## 3 Results and Discussion
### Analysis of Prediction Results of BP Neural Network  
Based on the analysis in Section 2.7, the number of input layers of the IANN and OANN models were, respectively, ascertained to be 9 and 7; the outlet layers of the two models are 3, while the other  
Figure 7: The variations of specific recovered exergy, wavelength with drying time (**a**) and the total exergy recovered by the radiator (**b**). key parameters, such as numbers of hidden layers, neurons, training epochs, and the momentum coefficient, should be further determined. The variation range of the key parameters of the two models are tabulated in Table 5.  
Based on the configurations of the ANN models, the 327 experimental data sets were used for the training (training epoch number of 1000, learning rate of 0.1). The variation of MSE values with different ANN configurations for the two models are tabulated in Table 6. As can be seen from the Table 6, the lowest MSE value of the IANN model is ascertained to be \(1.3876\times 10^{-4}\) when the hidden layer number is 2, neuron number is 12, and momentum coefficient is 0.4, while the MSE value of the OANN model is ascertained to be \(2.5324\times 10^{-4}\) when the hidden layer number is 2, neuron number is 12 and momentum coefficient is 0.4. In order to further investigate the influence of the training epochs on the MSE of the ANN models, the different training epochs (300-1500) and neuron numbers (5-13) are selected to train the IANN model with the hidden layer number of 2 and momentum coefficient of 0.4, and OANN model, with the hidden layer number of 3, and momentum coefficient of 0.4. The results are shown in Tables 7 and 8. It obviously can be seen from the Table 7 that the IANN model displays the best prediction performance (MSE = \(0.75975\times 10^{-4}\)) when the training epochs are 1500 and the hidden layer neuron numbers are 12, while the OANN model displays the best prediction performance (MSE = \(1.4542\times 10^{-4}\)) when the training epochs are 1500 and the hidden layer neuron numbers are 10. Accordingly, the architecture of the IANN model is ascertained to be 9-2-12-3, while the OANN model is 7-  
Based on the obtained architecture of the two models, the data sets were trained by the models and the training results are shown in Figure 8. As can be seen from the Figure 8a, the IANN model gets the best prediction performance when the training epochs are 30, where the model gets the lowest MSE value (\(2.1252\times 10^{-4}\)) and the \(R\)-values for the data sets used for training, validating, and testing the model are 0.99852, 0.99623, and 0.99788, respectively. On the other hand, the OANN model gets performs a best prediction performance when the epochs are 31, where the model gets the lowest MSE value (\(2.1125\times 10^{-4}\)) and the \(R\)-values for the data sets used for training, validating, and testing the model are 0.99779, 0.99225, and 0.99231, respectively. Indicating that the established models have sufficient reliability and can be used for predicting the \(MC\), \(\eta_{ex,h}\), and \(Er\). However, compared with the OANN model, the IANN model has a better prediction performance with the higher values of MSE and \(R\)-values, indicating that the IANN model has a better prediction performance. It can be summarized that the ambient conditions should be taken into consideration when establishing the prediction model.