Publication date
2014Author
Deng, J.Li, K.
Harkin-Jones, E.
Price, M.
Fei, M.R.
Kelly, Adrian L.
Vera-Sorroche, Javier
Coates, Philip D.
Brown, Elaine
Keyword
Non-linear modellingEenergy
Soft sensor
Process modelling
Polymer extrusion
Optimisation
Identification
Algorithm
Viscosity
Search
Peer-Reviewed
YesOpen Access status
closedAccess
Metadata
Show full item recordAbstract
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 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.Version
No full-text in the repositoryCitation
Deng 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.Link to Version of Record
https://doi.org/10.1177/0142331213502696Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1177/0142331213502696