Dynamic modelling and intelligent control of a single screw extrusion process

Tan, L.P., 2004. Dynamic modelling and intelligent control of a single screw extrusion process. PhD, Nottingham Trent University.

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In the plastics industry, single screw extruders are widely used to melt the solid polymer. The extruder contains a helical screw with a varying channel depth along the barrel. It is designed to optimise the efficiency of energy conversion, and the consistency of the molten polymer during the operation. The relative motion between the rotating screw and the stationary barrel continuously shears, melts and pumps the molten polymer out of the extruder die. The extrusion process is generally steady, but it is very difficult to maintain constant operating conditions. This is mainly because the process is subjected to various sources of process disturbances including variations in the quality and quantity of the feed polymer, which can result in poor quality product. Therefore, an effective extrusion controller needs to be developed.

The present extrusion controllers have been mostly concentrated on Proportional- Integral (PI) controllers and Self Tuning Regulators (STR). Generally, the resulting control systems are in Single-Input-Single-Output (SISO) structure. The SISO control systems exhibit a major shortcoming that only one process output could be regulated at each control cycle. Past experience suggests that strong interactions exist between the process parameters. This implies that an encouraging control performance could only be attained if the parameter interactions are taken into consideration while calculating a control action.

In this thesis, an intelligent control system namely Fuzzy supervisory indirect Learning Predictive Control (FsiLPC) system is proposed. The system is designed based on Model Based Predictive Control (MBPC), Controller Output Error Method (COEM) and Fuzzy Rule Based System (FRBS). The basic operating mechanism of the FsiLPC system is similar to the MBPC system, with one distinctive operating strategy. A control action in the FsiLPC system is calculated by a fuzzy supervisory unit, rather than using a control law as in a MBPC system. To improve the control action, the COEM is employed to tune the parameters of the fuzzy supervisory unit. This strategy allows the system to accept a predictive model of any structure.

The predictive model in the FsiLPC system needs to predict the behaviour of the extrusion process, and also be adaptive to the varying operating conditions. A semi physical dynamic extrusion model is developed for the needs. The model is governed by a set of partial differential equations, algebraic equations and FRBS sub-models. A hybrid GA-Fuzzy algorithm is implemented to produce an optimal structure for each FBRS sub-model. The sub-models thus obtained show advantages including simpler rule-base and fewer membership functions. These help to improve their interpretability and adaptive ability.

The implementation of the FsiLPC system for the extrusion process has been evaluated by means of simulation studies. The simulation studies include a parametric study and a comparative study. In the parametric study, the characteristics of the FsiLPC system are examined. The results of the study also help in finding suitable settings of the system parameters. The FsiLPC system is then compared with the PI and STR systems in the comparative study. These three control systems are evaluated based on the performance in tracking the changes of desired process output and minimising the impact of process disturbances. The performance of the FsiLPC system is relatively encouraging.

Item Type: Thesis
Creators: Tan, L.P.
Date: 2004
ISBN: 9781369314298
Rights: This thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that no information derived from it maybe published without the author’s prior written consent.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 18 Sep 2020 09:44
Last Modified: 26 Jul 2023 11:14
URI: https://irep.ntu.ac.uk/id/eprint/40800

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