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Title: ACCURACY OF GRAIN MOISTURE CONTENT PREDICTION USING TEMPERATURE AND RELATIVE HUMIDITY SENSORS

Author
item Armstrong, Paul
item UDDIN, SHARIF - KANSAS STATE UNIVERSITY
item ZHANG, NAIQIAN - KANSAS STATE UNIVERSITY

Submitted to: Applied Engineering in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/1/2005
Publication Date: 3/3/2006
Citation: Armstrong, P.R., Uddin, S., Zhang, N. 2006. Accuracy of grain moisture content prediction using temperature and relative humidity sensors. Applied Engineering in Agriculture. 22(2): 267-273.

Interpretive Summary: Grain temperature and moisture content (MC) are fundamentally important for safe grain storage. Temperature monitoring of grain is relatively easy, but monitoring MC is not possible because no sensors exist to do this. Relative humidity (RH), however, can be an indirect way to measure MC by measuring the internal RH of the air surrounding the grain. The air RH comes into equilibrium with the grain depending on the grain MC and temperature (T). MC can thus be predicted from equations that are experimentally derived and uses RH and T. Unfortunately these equations are not perfect and have some error because of the way it is derived and due to error of the RH and T sensing. This research found that RH and T sensor error did not influence grain MC prediction as greatly as the equation error. With development of better equations, grain moisture content monitoring in bins could become feasible with useful accuracy.

Technical Abstract: Grain temperature and moisture content (MC) are considered to be principal factors for safe storage of grain. Continuous monitoring of temperatures within grain masses is relatively easy using thermocouples, but monitoring of MC is limited by availability of sensors. However, temperature and relative humidity (RH) can be used to predict grain MC based on equilibrium moisture content (EMC) equations such as the Modified Henderson, Chung-Pfost, or Oswin. These models are limited to quasi-static thermodynamic conditions but do provide a method to predict MC with commercial sensors. Error analysis was performed using EMC relationships and temperature and RH sensor error data to determine the total error in grain MC prediction. Error inherent in the EMC regression model (+/- 2.15 % MC to +/- 3.8 % MC) was greater than the contribution of sensor error (approximately +/- 0.5 % MC to +/- 1 % MC) between storage conditions of 20% to 70% RH. Outside these RH ranges, sensor error can contribute substantially (+/- 2% MC to +/- 8% MC at 95% RH) to the total error. Development of EMC equations that exclude ranges of RH above 80% and below 20% may be desirable in order to develop EMC prediction equations with smaller standard errors due to regression. EMC equations respond differently to sensor error above 70% RH, with the Oswin equation displaying the largest errors for MC prediction. Between 20% RH and 70% RH, there was little difference between the prediction error for the equations.