Submitted to: Applied Engineering in Agriculture
Publication Type: Peer reviewed journal
Publication Acceptance Date: 6/1/2012
Publication Date: 9/1/2012
Publication URL: www.ars.usda.gov/SP2UserFiles/Place/54300520/466%20Development%20equilibrium%20moisture%20relationships%20for%20storage%20moisture%20monitoring%20of%20corn.pdf
Citation: Armstrong, P.R., Casada, M.E., Lawrence, J. 2012. Development of equilibrium moisture relationships for storage moisture monitoring of corn. Applied Engineering in Agriculture. 28(5):677-683. Interpretive Summary: Multipoint measurement of grain moisture within a bin is feasible using relative humidity and temperature measurements to predict grain moisture and is currently offered as a commercial monitoring system. Traditionally, prediction of moisture by relative humidity and temperature measurements was done by generalized mathematical models which are less precise than models developed for a specific grain. Developing a model requires considerable time and effort. This work developed methods to reduce this effort and also outlined methods to improve moisture measurement accuracy. As a result it is feasible that moisture predictions can be tailored to a specific grain in storage with improved moisture accuracy thus facilitating better quality management of general storage of grain and for in-bin drying processes.
Technical Abstract: Commercial systems are currently available to measure grain moisture during storage using relative humidity and temperature sensors and equilibrium moisture, EMC, models. However, the variability of the EMC relationships between grain lots necessitates that a good model needs to be selected or developed for each specific grain. This objective of this research was to develop equilibrium moisture models for seven corn samples, examine their prediction accuracy, and evaluate simplification of the experimental procedures used to develop these models. The models that were developed spanned a broad range of five moisture levels (5-Point models). Models were also developed using a reduced number of levels with a narrower moisture range, models with 4 and 2 moisture levels (4-Point and 2-Point models, respectively). The 4-Point model used all data except the highest moisture level while the 2-Point model used the extreme moisture levels of the 4-Point model. The 5-Point models had the highest standard error of estimates (SEE) averaging 0.90 and 0.82 for adsorption and desorption respectively. The 2-Point model was used to predict all the moisture levels of the 4-Point data; the standard errors of prediction (SEP) averaged 0.41 and 0.39 for adsorption and desorption, respectively, and are only marginally higher than 4-Point models. These results show that a 2-Point model can be used to for accurate moisture measurement across a moisture range common for stored corn and subsequently reducing the amount of work required for model development. The moisture levels suitable for a 2-Point model should be limited to upper and lower moisture contents corresponding to 90% relative humidity and 35% to 50% relative humidity, respectively.