Driving Efficiency with Intelligent Technologies · Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 Ash Brightness (Dirt spec) Tasks of operator • Keep

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Driving Efficiency withIntelligent TechnologiesMaria Knauer | 2020-03-11 | public

Three simple steps to drive efficiencyVisualize – Stabilize – Optimize

Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 2

Pro

ce

ss

flu

ctu

ati

on

s

Fluctuation Corridor

Stabilized Corridor

New Target Corridor

Time

Raw materials

People

Variance

of input

factors

Different

behavior of

individual PMs

High variance of

outputs, instable

processes

Rationally automated

stabilization

Equipment

Waste of resources

visualize stabilize optimize

lower limit of

quality target


Application example 1Improve DIP quality and lower cost

Application example 1Improve DIP quality and lower cost


Chemicals

Brightness

measurement

Overflows Overflows Chemicals

Right qualityLow cost

Ash

measurement

operator

Actu

ato

rs

Foam level ChemicalsPump speedFoam weir

Quality parameters

Without Advanced Process Control (APC)Complex interactions to be managed by operators

Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11

Ash Brightness (Dirt spec)

Tasks of operator

• Keep ash and brightness

(and ev. dirt specs) in

specification

• Reduce cost and increase

yield

Finding the optimum is a

complex task

5example for CM, fluting, TL

Production cost

Fiber cost Energy cost Chemical cost

Actuators

Quality parametersResource cost

With APCSteady DIP quality at minimum cost


Foam level

Chemicals cost

Fiber cost

Ash

Brightness

Pump speedFoam weir position

Tasks of MPC

• Keep ash and brightness in

specification

• Reduce cost by reducing

losses (pump speed + foam

level + foam weir) and

bleaching chemicalsAPC

model predictive control (MPC), cost controller

Energy cost

6

Chemicals

(Dirt spec)

With APCImproved Yield and reduced Energy consumption


90

85

80

75

70

65

60

09-27

40

35

30

25

20

15

10

En

erg

y c

on

su

mp

tio

n f

or

co

ntr

olled

flo

tati

on

pu

mp

s (

kW

h/t

)

Yie

ld b

ased

on

slu

dg

e f

low

to

dew

ate

rin

g (

%)

10-04 10-11 10-18 10-25 11-01 11-08 11-15 11-22 11-29 12-06 12-13 12-20

D 2.0%

D 3kWh/t

Yield w/o APC

Energy w/o APC

Yield with APC

Energy with APC


Application example 2 Improve paper quality and lower cost


Application example 2 Improve paper quality and lower cost

Right qualityLow cost

Basis weight Jet/Wire-Ratio Starch

9

RefiningFurnish mix

example for CM, fluting, TL

operator

Actu

ato

rs

Quality parameters

Without APCComplex interactions to be managed by operators


Basis weight

SCT CMT Tensile

StarchJet/Wire-Ratio

Tasks of operator

• Keep SCT, CMT, tensile

(and for Testliner also

burst) in specification

• Reduce cost by minimizing

starch usage & basis

weight

Burst (only TL)


Production cost

Fiber cost Energy cost Starch cost

Without a Virtual Sensor, strength values areonly available at the end of each tambour


CMT – Lab measurement

Turn-up to Turn-up = 30 min

Lab test finished

Time until CMT is available ~ 1h

With a Virtual SensorStrength values are continuously calculated & visualized


CMT – Virtual Sensor

CMT – Lab measurement

Virtual SensorReal time measurement without a “physical sensor”


Process Data

Machine Data

QCS Data

Physical Model

∑

𝑓(𝑥)

Statistical Model

Operation

Advanced Control

Visualization

Virtual Sensor

13

Lab Data

Metric Value

Train size 85 %

Train score 0.9577

RMSE Not calculated

Train correlation 97 %

Metric Value

Test size 15 %

Test score 0.9495

RMSE 0.0851

Test correlation 97 %

Virtual Sensors for strength valuesShow very high prediction quality


Virtual

Sensor

Actuators

Quality parameters

Virtual SensorReal-time visualization of quality parameters


Slice lip opening StarchJet/Wire-Ratio

Virtual sensor

• Operator decides based on

experience

• Every shift may develop

their own strategy

• Cost impact is mostly not

taken into account

15

SCT

CMT

Tensile

Burst (only TL)

operator

Actuators

Quality parametersProduction cost

Open loop controlMPC provides recommended actions to operator


Slice lip opening

Fiber cost

Energy cost


Open loop control

• APC provides suggestions

for optimum settings to

reach target quality at

lowest cost

• Operator decides if he

follows the suggestion -

experience of operators

stays final instance

Starch cost

16

APC

SCT

CMT

Tensile

Burst (only TL)

operator

Actuators

Quality parametersProduction cost

With APCEnsure the right quality at lowest cost


Basis weight

Fiber cost

Energy cost

SCT

CMT

Tensile


Closed loop control

• APC keeps SCT, CMT,

tensile (and for Testliner

also burst) in spec

• APC reduces cost by

minimizing starch usage

and basis weight

Burst (only TL)

Starch cost


APC

virtual sensors, model predictive control (MPC), cost controller

With APCFiber savings while maintaining target quality


Kraftliner (420.000 tpy)

SP Set Point

PV Process Value

Results

• 13,500 tpy fiber savings

– 8 gsm lower BW

– 3% fiber savings

Maintaining STFI also at

reduced basis weight!

Dry

weight

APC controlManual control

STFI

8 g/m²

SPmanual 267 g/m²

SPOnE 259 g/m²

SPmanual 30ft SPOnE 40ftJet/wire

ratio

With APC Fiber savings while maintaining target quality


Copy 80 (350.000 tpy) Results

• 3,200 tpy fiber savings by

– BW reduction from

80.74 to 80.34 gsm

– ash increase from

21.3 to 21.6% (another

0.24 gsm fiber

savings)

Caliper and bending stiffness

stayed in specification!

APC runningoptimization

phase


Outlook 1Quickly build Virtual Sensors in the Cloud

Building a virtual sensor online candecrease time to build a new VS from weeks to hours


Data preparation

& modelling

Machine operator

3-4 weeks

done by data scientists and technologists

tod

ay

tom

orr

ow

data scientist /

technologist

customer technologist /

production manager

Implementation & use

1 hour

done by customer

Virtual Sensor BuilderProduct Concept / Description

Define target value

Extract data

Prepare data

Reduce initial order

Clean data

Select features

(variables)

Validate with lab

data

Put live (on prem)

Optional: Validate

with trialsRe-train


Define target value

Extract data

Prepare data

Generate virtual sensor in cloudValidate with lab

data

Put live (online)

Optional: Validate

with trialsRe-train

automated during

setup of cloud connection

done offline (highly automated) by data scientist

3 – 4 weeks (done by data scientist)

1 hour (done by customer)

Data modelling ValidateData preparation Implement

tod

ay

with

VS

Bu

ilde

r

VirtualSensor BuilderSneak Preview



Outlook 2Reduce fiber losses with real time balancing

Lack of informationFiber losses can only be determined once a month


Recycled

Fiber

Final

PaperNo information

on losses until

the end of the month

Increase of losses e.g. due to

defects or bad process

conditions can only be detected

at the end of the month

S

Stock Preparation WEP

PM Top

Furnish

S

PM Back

Effluent Treatment

Sewer

SP

Online Mass BalanceReal-time losses overview


weekly/monthly

Recycled Fiber Final PaperWithout OMB

real time

With OMB

Real-time visualization of lossesAllows quick issue identification and corrective action



Outlook 3Break Prevention with help of AI

Break ProtectorMachine learning meets paper technology


Process model

• Understand interactions

Pattern recognition

• How does the process behave prior to a break?

• Display that the break risk has increased

Root cause indication

• Which signals are responsible for the increased risk?

• How much does each contribute?

Root cause analysis

• Analyze the root cause indicators with paper background

• Understand root causes and their appearance

Define counteraction

• Define corrective actions for each root cause

Trigger action

• Prevent the break!

Algorithm running in cloud Technologist uses Break Protector AppInfo to operator in

Break Protector App

Without process knowledge, breaks can only be indicated

Break Predictor Break Protector

Break ProtectorSneak Preview: Operator‘s view


Driving Efficiency with Intelligent TechnologiesSummary


• APCs which combine Virtual Sensors, Model Predictive Control and a Cost

Controller offer high cost savings potential in a wide range of applications.

• Technically these solutions are proven and state of the art, but they are still

not widely spread. Open loop control as intermediate step can ease the

transition to closed loop control.

• The rise of data storage in the cloud, new algorithms and powerful cloud

computing offer exciting new opportunities to e.g.

– quickly calculate soft sensors

– get life data about fiber losses

– predict and consequently prevent breaks

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Driving Efficiency with Intelligent Technologies · Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 Ash Brightness (Dirt spec) Tasks of operator • Keep