Invited talk, `Low-cost process monitoring for polymer extrusion', China-UK Bilateral symposium on life system modelling and simulation, 12-14 August,2013, University of Essex, Colchester, UK.

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& intelligent control
low cost process monitoring for polymer
extrusion
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Dr Jing Deng
Energy, Power and Intelligent Control
School of Electronics, Electrical Engineering and Computer Science
Queen's University Belfast
13/08/2013
j.deng@qub.ac.uk

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Content
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1. Background .
2. Thermal energy consumption monitoring.
3. Motor power consumption monitoring.
4. Viscosity monitoring through ‘soft-sensoring’.
5. Summary and future work.

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1. Background
Melt pressure
Melt temperature
Feed rate
Barrel temperature
Screw speed
Viscosity
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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
2. Thermal energy monitoring
- the extruder
1. Background

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2. Thermal energy monitoring
- the heating and cooling
Zone 1, Heating band
1.296kw
Zone 2, Heating band
1.267kw
Zone 3, Heating band
1.238kw
Clamp ring heating band
0.4964kw
Adapter heating band
0.106kw
Controller circuit
0.0016kw
Other circuits
0.06kw
Cooling fan
0.04637kw
Heating and cooling elements of the single screw extruder
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2. Thermo energy monitoring

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L1 L2 NL3
L1:
• Controller circuits
• Zone 3 heating and cooling
• Motor drive power supply
L2:
• Zone 1 heating and cooling
• Zone 4 heating
L3:
• Zone 2 heating and cooling
• Zone 5 heating
2. Thermal energy monitoring
- power supply
2. Thermo energy monitoring

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2. Thermal energy monitoring
- the controller
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2. Thermo energy monitoring

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PID
Controller
Heating band
Cooling Fan Extruder
Barrel Zone
Temperature
Set
Temperature
AFM215-303
DURAKOOL Mercury
displacement contactor
Time-proportional control
2. Thermal energy monitoring
- the controller
2. Thermo energy monitoring

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More close to
the actual
power
consumption
2. Thermo energy monitoring

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Advantage:
• Additional power consumption measurement
• More accurate thermal energy monitoring
• Expensive power meter is not required
Separate
power
supply
2. Thermal energy monitoring
- the advantages
2. Thermo energy monitoring

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Plot of energy consumption by different zones, screw speed at 10, cooling temperature at 25 degree
Temperature settings 170-180-190, material: LDPE 2102TN32W, MFR:2.5g/10min at 190 °C and 2.16 kg
2. Thermal energy monitoring
- monitor separate heating zones
2. Thermo energy monitoring

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Extruder Killion KTS-100
Material SABIC LDPE 2100TN00W
Cooling temperature setting: 25
Temperature setting: 170-180-190
Screw speed: 40 rpm
Data file: 20120720C
2. Thermal energy monitoring
- monitor separate heating zones
2. Thermo energy monitoring

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3. Motor power consumption monitoring
- the controller
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L1 N

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3. Motor power consumption monitoring
- the controller
Power in
Power out

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Those rising edges contain high-frequency energy
from harmonics of the PWM signal's frequency.
Because a motor presents an inductive load to the
inverter circuits, its inductance filters much of the
high-frequency energy. The high frequencies do little
to rotate the motor, but the energy in those
frequencies must go somewhere, and the high-
frequency energy dissipates as heat.
Measure PWM motor efficiency
3. Motor power consumption monitoring
- the controller

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Motor Apparent
power
consumption
Power factor
Active power
Screw speed
Voltage
current
current
Screw speed
3. Motor power consumption monitoring
- Apparent power consumption

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V_a = R_a * I + K_v * w
R_a = 12.4222;
K_v = 0.0038
V_a = 12.4222 * I + 0.0038 * N
3. Motor power consumption monitoring
- the controller

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4. Viscosity monitoring
Viscosity measurement
On-line rheometer In-line rheometer Off-line rheometer

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2. Viscosity monitoring
3/09/2012 Queen's University Belfast
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Viscosity calculation
𝜏 =
𝐻
2
∆𝑃
𝐿
𝛾 =
2𝑛 + 1
3𝑛
6𝑄
𝑊𝐻2
4. Viscosity monitoring

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2. Viscosity monitoring
3/09/2012 Queen's University Belfast
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Viscosity calculation
By substituting typical values
4. Viscosity monitoring

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4. Viscosity monitoring

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Table 1: The comparison of forward and backward selection
Advantage Disadvantage
Forward Fast/less computing Constrained minimization
Backward Slow/much computing Unconstrained minimization
• Forward selection method (constrained minimisation)
y
X1X1 θ1
e = y – X1 θ1
y
X1X1
= y – X1 θ1-X2 θ2
X2
X2 θ2
e
θ 1
4. Viscosity monitoring

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1 2 k n
j
Selected terms
Stage 1: Forward model selection
Stage 2: Backward model refinement
- Loop 1 ……..
- Loop 2 ……..
- Loop 3 ……..
………
Candidate terms pool
 Two-stage selection
• Remains efficient and effective from FRA
• Eliminates optimization constraint in FRA
• Reduces the training error without increasing model size
4. Viscosity monitoring

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4. Viscosity monitoring
Consider a general nonlinear model
Write in a matrix form

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4. Viscosity monitoring
A optimal design criterion
where is known as the design matrix
The new cost function becomes

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4. Viscosity monitoring
define
Some properties of R

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4. Viscosity monitoring
Also define some auxiliary matrices

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4. Viscosity monitoring

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4. Viscosity monitoring
Recursive updating
Net contribution of a new term to the cost function

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4. Viscosity monitoring
Employing Branch and Bound

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4. Viscosity monitoring
The net contribution of a new term to the cost function
where

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4. Viscosity monitoring

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5. Summary and future work
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• Low cost process monitoring techniques have been
developed for polymer extrusion, including thermo energy
monitoring, motor power consumption monitoring, and
viscosity monitoring.
• A-optimal design criterion and branch and bound can be
employed into subset selection algorithm to further
improve model compactness and computational effort.
• Current and future work mainly focus on
commercialisation of research outputs through an PoC
project.

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Questions ?
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Jing DENG
EPIC Research Cluster
j.deng@qub.ac.uk