A Review on Residence Time Distribution (RTD) in Food Extruders and Study on the Potential of Neural Networks in RTD Modeling

摘要

Residence time distribution and mean residence time depend on process variables, namely feed rate, screw speed, feed moisture content, barrel temperature, die temperature and die diameter. Flow in an extruder has been modeled by simulating residence time distribution, assuming the extruder to be a series of continuous-stirred-tank or plug-flow reactors. Others have developed relationships for mean residence time as functions of process variables. Better models can be developed using neural networks. As an example, data from the literature were used to model mean residence time as a function of process variables using statistical regression and neural networks. Neural network models performed better than regression models.