Development and validation of a headspace model for a stored grain silo filled to its eave

Abstract

A headspace computational model was formulated to predict air temperature and relative humidity (RH) in a grain silo using energy and mass balance principles. The headspace domain consisted solely of the headspace volume between the grain surface and the roof without exposed side wall, i.e., grain was filled to the eave, and was divided into nine control volumes. This approach resulted in nine headspace air temperatures and RHs which is unique compared to other published models. Solar radiation and convective heat transfer influencing the headspace temperature were included in the model. The periodic changes in solar radiation and wind speed induced temperature and humidity variations in the silo headspace. The driving factor behind the changes in headspace RH was the interchange of air through vents, the eave openings and from the grain mass. The headspace model predicted the air temperature and RH in each control volume and the associated roof temperatures. The predicted results were validated using data collected in the Stored Product Research and Education Center (SPREC) pilot silo during 2008. The standard error of prediction between the observed and predicted headspace temperatures was in the range of 3.9–5.4 °C, and between the predicted and observed headspace RH was 11.6%. It was concluded that the developed model can be used to predict temperature and relative humidity in the headspace of a silo filled level to its eave with reasonable accuracy.