimental are shown fairly good agreement.
机译:本文尝试预测自然对流模式下温室干燥机内部干燥过程中每小时的锯齿量。干燥粗麻布,直到粗麻布质量不断变化。人工神经网络(ANN)用于预测每小时干燥的棕榈糖的质量。在ANN建模中,太阳辐射,环境温度和相对湿度是输入参数,用于预测每小时的锯齿状质量。人工神经网络模型的结果也得到了具有粗略质量的实验干燥数据的验证。统计参数如均方根误差和相关系数(R〜2)用于测量ANN模型预测的值与实验研究实际观察到的值之间的差异。结果表明,人工神经网络模型的结果与实验结果吻合良好。

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