Development of a self-diagnosis software to enhance stored-grain cooling aeration system performance

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Development of a self-diagnosis software to enhance stored-grain cooling aeration system performance

Description

Abstract: Investigations about the ways to optimize cooling aeration operations were carried out at the experimental and training station of the French Institute for cereal grain quality, ARVALIS – Institut du végétal, in taking into account meteorological data at a given storage site. An aeration self-diagnostic software was designed in order to enable the grain store managers to check the performances and efficiency of their grain aeration equipment. Based on fundamentals of thermodynamic, this software can determine airflow rate, total pressure and static pressure of the air in an aeration system (canalor duct). The software calculates the number of required hours to achieve each cooling step. This duration is then compared to the number of available time periods to achieve efficient aeration by exploiting specific meteorological data at the storage site or in the vicinity. The software allows to validate the appropriate design of any aeration system.This study aimed to determine the reliability of values calculated by the software through a comparison with values measured through more specific assessment tools. The observations made on a fan test bench helped to validate the calculation method. Under real storage site conditions, the correlation between measured and calculated values is variable according to studied criteria (air flow rate, total pressure, static pressure). Overall, software performance, although imperfect, can be considered as acceptable for such a tool that cannot take into account all the specificities of each storage site. In the most critical aeration situations (less than 20 km3/h of flow rate, total and static pressures above 200 mm H2O (2 kPa) and more than 2 °C of air warming), the software gave complete satisfaction. Despite some identified weaknesses, the software can detect sub-optimal situations (e.g. undersized aeration power) in accordance with its original purpose.

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