## 3 Results and Discussion
### Artificial Neural Network Model  
A mathematical model based on ANN was developed to obtain a numerical expression that relates the environmental conditions of the experiment and the operating variables of the solar dryer. All the numerical calculations were executed through the MATLAB software and its ANN toolbox complement, due to the ease and flexibility of the platform for the application of artificial intelligence codes [38]. This model considers \(G_{T}\), \(T_{a}\), \(RH\), \(w_{v}\), \(T_{i}\), \(T_{o}\), and \(V_{f}\) as the input variables and the drying velocity (\(v_{d}\)) as the output variable. The ANN model was trained through a supervised learning process in which 80% of the data were used to train the model. This process was aimed at minimizing the error between the measured dry velocity and that generated by the model. In parallel, 10% of the data assigned to validation were used to avoid overtraining the model and guarantee its generalizability. Lastly, the remaining 10% of data were used to verify performance using unknown data. During this supervised learning process, the maximum number of iterations and the validation check (deviation index between the training and validation results) were considered as stopping criteria. Furthermore, in order to enable the general predictive capacity of the ANN, the k-validation approach suggested by [PERSON] and [PERSON] [41] was conducted. Table 5 presents the evaluation of the different network architectures (using the hyperbolic tangent and logistic sigmoid activation functions) to find the optimal ANN model. According to the analysis, for both cases, although the network architecture with 10 hidden neurons presented the best performance, the differences obtained with respect to the architecture with eight hidden neurons were minimal. Thus, to obtain a shorter computing time and better use regarding computational resources, the latter was chosen; the architecture of the artificial neural network can be observed in Figure 6.  
Figure 7 shows the regression curves between the simulated (\(v_{d,sim}\)) and experimental (\(v_{d,exp}\)) data of \(v_{d}\), for the models with the hyperbolic tangent (Figure 7a) and logistic sigmoid (Figure 7b) transfer functions. As can be seen, the model based on hyperbolic tangent (\(v_{d,sim}\) = 0.9865 \(v_{d,exp}\) + 0.1000) was the one with the least dispersion of data and was more in line with the values measured experimentally. The above is supported by the statistical information in Table 5, where, for the model with eight neurons in the hidden layer and with the hyperbolic tangent transfer function, the statistical parameters were as follows: \(RMSE\) = 0.1912, \(R^{2}\) = 0.9647, \(R\) = 0.9822, and \(MAPE\) = 31.9%. For the same neural model, but with the logistic sigmoid transfer function in the hidden layer, we had the following parameters: \(RMSE\) = 0.2139, \(R^{2}\) = 0.9521, \(R\) = 0.9757, and \(MAPE\) = 25.4%. In relation to results with this same approach reported in the literature, [PERSON], et al. (2012) reported acceptable \(RMSE\) values of 0.4052 and 2.9178, while, in this work, the corresponding values were 0.1992 and 0.2139. Furthermore, [PERSON], et al. (2012) performed modeling with a similar ANNi approach, obtaining \(R\) values of 0.9753 and 0.9678, which were considered acceptable to proceed with the optimization phase. Compared to this, this study presented better correlation values (\(R\) of 0.9822 and 0.9757). In the case of \(MAPE\), high values were presented, although the model was adequately adjusted, as proven above and as shown by the regression curves (See Figure 7). This is due to the fact that the drying curves presented regions close to zero, due to the phenomenology of the drying process itself, where the drying rates were close to zero toward the end of each experiment. This fact is important because the \(MAPE\) divides the absolute error from the real data; hence, real values that are close to 0 can significantly increase the \(MAPE\)([PERSON], 2012).