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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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 3
Application example 1Improve DIP quality and lower cost
Application example 1Improve DIP quality and lower cost
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 4
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 7
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 8
Application example 2 Improve paper quality and lower cost
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11
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)
10example for CM, fluting, TL
Production cost
Fiber cost Energy cost Starch cost
Without a Virtual Sensor, strength values areonly available at the end of each tambour
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 11
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 12
CMT – Virtual Sensor
CMT – Lab measurement
Virtual SensorReal time measurement without a “physical sensor”
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 14
Virtual
Sensor
Actuators
Quality parameters
Virtual SensorReal-time visualization of quality parameters
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11
Slice lip opening
Fiber cost
Energy cost
StarchJet/Wire-Ratio
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11
Basis weight
Fiber cost
Energy cost
SCT
CMT
Tensile
StarchJet/Wire-Ratio
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
17example for CM, fluting, TL
APC
virtual sensors, model predictive control (MPC), cost controller
With APCFiber savings while maintaining target quality
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 18
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 19
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 20
Outlook 1Quickly build Virtual Sensors in the Cloud
Building a virtual sensor online candecrease time to build a new VS from weeks to hours
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 21
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 22
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 24
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 25
Outlook 2Reduce fiber losses with real time balancing
Lack of informationFiber losses can only be determined once a month
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 26
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
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 27
weekly/monthly
Recycled Fiber Final PaperWithout OMB
real time
With OMB
Real-time visualization of lossesAllows quick issue identification and corrective action
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 28
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 29
Outlook 3Break Prevention with help of AI
Break ProtectorMachine learning meets paper technology
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 30
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 Technologies | Maria Knauer | 2020-03-11 32
Driving Efficiency with Intelligent TechnologiesSummary
Driving Efficiency with Intelligent Technologies | Maria Knauer | 2020-03-11 33
• 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