DOI: 10.1094/CC-82-0582 |  VIEW ARTICLE

Empirical Modeling of Die Pressure, Shaft Torque, SME, and Product Temperature of Rice Flour in a Corotating Twin-Screw Extruder.

Hanwu Lei (1,2), R. Gary Fulcher (2,3), Roger Ruan (1,2,4), and Bernhard van Lengerich (5). (1) Department of Biosystems and Agricultural Engineering, University of Minnesota, 1390 Eckles Avenue, St. Paul, MN 55108. (2) Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN 55108. (3) Department of Food Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada. (4) Corresponding author. Also Yangtz Scholar Distinguished Guest Professor, Nanchang University. Phone: 612-625-1710. Fax: 612-624-3005. E-mail: <ruanx001@umn.edu> (5) General Mills, Inc., 9000 Plymouth Ave. N., Golden Valley, MN 55427. Cereal Chem. 82(5):582-587. Accepted June 22, 2005. Copyright 2005 AACC International, Inc.

Empirical models for predicting die pressure, product temperature, shaft torque, and specific mechanical energy (SME) input based on rice flour extrusion using a DNDL-44/28D Buhler twin-screw extruder are presented. The models incorporate the effects of shear rate, barrel temperature, moisture content, flow rate, and screw geometry. The models were tested using rice flour at various screw configurations and extrusion conditions. Die pressure is a function of moisture content, product temperature, and flow rate. By testing the die pressure model, we found that, within the experimental range tested, die pressure was not significantly affected by barrel temperatures and screw configurations. Product temperature and shaft torque are functions of shear rate, moisture content, flow rate, barrel temperature, and screw configuration. Introducing the effect of screw configuration into the models for temperature and shaft torque resulted in an overall improved model performance. Predictions of various models gave good results. Validations of various models were verified using different screw geometries and other processing variables with reasonable accuracy. Extrusion tests indicated that the developed predictive models can be of use for extrusion processing.