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Modeling the extrusion process is complex because of the many confounding variables and the dynamic properties of the materials subjected to heat, shear and pressure. Efforts have been made to predict various system and product properties using a flow-modeling phenomenological approach. However, because of the many assumptions and the complexities involved, these models become often impractical for application. In this study, dimensional analysis was used to determine significant dimensionless parameters for the inputs and outputs of a model constrained only by the available experimental data. Thereafter, a rule-based FIS was used, instead of a conventional exponential model, for the prediction of output dimensionless parameters. Optimization or selection of subtractive cluster radii for FIS was achieved using a genetic algorithm. Data were obtained from 4 × 3 × 3 × 2 experimental design (16, 20, 24 and 28% moisture contents; 80, 120 and 160 rpm screw speeds; 3, 4 and 5 mm nozzle diameters and 120 and 140C barrel temperatures) and 3 × 2 (80, 120 and 160 rpm screw speeds; 120 and 140C barrel temperatures for a 4 mm nozzle diameter and 26% moisture content). These factorial design experiments were conducted using a laboratory twin-screw extruder. After training, the FIS captured the process trend based on the experimental data. Correlation coefficient (r2) values were found higher than those obtained from a linear regression model. Practical Applications Extrusion process is widely used to produce human foods, dog foods and polymers. Prediction of extrusion performance and scale-up of the process are very challenging because of complex and dynamic characteristics of biological materials when shear and heat are applied simultaneously. This study presents a novel modeling method to predict dimensionless parameters involving MRT and torque experienced by the extruder. The model was validated with data collected from extrusion of corn starch. This model can be used by producers to predict change in energy consumption and MRT and facilitate scale-up when feed moisture content, die diameter, screw speed and barrel temperature are changed. The modeling methodology can be further expanded to predict other extrusion performance parameters and for other materials by training and validating the model with new data set. |
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