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

M Moheno-Barrueta,G Martínez-Pereyra,G Pérez-Hernández,L Rojas-Blanco,O May Tzuc,V Cardoso-Fernández,A Bassam,E Ramírez-Morales

doi:10.3390/app11167616

Abstract

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 (vd) 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 (vd), 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.

Highlights

In Latin America, the Mexican agricultural industry is one of the most significant, establishing itself as the largest producer of vegetables in the region and occupying the second place in fruit exports worldwide
Agricultural activities play an essential role in the Mexican economy, with a contribution to gross domestic product (GDP) of more than 2%
A natural ventilation mixed pineapple solar dryer was evaluated for four drying scenarios according to specific drying periods for four typical seasons in Ghana

Summary

Introduction

In Latin America, the Mexican agricultural industry is one of the most significant, establishing itself as the largest producer of vegetables in the region and occupying the second place in fruit exports worldwide. Among the country’s growing regions, the south-southeast zone (made up of the States of Tabasco, Chiapas, Campeche, and southern Veracruz) stands out due to its favorable climatic and hydrographic conditions, where 96% of the agricultural surface has rainwater irrigation. In this region, 50% of the entire country’s water runs off, and the prevailing tropical rainy climate allows for harvests practically all year round [2]. The fact of having harvests almost all year round brings about an economic problem related to the placement and sale of the products. This process is subjected to the postharvest lifetimes of the crop since it can generate an excess of production, saturating the market, or generate economic losses for farmers, due to wastage

Methods

Results

Conclusion