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International Conference on Applied Energy
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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
Outline
Background & Objectives
Control strategies
Extruder at QUB
Future work & Summary
Implementation of Fuzzy control
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.
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
1. Background
Melt pressure
Melt temperature
Feed rate
Barrel temperature
Screw speed
Viscosity
2. Control strategies
Current control
PID control for Barrel temperature settings
PID control for screw speed setting
2. Control strategies
Developing control
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
3. Extruder at QUB
Melt pressureTransducer
Slit Die
3 Melt pressureTransducers to measure pressure drop
Melt Temperature measured by Infrared Sensor
Power consumption by HIOKI 3169-20
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
4. Implementation of Fuzzy control
Temperature
RS-422 communicationPressure transducers Screw speed
Infrared sensor
Ethernet cable
National instrumentCompact FieldPoint cFP-1808
cFP-AO 210cFP-SG 140cFP-TC 120 cFO-AI 10
Power meter
4. Implementation of Fuzzy control
Data acquisition system
4. Implementation of Fuzzy control
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
4. Implementation of Fuzzy control
The process of fuzzy logic control
‘Fuzzy system designer’ is included in the “PID and Fuzzy Logic Toolkit” in Labview 2011
4. Implementation of Fuzzy control
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)
Closed-loop melt pressure control
Pressure variations are within ±0.03MPa
4. Implementation of Fuzzy control
Closed-loop melt temperature control
Temperarue variations are within ±0.5°C
4. Implementation of Fuzzy control
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
5. Future work & Summary
Optimizing energy usage
• Feed zone cooling temperature optimization
• Barrel temperature settings optimization
• Throughput rate optimization
• Machine start-up time reduction
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.
If you have more questions, please don’t hesitate to email the author at: [email protected]
