A comparative artificial intelligence approach to inverse heat transfer modeling of an irradiative dryer

In this work, a variety of new approaches are developed and results are compared for solving inverse heat transfer problems where radiation is the dominant mode of thermal energy transport. An artificial neural network (ANN), two hybrid methods of genetic algorithms and artificial neural networks (GA–ANNs), and an adaptive neuro-fuzzy inference system network (ANFIS) were designed. These were trained and then employed to estimate the required input power in an irradiative batch drying process. A comparison of the results shows that the most accurate method is ANFIS but the number of parameters in ANFIS is larger than ANNs. Consequently, the ANFIS solution is time consuming in this application; however other neuro-fuzzy techniques may require fewer parameters and these will be considered in future studies. For the studied ANNs, the hybrid method of GA–ANN is optimal using the Levenberg–Marquardt optimization algorithm during back propagation in terms of accuracy and network's performance.