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This thesis is divided into three parts: dynamic modelling, measurement and control. The first part outlines the development of a dynamic model of the extrusion process which satisfies the above mentioned criteria. The dynamic model predicts quasi-3D spatial profiles of the degree of fill, pressure, temperature, specific mechanical energy input and concentrations of inert and reacting species in the extruder. The individual material transport models which constitute the dynamic model are examined closely for their accuracy and computational efficiency by comparing candidate models amongst themselves and against full 3D finite volume flow models. Several new modelling approaches are proposed in the course of this investigation. The dynamic model achieves a high degree of simplicity and flexibility by assuming a slight compressibility in the process material, allowing the pressure to be calculated directly from the degree of over-fill in each model element using an equation of state. Comparison of the model predictions with dynamic temperature, pressure and residence time distribution data from an extrusion cooking process indicates a good predictive capability. The model can perform dynamic step-change calculations for typical screw configurations in approximately 30 seconds on a 600 MHz Pentium 3 personal computer.
The second part of this thesis relates to the measurement of product quality attributes of extruded materials. A digital image processing technique for measuring the bubble size distribution in extruded foams from cross sectional images is presented. It is recognised that this is an important product quality attribute, though difficult to measure accurately with existing techniques. The present technique is demonstrated on several different products. A simulation study of the formation mechanism of polymer foams is also performed. The measurement of product quality attributes such as bulk density and hardness in a manner suitable for automatic control is also addressed. This is achieved through the development of an acoustic sensor for inferring product attributes using the sounds emanating from the product as it leaves the extruder. This method is found to have good prediction ability on unseen data.
The third and final part of this thesis relates to the automatic control of product quality attributes using multivariable model predictive controllers based on both direct and indirect control strategies. In the given case study, indirect control strategies, which seek to regulate the product quality attributes through the control of secondary process indicators such as temperature and pressure, are found to cause greater deviations in product quality than taking no corrective control action at all. Conversely, direct control strategies are shown to give tight control over the product quality attributes, provided that appropriate product quality sensors or inferential estimation techniques are available.",
Genetic Programming entries for Justin Elsey