## 4 Conclusions  
In this work, the design, instrumentation, experimental evaluation, and modeling of an indirect forced convection solar dryer with a chimney-type drying chamber were presented for its application in taro and plantain crops. A hybridization between artificial intelligence (artificial neural networks) and metaheuristic optimization techniques was established to obtain the operating conditions of the solar thermal system that maximize the drying velocity of the fruits. The system was evaluated under tropical climate conditions of the Mexican southeast by varying the fan voltage between 6 V and 12 V. The experimental results showed that, by applying a voltage of 9 V to the fan, the highest drying velocities were achieved for both fruits, which helped narrow down the search surface for the optimal answer. From the point of view of both regional fruits, it was observed that, as the plantain has less porosity than the taro, it presented the best drying curves, taking less time to reach the critical moisture content. At the same time, the higher critical moisture content in taro allowed it to have a greater amount of water in its last drying stage, thus leading to higher drying velocities than in plantain. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**ANNi-GA**} & \multicolumn{3}{c}{**ANNi-PSO**} & \multicolumn{3}{c}{**ANNi-ACO**} \\ \cline{3-11}  & \multicolumn{2}{c}{**Experimental**} & \multicolumn{3}{c}{} & \multicolumn{2}{c}{\(V_{f}\)} & \multicolumn{3}{c}{\(V_{f}\)} & \multicolumn{3}{c}{\(V_{f}\)} \\  & \multicolumn{2}{c}{**Data**} & \multicolumn{3}{c}{**(V)**} & \multicolumn{2}{c}{**(V)**} & \multicolumn{2}{c}{**(V)**} & \multicolumn{2}{c}{**(V)**} \\ \cline{2-11}  & \multicolumn{2}{c}{\(v_{d}\)} & \multicolumn{2}{c}{\(V_{f}\)} & \multicolumn{2}{c}{**Optimal**} & \(\varepsilon\) & \multicolumn{2}{c}{**Computing**} & \multicolumn{2}{c}{**Time (s)**} & \multicolumn{2}{c}{**Optimal**} & \(\varepsilon\) & \multicolumn{2}{c}{**Computing**} \\  & \multicolumn{2}{c}{**(g/min)**} & \multicolumn{2}{c}{**(V)**} & \multicolumn{2}{c}{**Optimal**} & \(\varepsilon\) & \multicolumn{2}{c}{**Time (s)**} & \multicolumn{2}{c}{**Time (s)**} & \multicolumn{2}{c}{**Time (s)**} \\ \hline \multirow{7}{*}{Taro} & 1 & 0.0667 & 6 & 5.90 & 1.67 & 3.4 & 6.20 & 3.33 & 3.3 & 5.70 & 5.00 & 4.0 \\  & 2 & 0.9333 & 6 & 6.17 & 2.83 & 3.7 & 5.75 & 4.17  
An artificial neural network model was developed using the artificial intelligence application approach to estimate the drying velocity of both fruits, considering as independent variables the fan voltage, solar irradiance, ambient temperature, wind velocity, relative humidity, and the inlet and outlet temperatures of the solar dryer. The best network architecture consisted of eight hidden neurons, obtaining statistical values of _RMSE_ of 0.1912, _R\({}^{2}\)_ of 0.9647, \(R\) of 0.9822, and _MAPE_ of 31.9%, for a hyperbolic tangent transfer function in the hidden layer. Although the aim was not to replace existing theoretical models, the results indicate that the ANN is an efficient alternative to synthesize the thermal phenomena inside the solar dryer and estimate the drying velocity with reduced error margins. The first contribution of the ANN model was to perform a sensitivity analysis to determine the degree of influence of the input variables on the drying velocity of the evaluated fruits and its relationship with the phenomenology of the drying process. Except for the wind velocity, all the variables had a significant impact on the drying velocity, and the fan voltage could be determined as the control variable allowing us to achieve the optimal theoretical response. The second theoretical contribution of the ANN model was its use as an objective function to optimize the drying velocity. The application of the ANNi method allowed finding the optimal voltage for a given drying velocity.