Development of an agricultural multi-zone solar dryer with airflow management using CFD and genetic algorithm

Abstract

This research focuses on the development of an agricultural drying house with multi-zone airflow management using Computational Fluid Dynamics (CFD) combined with Genetic Algorithm (GA) to enhance drying efficiency. The experiment compared the results of a conventional drying house with the improved version by analyzing drying time, energy consumption, average product moisture content, and drying uniformity. The results demonstrated that the drying house utilizing CFD and GA techniques reduced drying time by an average of 30.0 ± 2.6%, decreased energy consumption by 25.0 ± 2.0%, reduced the average product moisture content from 15.5 ± 1.0% to 10.5 ± 0.6%, and increased drying uniformity from 70.0 ± 3.5% to 90.1 ± 2.4%, all with statistical significance. These findings reflect the capability of CFD and GA technologies to improve efficiency, reduce production costs, and enhance the quality of products sustainably. This technology holds significant potential for advancing drying processes in agriculture and the food industry in the future.