low cost process monitoring for polymer extrusion
DESCRIPTION
low cost process monitoring for polymer extrusion. 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. Content. Background . Thermal energy consumption monitoring. - PowerPoint PPT PresentationTRANSCRIPT
energy, power & intelligent control
low cost process monitoring for polymer extrusion
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Dr Jing DengEnergy, Power and Intelligent Control
School of Electronics, Electrical Engineering and Computer ScienceQueen's University Belfast
13/08/[email protected]
energy, power & intelligent control
Content2
1. Background .
2. Thermal energy consumption monitoring.
3. Motor power consumption monitoring.
4. Viscosity monitoring through ‘soft-sensoring’.
5. Summary and future work.
energy, power & intelligent control
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) 2.24Screw diameter (mm) 25No. of barrel temperature zones 3Additional temperature zones connected
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Operating speed range (rpm) 0-115
Extruder Specifications
2. Thermal energy monitoring - the extruder1. Background
energy, power & intelligent control
2. Thermal energy monitoring - the heating and cooling
Zone 1, Heating band1.296kw
Zone 2, Heating band1.267kw
Zone 3, Heating band1.238kw
Clamp ring heating band0.4964kw
Adapter heating band0.106kw
Controller circuit0.0016kw
Other circuits0.06kw
Cooling fan0.04637kw
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
energy, power & intelligent control
2. Thermal energy monitoring - the controller
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2. Thermo energy monitoring
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PIDController
Heating band
Cooling Fan ExtruderBarrel Zone
Temperature
SetTemperature
AFM215-303DURAKOOL 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
energy, power & intelligent control
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Extruder Killion KTS-100Material SABIC LDPE 2100TN00WCooling temperature setting: 25Temperature setting: 170-180-190Screw speed: 40 rpmData file: 20120720C
2. Thermal energy monitoring - monitor separate heating zones
2. Thermo energy monitoring
energy, power & intelligent control
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
energy, power & intelligent control
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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
energy, power & intelligent control
2. Viscosity monitoring
3/09/2012 Queen's University Belfast
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Viscosity calculation
𝜏=𝐻2 (∆ 𝑃𝐿 ) �̇�=
2𝑛+13𝑛 ( 6𝑄
𝑊 𝐻 2 )
4. Viscosity monitoring
energy, power & intelligent control
2. Viscosity monitoring
3/09/2012 Queen's University Belfast
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Viscosity calculation
By substituting typical values
4. Viscosity monitoring
energy, power & intelligent control
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4. Viscosity monitoring
energy, power & intelligent control
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
energy, power & intelligent control
1 2 k n
j
Selected termsStage 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
• 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.
