1. low cost process monitoring for polymer extrusion 1 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

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

3. 1. Background Melt pressure Melt temperature Feed rate Barrel temperature Screw speed Viscosity 3

4. 4 Killion KTS-100 laboratory single-screw extruder Geometrical screw parameters Extruder Specifications 2. Thermal energy monitoring - the extruder 1. Background

5. 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 5 2. Thermo energy monitoring

6. 6 L1L2 N L3 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

7. 2. Thermal energy monitoring - the controller 7 2. Thermo energy monitoring

8. 8 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

9. 9 More close to the actual power consumption 2. Thermo energy monitoring

10. 10 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

11. 11 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

12. 12 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

13. 3. Motor power consumption monitoring - the controller 13 L1 N

14. 14 3. Motor power consumption monitoring - the controller Power in Power out

15. 15 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

16. 16 Motor Apparent power consumption Power factor Active power Screw speed Voltage current Screw speed 3. Motor power consumption monitoring - Apparent power consumption

17. 17 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

18. 18 4. Viscosity monitoring Viscosity measurement On-line rheometer In-line rheometer Off-line rheometer

19. 2. Viscosity monitoring 3/09/2012Queen's University Belfast 19 Viscosity calculation 4. Viscosity monitoring

20. 2. Viscosity monitoring 3/09/2012Queen's University Belfast 20 Viscosity calculation By substituting typical values 4. Viscosity monitoring

21. 21 4. Viscosity monitoring

22. Table 1: The comparison of forward and backward selection AdvantageDisadvantage ForwardFast/less computingConstrained minimization BackwardSlow/much computingUnconstrained minimization Forward selection method (constrained minimisation) y X1 X1 θ1 e = y – X1 θ1 y X1 = y – X1 θ1-X2 θ2 X2 X2 θ2 e θ 1 4. Viscosity monitoring

23. 12kn 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

24. 24 4. Viscosity monitoring Consider a general nonlinear model Write in a matrix form

25. 25 4. Viscosity monitoring A optimal design criterion whereis known as the design matrix The new cost function becomes

26. 26 4. Viscosity monitoring define Some properties of R

27. 27 4. Viscosity monitoring Also define some auxiliary matrices

28. 28 4. Viscosity monitoring

29. 29 4. Viscosity monitoring Recursive updating Net contribution of a new term to the cost function

30. 30 4. Viscosity monitoring Employing Branch and Bound

31. 31 4. Viscosity monitoring The net contribution of a new term to the cost function where

32. 32 4. Viscosity monitoring

33. 5. Summary and future work 33 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.

34. Questions ? 34 Jing DENG EPIC Research Cluster j.deng@qub.ac.uk