یک رویکرد هوش مصنوعی برای مدلسازی حرارتی معکوس یک خشک کن رادیویی؟

In a simple laboratory drying furnace, various input signals (different input power functions) were input to the dryer's halogen lamp and the resulting temperature history were measured and recorded for a point on the bottom surface of the dryer. After estimating the order, the sampling time and the dead-time of the system, the recorded data were arranged for inverse modelling purposes. Next, an artificial neural network (ANN) was designed and trained to play the role of the inverse heat transfer model. The results showed that ANNs are applicable to solve inverse heat estimation problems of irradiative batch drying process. An important advantage of this method in comparison with classical inverse heat transfer modelling approaches, detailed knowledge of the geometrical and thermal properties of the system (such as wall conductivity, emissivity , etc.) is not necessary. Such properties are difficult to measure and may undergo significant changes during the temperature transient.