2013
DOI: 10.1177/0142331213502696
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Low-cost process monitoring for polymer extrusion

Abstract: Polymer extrusion is regarded as an energy-intensive production process, and the real-time monitoring of both energy consumption and melt quality has become necessary to meet new carbon regulations and survive in the highly competitive plastics market. The use of a power meter is a simple and easy way to monitor energy, but the cost can sometimes be high. On the other hand, viscosity is regarded as one of the key indicators of melt quality in the polymer extrusion process. Unfortunately, viscosity cannot be me… Show more

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Cited by 6 publications
(2 citation statements)
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“…Methods based on fuzzy logic were developed by McAfee, [125], McAfee and Thompson, [126], and later by Liu et al, [127] and Abeykoon [103]. More recent work includes the soft sensor technique of Deng et al, [128] and Abeykoon [102], and the multi-objective optimisation approaches presented by Carrano et al, [129].…”
Section: Statistical Process Controlmentioning
confidence: 99%
“…Methods based on fuzzy logic were developed by McAfee, [125], McAfee and Thompson, [126], and later by Liu et al, [127] and Abeykoon [103]. More recent work includes the soft sensor technique of Deng et al, [128] and Abeykoon [102], and the multi-objective optimisation approaches presented by Carrano et al, [129].…”
Section: Statistical Process Controlmentioning
confidence: 99%
“…Complex industrial processes often have a large number of devices and components, each consuming different amount of energies and working in different states. To audit the energy usage in industry, in particular for SMEs where such information is often unavail-able, or there is no technical support to help them acquire such insight in energy usage or such technical support is too expensive to deploy, it is imperative to develop a low-cost easy accessible platform for real-time energy monitoring at different level of system granularity [5]. The system should be able to collect, pre-process, transmit and store these data for further analysis, i.e.…”
Section: Introductionmentioning
confidence: 99%