Hvordan tænker vi uddannelse i industriel IT?

Transcription

1 Hvordan tænker vi uddannelse i industriel IT? John Bagterp Jørgensen Technical University of Denmark Dansk Automationsselskab (Dau) Hvordan bygger vi IT ind i automationsuddannelserne October 25, 2017, Odense, Denmark

2 Cyber-Physical Systems (CPS) BIG DATA MACHINE LEARNING MODEL BASED CONTROL

3 Components of CPS Computation Information 3

4 Industry 4.0

5 Internet of Things

6 Computer Controlled Systems Computer Communication Network Sampler & A-D D-A & Hold Process 6

7 Communication: Read & Write Display Application Trend Application MPC Application Software Driver Software Driver Software Driver Software Driver 7

8 Display Application Read & Write using OPC (OPC UA) Trend Application MPC Application OPC Client OPC Client OPC Client OPC Server OPC Server OPC Server OPC Server Software Software Software Software Driver Driver Driver Driver 8

9 Connection of MPC App to Plant Target Calculation b x s, u s u 0 Regulator MPC x, b k k k=0 Estimator y 0 O P C C L I E N T O P C S E R V E R S DCS System PLC Software drivers for measurement devices Other data sources Plant Sensors Lab Analysis Industrial IT is accessing, monitoring and controlling physical plant hardware 9

10 Information Technology Infrastructure SAP / R3 ERP SCADA OPC Client OPC Client OPC Client OPC Server Application MPC 1 OPC Client OPC Server Application MPC 2 OPC Client OPC Server DCS System OPC Server PLC OPC Server Other Data Source 10

11 MPC Basic Idea Estimation and regulation problem Moving horizon implementation 11

12 Moving Horizon Control Past Predicted Future Setpoint Predicted Output Input Time

13 Moving Horizon Control Past Predicted Future Setpoint Predicted Output Input Time

14 Moving Horizon Control Past Predicted Future Setpoint Predicted Output Input Time

15 Moving Horizon Control Past Predicted Future Setpoint Predicted Output Input Time

16 Moving Horizon Control Past Predicted Future Setpoint Predicted Output Input Time

17 Moving Horizon Control Past Predicted Future Setpoint Predicted Output Input Time

18 Moving Horizon Control Past Predicted Future Setpoint Predicted Output Input Time

19 Role of MPC in the Operational Hierarchy Plant-Wide Optimization Global steady state optimization (every day) Unit 1 Local Optimizer High / Low Select Logic Unit 2 Local Optimizer Local steady state optimization (every hour) Make fine adjustments for operating conditions of local units PID SUM Lead Lag PID SUM Model Predictive Control (MPC) Dynamic constraint control (every minute) Take each local unit to the optimal condition. Reject Disturbances. Unit 1 DCS SISO PID Controls Unit 2 DCS SISO PID Controls FC PC TC LC FC PC TC LC Basic dynamic control (every second) 19

20 Structure of Optimizer & MPC Setpoints MVs 20

21 MPC Structure 21

22 MPC Structure 22

23 MPC Structure 23

24 MPC = PI + Decoupler 24

25 Technical Advantages of MPC Explicit process models allow control of difficult dynamics Dead-time (time delay) Inverse response Interactions (multivariate) Nonlinearity Optimization of future plant behavior handles Feedforward from measured or estimated disturbances Feedforward from setpoint changes and desired future trajectory Feedback Input and output constraints are handled by the controller Infrequent and irregular laboratory measurements 25

26 Optimal Operation is Close to Limits P2 Optimum operation point Limits Optimum close to constraints requires process optimization and advanced process control P1

27 Economic Benefit of Process Control Limit Safety Margin Target No APC APC reduces variation Reduced variation allows operation closer to the limit

28 Economic Benefit of Process Control

29 Economic Benefit of Process Control Economic value added by feedback control Squeze & Shift 29

30 Rapid Product Change Controlled Variable Manipulated Variable Time 30

31 Industrial Implementation 31

32 Physical setup Scada System GPDK Costumer Ethernet Network VPN OPC PLC DRYCONTROL Control room Process area

33 Physical setup 33

34 Performance Residual Moisture %: DRYCONTROL on DRYCONTROL off Exhaust Air Temperature: Disturbances: , DRYCONTROL GEA Process Engineering

35 Step Response Experiments 5 Measured Output Manipulated Input

36 Step Response Experiments 5 Measured Output K( s 1) s Gs () e ( T s 1)( T s 1) Manipulated Input

37 Identification of the Deterministic and Stochastic Model 37

38 Software Architecture of APC APControl Model Identification Model APCView DCS / SCADA GUI Commands Status APC Datalogs APCBox Setpoints Data DCS / SCADA

39 APCBox MPC Algorithm Optimization Algorithm APCView Configuration APC Modules Industrial PC Operating Systems Windows Linux Communication OPC TCP/IP UDP

40 Cement Mill Control & Optimization

41 Central Control Building

42 Central Control Room

43 Opstation

44 An Artificial Pancreas A Closed Loop System

45 Components of a Closed Loop System Actuators - Insulin Pump - Insulin Pen Sensors - Continuous glucose sensor - Finger stick measurements (for calibration) Algorithms - Control Algorithm - Monitoring and Fault Detection Algorithms - Safety Algorithms

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47 Clinical Closed Loop Studies 47

48 Clinical Closed Loop OPEN-LOOP CLOSED-LOOP 48

49 Glucose - Mean and Variability Meal Open and Closed Loop 49

50 Challenges OPEN-LOOP CLOSED-LOOP 50

51 An Offshore Oil Reservoir 51

52 Mathematical Model 52

53 Closed-Loop Reservoir Management 53

54 Smart Energy Systems 54

55 Thank You Q & A John Bagterp Jørgensen Technical University of Denmark jbjo@dtu.dk