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Drying of black cumin (Nigella sativa) in a microwave assisted drying system and modeling using extreme learning machine

Balbay, Asim, Kaya, Yilmaz, Sahin, Omer
Energy 2012 v.44 no.1 pp. 352-357
Nigella sativa, algorithms, data collection, drying, microwave treatment, neural networks, neurons, prediction, seeds, temperature, water content
Drying characteristics of Black cumin seeds (BCs) (Nigella sativa) with initial moisture content 58.14% (d.b) was investigated in microwave assisted drying system. The experiments were carried out in two aspects which were the BCs drying with different temperatures (35, 40 and 50 °C) and different microwave power levels (250, 500 and 750 W). The results showed that the drying rates of BCs have high efficiency with drying temperatures at constant microwave power level. Furthermore, in present study, the applications of Extreme Learning Machine (ELM) and Artificial Neural Network (ANN) for predicting the moisture ratio (MR) (output feature for ELM modeling) were investigated. Microwave temperature, microwave power and drying time were input layer features for the modeling. An ELM model by 93 neurons with Sine transfer function in hidden layer was selected. The results revealed that a network with the Sine function made the most accurate prediction for the BCs drying system. For BCs all data set, maximum R² and minimum RMSE (root mean square error) were found as 0.9987 and 0.0123, respectively.