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 (<i>v<sub>d</sub></i>) of an active in...
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MDPI AG
2021-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/16/7616 |
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| author | M. Moheno-Barrueta O. May Tzuc G. Martínez-Pereyra V. Cardoso-Fernández L. Rojas-Blanco E. Ramírez-Morales G. Pérez-Hernández A. Bassam |
| author_facet | M. Moheno-Barrueta O. May Tzuc G. Martínez-Pereyra V. Cardoso-Fernández L. Rojas-Blanco E. Ramírez-Morales G. Pérez-Hernández A. Bassam |
| author_sort | M. Moheno-Barrueta |
| collection | DOAJ |
| description | 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 (<i>v<sub>d</sub></i>) of an active indirect solar dryer for plantain and taro (<i>Colocasia antiquorum</i>). 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 (<i>v<sub>d</sub></i>), obtaining a linear regression coefficient <i>R</i> = 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. |
| first_indexed | 2024-03-10T09:01:19Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:01:19Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj.art-5d0dc52903de4890a9f9efc071c899a22023-11-22T06:44:19ZengMDPI AGApplied Sciences2076-34172021-08-011116761610.3390/app11167616Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural NetworkM. Moheno-Barrueta0O. May Tzuc1G. Martínez-Pereyra2V. Cardoso-Fernández3L. Rojas-Blanco4E. Ramírez-Morales5G. Pérez-Hernández6A. Bassam7Col. Magisterial, Centro, Zona de la Cultura, Avenida Universidad S/N, Universidad Juárez Autónoma de Tabasco, Villahermosa C.P. 86040, MexicoFacultad de Ingeniería, Campus V, Predio S/N Por Av. Humberto Lanz Cárdenas y Unidad Habitacional Ecológica Ambiental, Col. Ex Hacienda Kalá, Universidad Autónoma de Campeche, Campeche C.P. 24085, MexicoCol. Magisterial, Centro, Zona de la Cultura, Avenida Universidad S/N, Universidad Juárez Autónoma de Tabasco, Villahermosa C.P. 86040, MexicoFacultad de Ingeniería, Av. Industrias No Contaminantes S/N, Periférico Norte Apartado Postal 150 Cordemex, Universidad Autónoma de Yucatán, Merida C.P. 97310, MexicoCol. Magisterial, Centro, Zona de la Cultura, Avenida Universidad S/N, Universidad Juárez Autónoma de Tabasco, Villahermosa C.P. 86040, MexicoCol. Magisterial, Centro, Zona de la Cultura, Avenida Universidad S/N, Universidad Juárez Autónoma de Tabasco, Villahermosa C.P. 86040, MexicoCol. Magisterial, Centro, Zona de la Cultura, Avenida Universidad S/N, Universidad Juárez Autónoma de Tabasco, Villahermosa C.P. 86040, MexicoFacultad de Ingeniería, Av. Industrias No Contaminantes S/N, Periférico Norte Apartado Postal 150 Cordemex, Universidad Autónoma de Yucatán, Merida C.P. 97310, MexicoIn 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 (<i>v<sub>d</sub></i>) of an active indirect solar dryer for plantain and taro (<i>Colocasia antiquorum</i>). 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 (<i>v<sub>d</sub></i>), obtaining a linear regression coefficient <i>R</i> = 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.https://www.mdpi.com/2076-3417/11/16/7616solar dryerforced ventilationartificial neural network inversemetaheuristic optimizationoptimal operating conditions |
| spellingShingle | M. Moheno-Barrueta O. May Tzuc G. Martínez-Pereyra V. Cardoso-Fernández L. Rojas-Blanco E. Ramírez-Morales G. Pérez-Hernández A. Bassam Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural Network Applied Sciences solar dryer forced ventilation artificial neural network inverse metaheuristic optimization optimal operating conditions |
| title | Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural Network |
| title_full | Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural Network |
| title_fullStr | Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural Network |
| title_full_unstemmed | Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural Network |
| title_short | Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural Network |
| title_sort | experimental evaluation and theoretical optimization of an indirect solar dryer with forced ventilation under tropical climate by an inverse artificial neural network |
| topic | solar dryer forced ventilation artificial neural network inverse metaheuristic optimization optimal operating conditions |
| url | https://www.mdpi.com/2076-3417/11/16/7616 |
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