نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه ماشینهای کشاورزی، دانشکده کشاورزی سنقر، دانشگاه رازی، کرمانشاه، ایران
2 دانش آموخته دکترا ماشینهای کشاورزی، دانشکده کشاورزی، دانشگاه تهران،کرج ، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Drying is one of the most effective methods to increase the shelf life of food products. Increasing efficiency and reducing energy consumption in drying is essential. Saving time and energy are the best advantages of the microwave drying method. So this work aims to investigates the influences of microwave power (P), mass of product (M) and time (T) on the absorbed energy and energy loss and also predict the efficiency during microwave dehydration of apple leaves. We perform the investigation at P=100, 300 and 600 W, M=20, 40, 60, 80 and 100 g; and T=60, 90, 120, 150 and 180 s. In this paper, three different machine learning methods are used to simulate the absorbed energy and energy loss of apple along with prediction of the microwave efficiency during dehydration process. These methods are: artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS) and support vector machines (SVM) coupled with firefly algorithm (FFA) Our results indicates that the amount of absorbed energy by apple during microwave dehydration strongly depends on on the amount of the mass. The highest and lowest efficiency of the microwave was observed in the power of 600 W and mass of 100 g, and the lowest efficiency was observed in the time of 180 s and power of 100W. Also, absorbed energy at 600W was found to be almost four times higher than that at 100 W. ANFIS and SVM-FFA showed good results than ANN for predicting absorbed energy, energy loss and microwave efficiency.
کلیدواژهها [English]