The present work deals mainly with dehydration characteristics of onion slices. Microwave power levels of 100, 350, 550 and 750 W was practiced to dry onion slices with thicknesses of 2.5, 5, 7.5 and 10 mm. The results showed that moisture diffusivity and specific energy consumption of the process increased with both increasing microwave power and the samples thickness, and ranged from 0.82 × 10⁻⁸ to 6.13 × 10⁻⁸ m² s⁻¹ and from 0.82 to 5.43 MJ kg⁻¹ _water, respectively. The average activation energy varied in the range of 1.28–1.77. Furthermore, for simulation of drying process and to predict the moisture removal behavior of the samples, multi-layer feed-forward (MLF) artificial neural network (ANN) was employed. Practicing different networks and based on statistical parameters, the best topology, transfer functions and training algorithms were determined. The results revealed that, as a powerful tool, ANN modeling could be effectively used to predict drying kinetics and determine the moisture content of the samples.

目前的工作主要涉及洋葱片的脱水特性。实践中使用100、350、550和750 W的微波功率来干燥厚度为2.5、5、7.5和10 mm的洋葱片。结果表明，随着微波功率的增加和样品厚度的增加，该工艺的水分扩散率和比能耗增加，范围从0.82×10 ^-8到6.13×10 ^-8  m ²  s ^-1和从0.82到5.43 MJ kg ^-1 _水， 分别。平均活化能在1.28–1.77范围内变化。此外，为了模拟干燥过程并预测样品的水分去除行为，采用了多层前馈（MLF）人工神经网络（ANN）。通过使用不同的网络并根据统计参数确定最佳的拓扑，传递函数和训练算法。结果表明，作为一种功能强大的工具，人工神经网络建模可有效地用于预测干燥动力学并确定样品的水分含量。