Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural Network

In this theoretical-experimental study is presented a hybridization strategy based on the application of an inverse artificial neural network model (ANNi) coupled with metaheuristic optimization algorithms to optimize the drying velocity (v(d)) of an active indirect solar dryer for plantain and taro (Colocasia antiquorum). In the experimental stage, both fruits were evaluated in periods from 9:00 a.m. to 5:00 p.m. under a humid tropical climate region, varying the voltage of the air extractor fan (at 6 V, 9 V, and 12 V) to control the fan velocity. The experimental results showed that the maximum drying velocities were reached at 9 V, achieving a drying velocity of 1.5, 0.9, and 0.55 g/min, with a total drying time of 465 min for the taro, and 1.46, 1.46, and 0.36 g/min, with a total drying time of 495 min, for the plantain. As a result of the drying curves, it was observed that the drying velocity is higher in taro than in plantain. Subsequently, an artificial neural network (ANN) architecture was trained using the database generated from the solar dryer's experimental stage. Six environmental variables and one operational variable were considered as the model's inputs, feeding the ANN to estimate the drying velocity (v(d)), obtaining a linear regression coefficient R = 0.9822 between the experimental and simulated data. A sensitivity analysis was performed to determine the impact of all the input variables. A hybrid strategy based on ANNi was developed and evaluated with three metaheuristic optimization algorithms; the best result was obtained by genetic algorithms (ANNi-GA) with an error percentage of 0.83% and an average computational time of 4.3 s. The scope of this optimization strategy was to obtain a theoretical result that allows predicting the behavior of the dryer and improving its performance for its practical application in future work through the implementation in development boards. Lastly, the optimization strategy presented is not limited to indirect solar dryers and opens a research window for evaluating other solar drying technologies.

Automated summary

This study presents a hybridization strategy using an inverse artificial neural network model and metaheuristic optimization algorithms to optimize drying velocity for plantain and taro in an active indirect solar dryer. Experimental results showed that the maximum drying velocities were achieved at 9 V, with taro having higher drying velocity than plantain. The hybrid strategy demonstrated good performance in optimization, particularly with genetic algorithms, indicating potential for future practical applications in solar drying technologies.