Genetic algorithm in the design of soybean silos for airflow homogenization¹

Algoritmo genético no projeto de silos de soja visando a homogeneização do fluxo de ar

HIGHLIGHTS:

The Darcy-Forchheimer model was parameterized for airflow in bulk soybeans.

Genetic algorithms were developed to optimize the grain storage bin dimensions and air inlet configuration.

The developed genetic algorithm increased the airflow homogeneity for the studied case.

ABSTRACT

One of the main processes used to maintain grain quality in large storage bins is aeration. An optimization model that considers the limited and discrete nature of the air inlets, in addition to the geometric characteristics of the storage bin, could produce results more easily applicable results for the design of new grain storage bins. The objective of this study was to parameterize and to apply the Darcy-Forchheimer model for simulating airflow in soybean grain mass as function of grain layer height, and develop an artificial intelligence method based on genetic algorithm for optimizing grain storage bin dimensions and air inlet configurations to obtain a more homogeneous airflow in the grain mass. The parameterization considered the effect of grain compaction and the OpenFOAM simulations showed good agreement with the experimental data. The proposed genetic algorithm was able to increase the airflow homogenization when compared to the grain storage bin used as reference.

Key words:
soybean; aeration; OpenFOAM software; Darcy-Forchheimer model; grain storage bin