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Jing - UK-India Bi-lateral Workshop on Sustainable Energy and Smart Grid
- 1. energy, power & intelligent control Saving energy through intelligent modelling, control, and optimization 1 Dr Jing Deng http://jing-deng.com Energy, Power and Intelligent Control School of Electronics, Electrical Engineering and Computer Science Queen's University Belfast 27/03/2014 j.deng@qub.ac.uk
- 2. energy, power & intelligent control Content 2 1. Background. 2. EPSRC/RCUK (EP/G042594/1), “UK-China Science Bridge in Sustainable Energy and Built Environment (UC-SEBE)”. 3. 2010-2013, EPSRC (EP/G059489/1), “Thermal Management in Polymer Processing”. 4. EPSRC (EP/L001063/1), “Intelligent Grid Interfaced Vehicle Eco-charging (iGIVE)” (http://i-give.org.uk).
- 3. energy, power & intelligent control 3 Ultra Power Saving Mode powered by PowerXtend WebXtend: boots extra 25% NavXtend: boots extra 25% GameXtend: boots extra 50% 10% Battery last for 24 hours Background
- 4. energy, power & intelligent control 4 • DU Battery Saver • Easy Battery Saver • Battery Widget Reborn • JuiceDefender • Battery Doctor Background
- 5. energy, power & intelligent control 5Background
- 6. energy, power & intelligent control 6 eQ-3 MAX! Wireless Radiator Thermostat (~£25) eQ-3 MAX! Wireless Window Sensor (~£20) eQ-3 MAX! Eco Switch (~£20) eQ-3 MAX! Cube LAN Gateway (~£45) Background http://www.eq-3.de
- 7. energy, power & intelligent control 7Background Renewable energy resources EV Battery Economic dispatch Openpmu.org Fault detection and diagnosis Model-based Model-based Intelligent Energy and health monitoring Modelling, control, and optimization
- 8. energy, power & intelligent control 1. UK-China Science Bridge 8 Intelligent system & control Power system Civil engineering Queen’s Science Bridge: pursue energy and infrastructure issues in a sustainable manner, via enhanced uptake of technology, knowledge and expertise and strengthened collaborations
- 9. energy, power & intelligent control 1. UK-China Science Bridge 9 0 5 10 15 20 25 30 35 Full time PhD Exchanged students Undergraduate Research fellows Trained 30 PhD students, 20 exchange students, 11 undergraduate students, and 4 research fellows. Engineering leaderships
- 10. energy, power & intelligent control 1. UK-China Science Bridge 10 0 5 10 15 20 25 30 35 Invited lectures Research training Honours 30 invited lectures and seminars, 10 research training activities, 14 visiting professorships Academic engagements
- 11. energy, power & intelligent control 1. UK-China Science Bridge 11 0 20 40 60 80 100 120 140 160 180 partent journal conference 163 conference papers; 44 journal papers Joint publications
- 12. energy, power & intelligent control 1. UK-China Science Bridge 12 1. Joint Laboratory on Energy and Automation with Baosteel, SAIC, SHU Joint project won Science and Technology Progress Award by Shanghai Municipal Government Creation prize from Shanghai International Industrial Fair 2010 first prize of Chinese Machinery Industry Science and Technology
- 13. energy, power & intelligent control 1. UK-China Science Bridge 13 2. Joint Laboratory on Autonomous Service Robots Autonomous wheelchair project in Shanghai EXPO 2010
- 14. energy, power & intelligent control 1. UK-China Science Bridge 14 1). 2010 International Conference on Life System Modeling and Simulation& 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment 3). UK-China Science Bridge Forum & 2nd International Conference on Intelligent Computing for Sustainable Energy and Environment 2). UK-China workshop and PhD student summer school on smart grids 3. Jointly organized international conferences/workshops
- 15. energy, power & intelligent control 1. UK-China Science Bridge 15 Knowledge Transfers Intelligent optimized algorithms deployed in Shanghai Waigaoqiao & Caohejing power stations Wireless data acquisition deployed in Nantong sewage treatment company Hybrid network measurement and monitoring system used in Baosteel company
- 16. energy, power & intelligent control 1. UK-China Science Bridge 16 In situ testing of concrete in the ‘Bird’s Nest Stadium’ Beijing QUB structural health monitoring station at Hangzhou Bay Bridge QUB energy efficiency monitoring system Knowledge Transfers
- 17. energy, power & intelligent control 1. UK-China Science Bridge 17 Knowledge Transfers Crown cap surface detection systems for a Chinese company, detecting 280,000 caps/hour. Technologies have been used in developing new modules for SUPMAX Distributed Control System series for energy and environment applications by SAIC, deployed in many countries and regions.
- 18. energy, power & intelligent control 2. Thermal Management in Polymer Processing 18 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.
- 19. energy, power & intelligent control 19 2. Thermal Management in Polymer Processing 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
- 20. energy, power & intelligent control Melt pressure Melt temperature Feed rate Barrel temperature Screw speed Viscosity 20 2. Thermal Management in Polymer Processing
- 21. energy, power & intelligent control21 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 2. Thermal Management in Polymer Processing
- 22. energy, power & intelligent control 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 22 2. Thermal Management in Polymer Processing
- 23. energy, power & intelligent control 23 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. Thermal Management in Polymer Processing
- 24. energy, power & intelligent control 24 2. Thermal Management in Polymer Processing
- 25. energy, power & intelligent control 25 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. Thermal Management in Polymer Processing
- 26. energy, power & intelligent control 26 More close to the actual power consumption 2. Thermal Management in Polymer Processing
- 27. energy, power & intelligent control 27 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. Thermal Management in Polymer Processing
- 28. energy, power & intelligent control 28 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. Thermal Management in Polymer Processing
- 29. energy, power & intelligent control 29 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. Thermal Management in Polymer Processing
- 30. energy, power & intelligent control 30 L1 N 2. Thermal Management in Polymer Processing
- 31. energy, power & intelligent control 31 Power in Power out 2. Thermal Management in Polymer Processing
- 32. energy, power & intelligent control 32 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 2. Thermal Management in Polymer Processing
- 33. energy, power & intelligent control 33 Motor Apparent power consumption Power factor Active power Screw speed Voltage current current Screw speed 2. Thermal Management in Polymer Processing
- 34. energy, power & intelligent control 34 V_a = R_a * I + K_v * w R_a = 12.4222; K_v = 0.0038 V_a = 12.4222 * I + 0.0038 * N 2. Thermal Management in Polymer Processing
- 35. energy, power & intelligent control 35 Viscosity measurement On-line rheometer In-line rheometer Off-line rheometer 2. Thermal Management in Polymer Processing
- 36. energy, power & intelligent control 2. Viscosity monitoring 3/09/2012 Queen's University Belfast 36 Viscosity calculation 2. Thermal Management in Polymer Processing
- 37. energy, power & intelligent control 2. Viscosity monitoring 3/09/2012 Queen's University Belfast 37 Viscosity calculation By substituting typical values 2. Thermal Management in Polymer Processing
- 38. energy, power & intelligent control 38 2. Thermal Management in Polymer Processing
- 39. 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 2. Thermal Management in Polymer Processing
- 40. energy, power & intelligent control 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 2. Thermal Management in Polymer Processing
- 41. energy, power & intelligent control 41 Consider a general nonlinear model Write in a matrix form 2. Thermal Management in Polymer Processing
- 42. energy, power & intelligent control 42 A optimal design criterion where is known as the design matrix The new cost function becomes 2. Thermal Management in Polymer Processing
- 43. energy, power & intelligent control 43 define Some properties of R 2. Thermal Management in Polymer Processing
- 44. energy, power & intelligent control 44 Also define some auxiliary matrices 2. Thermal Management in Polymer Processing
- 45. energy, power & intelligent control 45 2. Thermal Management in Polymer Processing
- 46. energy, power & intelligent control 46 Recursive updating Net contribution of a new term to the cost function 2. Thermal Management in Polymer Processing
- 47. energy, power & intelligent control 47 Employing Branch and Bound 2. Thermal Management in Polymer Processing
- 48. energy, power & intelligent control 48 The net contribution of a new term to the cost function where 2. Thermal Management in Polymer Processing
- 49. energy, power & intelligent control 49 2. Thermal Management in Polymer Processing
- 50. energy, power & intelligent control 50 2. Thermal Management in Polymer Processing
- 51. energy, power & intelligent control 51 3. Intelligent Grid Interfaced Vehicle Eco-charging (iGIVE)
- 52. energy, power & intelligent control 52 WP1) Framework of energy and information flows WP2) Power flow control-based battery model WP3) Bi-directional traction drive charging system WP4) Model for environment friendly EV charging WP5) Optimal dispatching strategy for EV WP6) Holistic system model for the impact of EV actors 3. Intelligent Grid Interfaced Vehicle Eco-charging (iGIVE)
- 53. energy, power & intelligent control Future collaboration • Uk and china agree £20 million low carbon innovation programme (announced on 06 March 2014). • Energy Resilient Manufacturing (EPSRC, 04/04/2014) 53
- 54. energy, power & intelligent control Questions ? 54 Jing DENG EPIC Research Cluster j.deng@qub.ac.uk
Editor's Notes
- For those who are not familiar with the polymer extrusion process, from this picture, the polymer pellets are fed through this hopper, and then pushed by the screw from the feed zone to the die to form different shapes. The polymer is melted mainly by the shear stress from the screw and partly from the barrel heating. So in this picture, the feed rate, screw speed and barrel temperature settings can be regarded as system inputs, and the melt pressure, temperature and viscosity are system outputs. It is clear that the whole system is open loop system, in order to get proper melt properties, technicians have to speed long time to properly adjust these inputs, energy and material are then wasted during this procedure.
- The energy consumption methods were developed for this single screw extruder, and some of its specifications are shown in the tables on the right hand side.
- The heating and cooling power of each elements was first investigated, this is done through several trials where different configurations of heating and cooling were operated, and the power consumption was monitored by a HIOKI 3169 power meter. As you can see here, the main power was consumed at these three heating zones.
- This extruder was supplied with three phase power, and each phase provides electricity power to different components. So from this slide, you can see that the motor power was supplied by phase 1, while the other two phases are mainly used for heating power supply.
- The heating power of each zone is regulated through this eurotherm 808 controller
- And the traditional pid algorithm is used. As the heating band is connected to a displacement contactor, so the Pulse width modulation is utilized to incorporate the controller outputs. In the bottom left figure, the positive signal means heating, while the negative signal indicate fan cooling. By multiplying this control signal with the associated heating or cooling power, we can then easily obtain the power consumption at each zone of this extruder.
- In order to verify the power consumption obtained through the controller, a HIOKI 3169 power meter is also connected. And in the right picture, the green line is the power consumption through the new methods, while the blue line is the measurements through the power meter. You can see that at the first few minutes, these two values are exactly the same, but there is some difference after around 5 minutes. This is because at the first few minutes, the heating was on for most of the time, while after this warm up period, the heating switches between on and off frequently to maintain the set temperature. The maximum sample rate of the power meter is 1Hz and this is not enough to capture all changes. That’s why the measured value is lower than the actual value.
- Additionally, the developed method is also able to monitor additional heating elements which is difficult for a single power meter.
- The ability to monitor power consumption at each heating zone provide the opportunity to investigate the power distribution along the extruder, so here you can see that zone 1 (blue line) which is near the water cooling and material feeding consumption more than half of the total thermal energy.
- Another representation of the thermal energy consumption at different heating zone.
- A DC motor is installed on this extruder to drive the screw. Its controller is eurotherm 512C which also use PID algorithm to regulate the screw speed.
- The controller output is also converted to PWM signal. In this picture, the power in terminal is connect to phase 1 supply, while the power out terminal is connected to the DC motor.
- Due to the PWM regulation, the power factor of this drive system is quite low, and it is usually lower than 0.5.
- In order to monitor the motor apparent power consumption, it active power and power factor need to be known. Fortunately, the motor power factor is found to have a linear relationship with the screw speed, and its active power can be calculated through the armature voltage and armature current. The current can be directly read through one of the controller terminals, and the voltage is found to be related with screw speed and armature current.
- So here shows you the model of armature voltage which is derived through the first principal model of DC motor, and only two parameters need to be identified.
- The forward selection is fast and needs less computing, but there exist a constraint in model optimization process, which will be shown later. By contrast, backward elimination doesn’t involve any constraint, but it needs more computation, thus it is much slower. Now, I’d like to show you why the forward selection involves a constrained optimization. Suppose y is the target vector, at the first step, x1 was chosen as the best one to interpret y; at the second step, one more term need to be chosen to interpret y together with x1. As x2 is selected, the coefficient of x1 is also changed. That means, the contribution of later selected terms are calculated based on previous selected ones. Thus the forward selection is a constrain minimisation process.
- The two-stage selection in this paper has proven to be able to eliminate the constraint involved in forward selection. The fist stage is the same as FRA (Fast recursive algorithm), and the second stage is a iterative model refinement process. Now, let’s look at the right picture, supposed n terms / hidden nodes have been selection by the forward stage. At the second stage, the contribution of all the selected terms are to be reviewed, suppose the k^th one is the term of interest, it will first be moved to the n^th position as it was the last selected term. Then the contribution of candidate terms are recalculated based on the new n-1 selected terms. If any of them is more significant than the shift one, they will be interchange. And this process is continued until all the selected model terms are significant than those remaining in the candidate pool.Some advantages of two-stage selection includes Remains efficient and effective from FRAEliminates optimization constraint in FRAReduces the training error without increasing model size