slides for icae 2012 conference

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: j.deng@qub.ac.uk