TY - JOUR

T1 - Identification of the relationship between equilibrium moisture content, dry bulb temperature, and relative humidity using regression analysis

AU - Ray, Charles D.

AU - Gattani, Neelesh

AU - del Castillo, Enrique

AU - Blankenhorn, Paul R.

PY - 2007/4/1

Y1 - 2007/4/1

N2 - This paper evaluates the performance of equilibrium moisture content (EMC) predictions using the least squares regression equation given in The Dry Kiln Operator's Manual (Simpson 1991). The fit of the regression equation in The Manual was found to be adequate only when the dry bulb temperature is below 110°F. At temperatures above 110°F, it generally overestimates EMC. A new polynomial regression equation is presented in this paper to predict EMC at dry bulb temperatures above 110°F. Comparisons between the old and new regression equations show an improvement in the root mean squared error of the predictions of about 44% when using the new equation. The proposed equation facilitates better control of the drying process in computer-controlled kiln applications using prediction equations for EMC estimates.

AB - This paper evaluates the performance of equilibrium moisture content (EMC) predictions using the least squares regression equation given in The Dry Kiln Operator's Manual (Simpson 1991). The fit of the regression equation in The Manual was found to be adequate only when the dry bulb temperature is below 110°F. At temperatures above 110°F, it generally overestimates EMC. A new polynomial regression equation is presented in this paper to predict EMC at dry bulb temperatures above 110°F. Comparisons between the old and new regression equations show an improvement in the root mean squared error of the predictions of about 44% when using the new equation. The proposed equation facilitates better control of the drying process in computer-controlled kiln applications using prediction equations for EMC estimates.

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M3 - Article

AN - SCOPUS:34249103236

VL - 39

SP - 299

EP - 306

JO - Wood and Fiber Science

JF - Wood and Fiber Science

SN - 0735-6161

IS - 2

ER -