Neural networks in extrusion process identification and control

Abstract

Although neural networks have become one of the key research objects within artificial intelligence, relatively little information is available on neural networks related to food process control. The interest in such areas as dynamic modelling of food processes has increased, not least due to dramatic improvement and availability of the calculation methods and hardware. In the present case, flat bread extrusion was used as an example food process. Dynamic changes of torque, specific mechanical energy (SME) and pressure were identified (modelled) and controlled using two independently taught feed-forward artificial neural networks (ANN). SME, torque and pressure are system parameters which can be controlled with process parameters, such as feed moisture, mass feed rate and screw speed. Target parameters, such as product expansion index, bulk density, etc. are normally difficult to measure on-line, but can be estimated as functions of the system parameters. For the modelling of the whole flat bread extrusion cooking process a MIMO (multi input and multi output) approach was necessary. The neural network topology for the process model was 21-9-3 and for the controller 18-20-2. The process model was taught with 629 real data samples and the controller with 115 synthetic samples created with the process model. When testing the MIMO controller, the SME and pressure set points were quite well reached. One of the clear advantages of neural networks in the controller design is the ease of constructing a complex MIMO controller.