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• W1968563074 abstract "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 (R2) 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." @default.
• W1968563074 created "2016-06-24" @default.
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• W1968563074 date "2013-05-09" @default.
• W1968563074 modified "2023-10-14" @default.
• W1968563074 title "Application of artificial neural network for the prediction of jaggery mass during drying inside the natural convection greenhouse dryer" @default.
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• W1968563074 doi "https://doi.org/10.1080/01430750.2013.793455" @default.
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