1. Jing Deng
Quen’s University Belfast, UK
PRO-TEM Special Session on Thermal Energy Management: Energy System & Efficiency Improvement
Fuzzy Logic Based Melt Quality Control of a
Single Screw Extruder
6 July 2012
3. The project: “Thermal Management in Polymer Processing ”
1. Background
The aim of the proposal is to develop methods and technologies to facilitate the
efficient use of thermal energy in existing polymer processing plant operation
and in the design of future plants.
4. 1. Background
Develop monitoring and control techniques to optimise
energy use and quality in extrusion
• Development of inferential techniques to monitor
melting stability.
• Development of low cost techniques to monitor
power consumption on-line
• Development of an ‘expert’ system for machine set-
up and on-line optimisation
WP3
8. 3. Extruder at QUB
Killion KTS-100 laboratory single-screw extruder
Geometrical screw parameters
DC motor power (kW) 2.24
Screw diameter (mm) 25
No. of barrel temperature
zones
3
Additional temperature zones
connected
3
Operating speed range (rpm) 0-115
Extruder Specifications
9. 3. Extruder at QUB
Melt
pressure
Transducer
Slit Die
3 Melt pressure
Transducers to measure pressure drop
Melt Temperature
measured by
Infrared Sensor
Power
consumption
by HIOKI
3169-20
10. What affects energy efficiency:
1. Heat lost to environment
2. Unnecessary high temperature settings
3. Incomplete melt causes screw torque increase
4. Too cold of feed area cooling
5. Unnecessary low throughput
3. Extruder at QUB
11. 4. Implementation of Fuzzy control
Pressure
transducers Screw speed
Infrared sensor
National instrument
Compact FieldPoint
cFP-1808
cFP-AO 210cFP-SG 140cFP-TC 120 cFO-AI 10
Power meter
14. Fuzzy control
“Fuzzy logic is a method of rule-based decision making used for
expert systems and process control”
– ”PID and Fuzzy Logic Toolkit”
Advantages:
Model-free control.
Easier implementation for multi-input and multi-output system.
Robust to the change of process condition and interruptions.
Toolbox available in both Matlab and Labview.
“The problem lends itself to a rule-based control architecture and
appropriate fuzzy-expert schemes will be explored”
- “Thermal Management in the Process Industries” proposal
4. Implementation of Fuzzy control
16. ‘Fuzzy system designer’ is included in the “PID and Fuzzy
Logic Toolkit” in Labview 2011
4. Implementation of Fuzzy control
17. Without control
4. Implementation of Fuzzy control
Temperature fluctuations
at constant screw speed
Large fluctuation can be
observed on the melt pressure
(Material used was LDPE)
18. Closed-loop melt pressure control
Pressure variations are within ±0.03MPa
4. Implementation of Fuzzy control
20. 5. Future work & Summary
Developing viscosity control
Viscosity is good indicator to
the melt qualityChallenge: No direct viscosity measurement.
Solution: “Soft-sensor” approach based on
mathematical model
21. 5. Future work & Summary
Optimizing energy usage
• Feed zone cooling temperature optimization
• Barrel temperature settings optimization
• Throughput rate optimization
• Machine start-up time reduction
22. 5. Future work & Summary
• This work is to improve both the energy efficiency and product
quality of polymer extrusion process.
• Platform, including extruder, real-time data acquisition, and
LabVIEW interface have been developed.
• Fuzzy control has been developed for melt pressure and melt
temperature.
• Future work is to develop the viscosity control and incorporate
adaptive learning and optimization abilities to reduce the energy
consumption and improve product quality.
23. If you have more questions, please don’t hesitate to email the author at: j.deng@qub.ac.uk
