Hvordan tænker vi uddannelse i industriel...

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

Cyber-Physical Systems (CPS)

BIG DATA – MACHINE LEARNING – MODEL BASED CONTROL

Components of CPS

3

Information

Computation

Industry 4.0

Internet of Things

Computer Controlled Systems

Process

Sampler&

A-D

Computer

D-A

&

Hold

Communication Network

6

Software

Driver

Software

Driver

Software

Driver

Software

Driver

Display

Application

Trend

Application

MPC

Application

Communication: Read & Write

7

Software

Driver

Software

Driver

Software

Driver

Software

Driver

OPC Server OPC Server OPC Server OPC Server

Display

Application

Trend

Application

MPC

Application

OPC Client OPC Client OPC Client

Read & Write using OPC

(OPC UA)

8

Connection of MPC App to Plant

Industrial IT is accessing, monitoring and

controlling physical plant hardware

Target

CalculationRegulator

Estimator

b

xs, u

su

0

MPC

y0

x , bk k k=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

9

Information Technology Infrastructure

Application

MPC 1

OPC Client

Application

MPC 2

OPC Client

OPC Server

DCS

System

OPC Server

PLC

OPC Server

Other Data

Source

OPC Server OPC Server

SAP / R3

OPC Client

ERP

OPC Client

SCADA

OPC Client

10

MPC – Basic Idea

Estimation and

regulation problem

Moving horizon

implementation

11

Moving Horizon Control

Past Predicted Future

Setpoint

Predicted

Output

Input

Time

Role of MPC in the Operational Hierarchy

Plant-Wide Optimization

Unit 1

Local Optimizer

Unit 2

Local Optimizer

Model

Predictive

Control

(MPC)

High / Low

Select Logic

PIDLead

LagPID

SUM SUM

Unit 1 DCS

SISO PID Controls

Unit 2 DCS

SISO PID Controls

FC PC LCTC FC PC LCTC

Basic dynamic control

(every second)

Dynamic constraint control

(every minute)

Local steady state optimization

(every hour)

Global steady state optimization

(every day)

Make fine adjustments for

operating conditions of local units

Take each local unit to the

optimal condition.

Reject Disturbances.

19

Structure of

Optimizer & MPCSetpoints MVs

20

MPC Structure

21

MPC Structure

22

MPC Structure

23

MPC = PI + Decoupler

24

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

Optimal Operation is Close to Limits

Optimum operation point

Limits

P1

P2

Optimum close to constraints requires process optimization and advanced process control

0 0.5 1 1.5 2 2.5 32

4

6

8

10

12

14

16

18

Economic Benefit of Process Control

No APC

APC reduces variation

Reduced variation allows operation closer to the limit

Limit

Target

Safety Margin

27


0 2 4 6 8 10 12 14 16 18 200

500

1000

1500

2000

2500

0 2 4 6 8 10 12 14 16 18 200

2000

4000

6000

8000

10000

0 2 4 6 8 10 12 14 16 18 200

2000

4000

6000

8000

10000

28


0 2 4 6 8 10 12 14 16 18 200

500

1000

1500

2000

2500

0 2 4 6 8 10 12 14 16 18 200

2000

4000

6000

8000

10000

0 2 4 6 8 10 12 14 16 18 200

2000

4000

6000

8000

10000

Economic value

added by

feedback control

Squeze

& Shift

29

0 20 40 60 80 100 120 140 160 180 200-0.5

0

0.5

1

1.5

Co

ntr

olle

d

Va

ria

ble

0 20 40 60 80 100 120 140 160 180 200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Time

Ma

nip

ula

ted

V

aria

ble

Rapid Product Change

30

31

Industrial Implementation

Physical setup

PLC

Ethernet Network

Scada System

DRYCONTROL

Control room

Process area

OPC

GPDK

VPN

Costumer

33

Physical setup

GEA Process Engineering

Performance

22-10-2013, DRYCONTROL™

DRYCONTROL™ on

DRYCONTROL™ off

Residual Moisture %:

Exhaust Air Temperature:

Disturbances:

34

Step Response Experiments

0 20 40 60 80 100 120 140 160 180 2001

2

3

4

5

Me

asu

red

Ou

tpu

t

0 20 40 60 80 100 120 140 160 180 2001

1.5

2

Ma

nip

ula

ted

In

pu

t

0 20 40 60 80 100 120 140 160 180 2001

2

3

4

5

Me

asu

red

Ou

tpu

t

0 20 40 60 80 100 120 140 160 180 2001

1.5

2

Ma

nip

ula

ted

In

pu

t

1 2

( 1)

( 1)( 1)( ) sK s

eT s T s

G s

Step Response Experiments

37Identification of the

Deterministic and Stochastic Model

Software Architecture of APC

Model Identification

APCView

APCDatalogs

APCBox DCS / SCADA

DCS / SCADAGUIModel

Commands Status

Setpoints

Data

APControl

APC Modules• APCBox

– MPC Algorithm

– Optimization Algorithm

• APCView

– Configuration

• Industrial PC Operating Systems

– Windows

– Linux

• Communication

– OPC

– TCP/IP

– UDP

Cement Mill

Control & Optimization

Central Control Building

Central Control Room

Opstation

An Artificial Pancreas – A Closed Loop System

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

Clinical Closed Loop Studies

47

Clinical Closed Loop

48

OPEN-LOOP CLOSED-LOOP

Glucose - Mean and VariabilityOpen and Closed Loop

49

Meal

Challenges

50

OPEN-LOOP CLOSED-LOOP

An Offshore Oil Reservoir

51

Mathematical Model

52

Closed-Loop Reservoir

Management

53

Smart Energy Systems

54

John Bagterp Jørgensen

Technical University of Denmark

E-mail: [email protected]

Thank You – Q & A

Hvordan tænker vi uddannelse i industriel...

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