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dc.contributor.authorDeng, J.
dc.contributor.authorLi, K.
dc.contributor.authorHarkin-Jones, E.
dc.contributor.authorPrice, M.
dc.contributor.authorFei, M.R.
dc.contributor.authorKelly, Adrian L.
dc.contributor.authorVera-Sorroche, Javier
dc.contributor.authorCoates, Philip D.
dc.contributor.authorBrown, Elaine C.
dc.date.accessioned2016-11-23T18:23:21Z
dc.date.available2016-11-23T18:23:21Z
dc.date.issued2014
dc.identifier.citationDeng J, Li K, Harkin-Jones E et al (2014) Low-cost process monitoring for polymer extrusion. Transactions of the Institute of Measurement and Control. 36(3): 382-390.
dc.identifier.urihttp://hdl.handle.net/10454/10618
dc.descriptionNo
dc.description.abstractPolymer 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 measured directly using current sensory technology. The employment of on-line, in-line or off-line rheometers is sometimes useful, but these instruments either involve signal delay or cause flow restrictions to the extrusion process, which is obviously not suitable for real-time monitoring and control in practice. In this paper, simple and accurate real-time energy monitoring methods are developed. This is achieved by looking inside the controller, and using control variables to calculate the power consumption. For viscosity monitoring, a 'soft-sensor' approach based on an RBF neural network model is developed. The model is obtained through a two-stage selection and differential evolution, enabling compact and accurate solutions for viscosity monitoring. The proposed monitoring methods were tested and validated on a Killion KTS-100 extruder, and the experimental results show high accuracy compared with traditional monitoring approaches.
dc.relation.isreferencedbyhttps://doi.org/10.1177/0142331213502696
dc.subjectNon-linear modelling
dc.subject; Eenergy
dc.subject; Soft sensor
dc.subject; Process modelling
dc.subject; Polymer extrusion
dc.subject; Optimisation
dc.subject; Identification
dc.subject; Algorithm
dc.subject; Viscosity
dc.subject; Search
dc.titleLow-cost process monitoring for polymer extrusion
dc.status.refereedYes
dc.typeArticle
dc.type.versionNo full-text in the repository


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