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Publisher: IEEE Computational Intelligence Society
Languages: English
Types: Article
Subjects:
Extrusion is a fundamental technique of processing polymeric materials, and the thermal homogeneity of the process melt output is a major concern for high-quality extruded products. Therefore, accurate process thermal monitoring and control are highly invaluable for product quality control. However, most of the industrial extruders use conventional thermocouples whose measurements are limited to a single point and are highly influenced by barrel metal wall temperature. It has shown that the melt temperature varies considerably with the die radial position, and hence, point-based measurements are not sufficient to determine the actual thermal stability across the melt flow. Therefore, thermal control techniques based on such point/bulk measurements may be limited in performance. In addition, the majority of process thermal control methods are based on linear models and are not capable of dealing with process nonlinearities. In this study, a review of the previous work relating to extruder melt temperature control is presented while identifying their limitations. A novel model-based control approach is then proposed to control the polymer extrusion process incorporating a melt temperature profile prediction soft sensor and fuzzy logic. The results show that the proposed controller is good in achieving the desired average melt temperature across the melt flow while minimizing the melt temperature variance. The adjustments made by the controller to the manipulated variables confirmed that it has the capability of adjusting the suitable variables, depending on the different situations encountered. Therefore, this will be a promising alternative to linear control techniques and control techniques based on point/bulk thermal measurements which are common in the present industry.
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