## 3 Results and Discussion
### Optimal Operation Conditions  
In this section, the inverse artificial neural network (ANNi) approach was implemented, considering as an objective function the ANN model developed (architecture 7-8-1; hyperbolic tangent activation function in the hidden layer). Based on the sensitivity analysis results and the discussion in Section 3.3, the ventilator's operating voltage (\(V_{f}\)) was considered as the control variable. To establish a strategy in the theoretical operation that allows maximizing the drying velocity of both fruits (plantain and taro), the optimization  
Figure 8: Sensitivity analysis: the relative importance of the input variables in the drying rate for solar dryer. of the objective function was expressed as follows in order to evaluate various operating scenarios considering the range used in the experimental process:  
\[\text{maximize}:\ \min(y)=-v_{d,sim}\left(V_{f}\right)\text{,subject to}:\ V_{fj, min}<V_{fj}<V_{fj,max}\text{ where }j\ =\ 6\text{to}12. \tag{12}\]  
To execute the optimization by ANNI, a Matlab script, aided by the Global Optimization Toolbox [45], was created in which the feasibility of the solution was evaluated using three different metaheuristic algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) (described in detail in Appendix A). These algorithms were selected because they have shown successful results in the optimization of complex problems in diverse fields of research [46; 47; 48; 49]. Using Equation (13), the error percentage (\(\epsilon\)) was calculated to identify the ANN-metaheuristic optimization tuple that best approximates its calculations. In the same way, the computation time was recorded due to its importance during the possible execution of the optimization strategy in the operation of the fan voltage in real time. \[\epsilon=\frac{V_{fexp}-V_{fopt}}{V_{fexp}}\times 100\% \tag{13}\]  
Table 7 shows the results of implementing ANNI, considering a set of random samples of both plantain and taro that were not part of the training or validation model stages. The execution of the algorithms was carried out on a personal computer with an Intel i7 processor, eight cores, 16 threads, and 32 GB of RAM. Because the algorithms work based on stochastic methods, a total of 20 runs were performed to identify the average computation time to reach the solution. It is observed that, for this function, the smallest \(\epsilon\) for each optimization strategy was as follows: 0.83% for ANNI-GA, with a computation time of 4.3 s; 1.11% for ANNI-PSO, with a computation time of 8.7 s; 3.00% for ANNI-ACO, with a computation time of 9.2 s. As can be seen, ANNI-PSO and ANNI-ACO presented the slowest calculation times of the three techniques evaluated; however, the percentage error for the optimal fan voltage for both algorithms was the highest. In comparison with ANNI-PSO and ANNI-ACO, ANNI-GA generated lower error rates, lower by 0.28 and 2.17 percentage points, respectively. Moreover, it presented better calculation times, with differences of 4.4 s and 4.9 s, respectively. The results of the table indicate that ANNI is an appropriate tool for application in indirect solar dryers, obtaining, for taro, error rates in the fan voltage lower than 3.33%, 5.56%, and 6.67% for ANNI- GA, ANNI-PSO, and ANNI-ACO, respectively, according to the experimental data. In the case of plantain, the errors were 5.33%, 4.58%, and 4.89% for ANNI-GA, ANNI-PSO, and ANNI-ACO respectively. By applying three different optimization algorithms to the ANNI, it was possible to obtain the input values associated with the solar dryer. As for the computational time, ANNI-GA applied to the plantain (5.3 s) required less time than for taro (5.5 s). When comparing the computation time with the different algorithms for the case of taro, a maximum difference of 4.4 s was obtained between ANNI-GA and ANNI-PSO, while 2.9 s was measured between ANNI-GA and ANNI-ACO, and 1.5 s was obtained between ANNI-PSO and ANNI-ACO.