Low-cost process monitoring for polymer extrusion

Jing Deng, Kang Li, Eileen Harkin-Jones, Mark Price, Minrui Fei, Adrian Kelly, Javier Vera-Sorroche, Phil Coates, Elaine Brown

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
160 Downloads (Pure)


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
Original languageEnglish
Pages (from-to)382-390
Number of pages9
JournalTransactions of the Institute of Measurement and Control
Publication statusPublished (in print/issue) - 26 Sept 2013


  • Energy efficiency
  • Extrusion
  • Polymers


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