T87
TISSUE
Multivariable Control
and Energy Optimization
of Tissue Machines
By S. Chu, R. MacHattie and J. Backström
Abstract: The desire to increase profits by minimizing operating costs without sacrificing paper quality and
runnability is a goal all papermakers strive for. Modern tissue machines are typically equipped with more
than twenty low-level control loops and multiple sheet property measurements at various locations along the
machine. It is a large and strongly coupled process that can be difficult for control engineers to optimize without advanced multivariable control techniques. This paper examines the process interactions and energy cost
reductions using model predictive control (MPC) technology with an optimization layer that automatically
drives the process towards the lowest cost while honoring hard process and quality constraints. The studied
paper machine was equipped with a fast scanning moisture measurement before the Yankee dryer in addition
to the measurements of a traditional reel scanner.
T
raditional and through-air dried
(TAD) tissue manufacturing expends
more resources removing water than
any other function. Knowing the water
content throughout the process and the
efficiencies of the various water removal elements
used, allows an advanced control system to control
the process in the most economical manner.
The studied paper machine was equipped with
an ExPress Moisture scanner measurement [1]
before the Yankee dryer in addition to the measurements of a traditional reel scanner. With these
measurements along with advanced multivariable
control, the economic efficiencies of each drying
element and the optimization layer, it was shown
that the advanced control system distributed the
drying load such that significant economic benefits
were realized.
Several trials were run with different energy
costs. It is given that energy costs change with time,
so the cost of energy is updated in the advanced
control system periodically, which can have a big
impact on how the tissue machine is optimized. In
all trials, the more expensive manipulated variables
(MVs) were driven down to their lower operating
cost limits and the cheaper MVs were driven to
their higher operating cost limits. Significant energy
cost savings were realized without sacrificing paper
quality and machine runability.
MACHINE OVERVIEW
The paper machine studied uses two TADs and a
Yankee dryer to dewater the tissue (Fig. 1). A traditional reel scanner measuring Dry Weight and Reel
Moisture along with an ExPress Moisture scanner
30 (located after TAD2) measuring TAD Moisture
are the on-line measurements available. Before the
optimization trials, the machine direction (MD)
controls were multivariable but only Stock Flow and
TAD2 exhaust temperature were used in cascade
control. The setpoints for the rest of the manipulated variables (MVs) were fixed based on operator
experience and previous operating conditions.
A new multivariable control strategy was devised
to take advantage of the economic optimization
layer in the multivariable MPC. The control strategy was based on customer requirements, a benefit
analysis of MPC MD controls [3], and a case study
that was published [2]. Several new MVs were added to the control strategy with process upper and
lower limits. The MVs added were TAD1 Supply
Temperature (TAD1 Supply Temp), TAD1 dry
end differential pressure (TAD1 DE DP), TAD1
gap pressure (TAD1 Gap Pres), TAD2 dry end
differential pressure (TAD2 DE DP) and TAD2
gap pressure (TAD2 Gap Pres). Furthermore,
Machine Speed and Tickler Refiner were added as
disturbance variables (DVs) to the control strategy.
These provide feedforward information to MVs
such that disturbances will be minimized before
their impact on the controlled variables (CVs) is
measured. Figure 1 shows the relative locations of
the MVs and CVs and Fig. 2 shows the control
matrix. Bump tests were performed to determine
the transfer functions between the MVs and CVs.
EXPRESS MOISTURE MEASUREMENT TAD MEASUREMENT
For moisture, traditional tissue machine MD
controls almost always include controlling the
Pulp & Paper Canada November/December 2010
S. Chu,
Honeywell Process
Solutions,
North Vancouver, BC
R. MacHattie,
Honeywell Process
Solutions,
North Vancouver, BC
J. Backström,
Honeywell Process
Solutions,
North Vancouver, BC
pulpandpapercanada.com
PEER REVIEWED
T88
reel moisture only (i.e. the final product
moisture). However, the final moisture is
controlled by many elements far up the
machine where the moisture levels are
different, and there are drying elements
between those locations and the reel that
can further change the moisture [4]. The
various drying elements that can manipulate moisture also have varying efficiencies
and costs. These costs change with time.
It has long been understood that better
control will lead to better quality and cost
performance and this can be achieved by
measuring the moisture further up the
machine, but this has not been practical, until recently [5]. With the ExPress
Moisture scanner located after TAD2,
FIG. 1. Paper machine overview with Profit multivariable MPC.
Figure 1: Paper Machine Overview with Profit Multivariable MPC
moisture can now be measured upstream
Figure 1: Paper Machine Overview with Profit Multivariable MPC
of the reel and closer to the critical
Yankee
TAD1
TAD1
Yankee
Stock
Stock
TAD1 DE
TAD1 TAD2 Exh TAD2 DE
TAD2
Tickler
Supply Machine
Yankee
drying elements. Combining this new
Supply
Gap
Hood
TAD1
TAD1
Yankee
Flow
DPDE Gap
PresTAD2Temp
DP TAD2
Gap Pres
Fan
Stock
MachineSpeed
Stock Flow
TAD1
TAD1
Exh
TAD2
DE
Tickler Refiner
Supply
Temp
Supply
Gap Pressure
HoodTemp
Speed
Flow
Speed
Flow
DP
Gap
Pres
Temp
DP
Gap
Pres
Refiner
Fan
direct moisture measurement with the
Temp
Pressure
Temp
Speed
Dry Weight
reel moisture measurement via the Profit
Dry Weight
multivariable MPC along with the ecoReel
Reel
Moisture
nomic efficiencies of these various dryMoisture
ing elements then makes it possible to
TAD
TAD
Moisture
Moisture
truly optimize the energy consumption
TAD1
TAD1
of the machine, since the control has
Exhaust
Exhaust
Pressure
Pressure
direct feedback of process changes and
Figure
ProfitMultivariable
Multivariable MPC
MPC Control
true moisture levels going into various
Figure
2: 2:
Profit
ControlMatrix
Matrix
FIG. 2. Profit multivariable
MPC control matrix.
machine sections. As the cost of difIt has
longlong
beenbeen
understood
that better
controlcontrol
will lead
to lead to
EXPRESS MOISTURE
MOISTURE MEASUREMENT
MEASUREMENT – – TAD
It has
understood
that better
will
TAD
ferent energy forms change, the EXPRESS
Profit
better
quality and Coefficients
cost performance and this can be achieved
MEASUREMENT
table i. TAD Regulatory Loops with Linear
Objective
better
quality and cost performance and this can be achieved
MEASUREMENT
by measuring the moisture further up the machine, but this has
controller with the optimization layerFor
willmoisture, traditional tissue machine MD controls almost
measuring
the until
moisture
further
the machine,
but this has
MV
Energy
Fuel
Units
Linear
Objrecently
Coef
Cost/Eng
Unit
Foralways
moisture,
traditional
tissue
machine
MD
notbybeen
practical,
[5].upWith
the
ExPress
include
controlling
the reel
moisture
onlycontrols
(i.e. the almost
final
automatically adjust and attain the always
lowest
not been
practical,
until TAD2,
recently
[5]. With
thebeExPress
include
controlling
the reel
moisture
onlyis(i.e.
the final
Moisture
scanner
located after
moisture
can now
product
moisture).
However,
the final
moisture
controlled
Moisture
scanner
located
after
TAD2,
can now be
upstream
of the
reel and
closer
to themoisture
critical drying
product
moisture).
However,
final moisture
is moisture
controlled degmeasured
possible operating cost while maintaining
by many
elements
far upTemp
thethemachine
where
TAD1
Supply
Gas the
Fmeasured
upstreamthis
of new
the 0.680
reel
andmoisture
closer to
the critical drying
elements.
Combining
direct
measurement
by levels
manyare
elements
farand
upthere
the machine
where
the between
moisture
different,
are
drying
elements
the product quality.
TAD1 DE DP
Electricity
Inch Hwith
O the reel
47.267
Combining
new direct via
moisture
moisture this
measurement
the measurement
Profit
2 elements.
levels are different, and there are drying elements between
those locations and the reel that can further change the
with-0.030
themeasurement
economic efficiencies
of Profit
TAD1
Gap
Electricity
O
with the MPC
reelalong
moisture
via the
moisture
[4].
The
various
drying
elements
that
can manipulate
those
locations
and
thePres
reel that
can
further
change the Inch Hmultivariable
2
various drying
elements
it possible to
truly
MPC
alongthen
withmakes
the economic
efficiencies
of
moisture
also
have
varying
efficiencies
andGas
costs.
costs
TAD2
Exh
Temp
Degthese
Fmultivariable
5.858
[4].
The
various
drying
elements
that
canThese
manipulate
TAD REGULATORY CONTROL moisture
optimize
the energy
consumption
ofthen
the makes
machine,
since the to truly
these various
drying
elements
it possible
change also
with
time. DE
moisture
have
varying
efficienciesElectricity
and costs. These costs Inch H2O
TAD2
DP
40.249
control
has direct
feedback
of processofchanges
and true
LOOPS
optimize
the energy
consumption
the machine,
since the
change with TAD2
time. Gap Pres
Electricity
Inch H2O
-16.415
control has direct feedback
of process changes and true
Each TAD has several regulatory control
2
loops that can affect TAD Moisture and
Reel Moisture. The regulatory control
loops have different efficiencies and costs
because of the various forms of energy that
each consumes. The temperature control
loops consume natural gas and the pressure
control loops consume electricity.
By adding the temperature and pressure loops in the control strategy along
with associating costs and defining upper
and lower limits with each loop, the
Profit multivariable MPC with the economic optimization layer can distribute
the drying load in the TADs to minimize costs while not upsetting quality
(i.e. maintaining TAD Moisture and
Reel Moisture).
Table I shows the TAD loops that affect
the TAD Moisture and Reel Moisture.
pulpandpapercanada.com
MV
TAD1
TAD1
TAD1
TAD2
TAD2
TAD2
Supply Temp
DE DP
Gap Pres
Exh Temp
DE DP
Gap Pres
Energy Fuel
2
Units
Linear Obj Coef Cost/Eng Unit
Gas
Electricity
Electricity
Gas
Electricity
Electricity
deg F
Inch H2O
Inch H2O
Deg F
Inch H2O
Inch H2O
0.680
47.267
-0.030
5.858
40.249
-16.415
LINEAR OBJECTIVE COEFFICIENTS
The linear objective coefficients are parameters in the objective function of the optimization layer. The general form of the
objective function is eq. 1:
Minimize J = ∑bj × MVj
j
(1)
where are the linear objective coefficients for the MVs representing the energy
cost per engineering unit of the MVs.
Bump tests were performed to determine the linear objective coefficients for
each MV used in the Optimizer. Table I
shows the linear objective coefficients for
the MVs used in the Profit multivariable
MPC with the optimization layer.
November/December 2010 Pulp & Paper Canada 31
T89
TISSUE
Table II. MV Cost Ranking - Trial 1.
MV
Energy Unit Low Limit High Limit
TAD1 Supply Temp
Deg F
TAD1 DE DP
Inch H2O
TAD1 Gap Pres
Inch H2O
TAD2 Exh Temp
Deg F
Figure
Gas
Costs
TAD2 3:
DETAD
DP Natural
Inch
H2O
Trial
1 Gap Pres
TAD2
Inch H2O
Linear Obj Coef Cost/Eng Unit
300.0
450.0
1.0
3.9
0.4
1.5
175.0
250.0
and1.0
Electrical Costs3.5
0.2
1.5
0.68
47.30
-0.03
5.86
Figure
40.26 3:
Trial 1
-16.40
Process
Cost
Gain
(Cost/%
(%Moi/Eng Unit) Moi)
TAD
-0.12
-5.12
1.95
-0.45
Natural
-3.14
4.25
Optimization
Rank
Behavior
5.48
9.24
0.02
13.02
Costs
and
12.82
3.86
Gas
4
450 (max)
3
Controlling Moi
6
0.4 (max)
1
175 (min)
Electrical
Costs2
1 (min)
5
0.2 (max)
Figure 6: TAD2 Manipulated Variables – Trial 1
tem
Controlled Variables
12.7
25
12.6
12.5
20
15
12.2
12.1
10
12
DW (lb/ream)
DW (lb/ream)
12.3
Moisture (%)
12.4
11.9
5
11.8
11.7
ReelDwt PV
ReelMoi PV
ExpressMoi PV
12:19:52
Fig.4.4.Total
Total Costs
costs -–Trial
Figure
Trial1.1
Figure 7: CVs undisturbed – Trial 1
Figure 6: TAD2 Manipulated Variables – Trial 1
12:13:25
12:06:58
12:00:31
11:54:04
11:47:37
11:41:10
11:34:43
11:28:16
11:21:49
11:15:22
11:08:55
11:02:28
10:56:01
10:49:34
10:43:07
10:36:40
10:30:13
10:23:46
10:17:19
9:57:58
10:10:52
9:51:31
10:04:25
9:45:04
9:38:37
9:32:10
9:25:43
9:19:16
9:12:49
9:06:22
8:59:55
8:53:28
8:47:01
0
8:40:34
Figure 4. Total Costs – Trial 1
Fig. 3.3:TAD
gas
costs
andand
electrical
costsTrial 1.
Figure
TADnatural
Natural
Gas
Costs
Electrical
CostsTrial 1
8:34:07
11.6
Time
Fig
ECONOMIC OPTIMIZER – TRIAL 2
tem
Controlled
Variables
Since the energy costs change
with
time, the cost of energy is
updated in the control system periodically, which can have a
big impact on how the machine is optimized. Trial 2 shows
that even though different MVs were manipulated to minimize
energy costs, all CVs remained undisturbed.
Natural gas costs varied greatly in 2008. The peak of the
natural gas cost was in the summer of 2008 and it was
approximately double the cost in trial 1. Trial 2 was performed
with the cost of natural gas at close to its peak. With the
increased price of natural gas, the natural gas costs were
higher than the electrical costs. This is reflected during this
trial as natural gas usage decreased as electrical usage
Figure 3: TAD Natural Gas Costs and Electrical Costsincreased
(Figure
8). Tablevariables
3 shows -each
Trial Figure
1 Fig. 5.4.TAD1
- Trial 1.
Fig.
6. TAD2
manipulated
TrialMV
1. along with their
Totalmanipulated
Costs – Trialvariables
1Variables
Figure 5: TAD1
Manipulated
– Trial 1
respective
coefficients,
process
gains
Figure 6:linear
TAD2objective
Manipulated
Variables
– Trial
1 and cost
rankings.
As
expected,
TAD2
exhaust
temperature
Figure
5: TAD1
Manipulated
Variables
– Trial 1 and TAD1
Figure
7:
CVs
undisturbed
–
Trial
1
ECONOMIC OPTIMIZER - TRIAL 1
The trial sequence was
as follows:
o TAD2
Exh2Temp
(rank 1)since
is driven
supply
temperature are ranked
1 and
respectively
both
A trial was performed with the economic - Baseline data was collected
between
to
its
lowest
cost
operating
limit
(175
tem
MVs consume natural gas.
Controlled Variables
optimization layer turned on with the lin- 8:30 - 9:30.
deg F).
See Fig.
ECONOMIC
OPTIMIZER
– TRIAL
2 6.
The
trial sequence
was as follows:
ear objective coefficients that are shown
o 100.0 relative costSince
unitsthe
of energy
energy.costs change
o TAD2
DE
DPthe
(rank
driven isto
with time,
cost2)ofisenergy
- Baseline data was
collected
3:00
– 3:24. inch
in Table I. To rank the cost of each MV,
See Fig. 4.
its lowest
costbetween
operating
limit
updated in the control system
periodically,
which
can(1.0
have a
the linear objective coefficients must be - Attempted to put optimizer
ononconSeeis Fig.
6.
big impact
how the H2O).
machine
optimized.
Trial 2 shows
thatSome
even though
werethe
manipulated
to minimize
converted to a relative cost in common trol between 9:30 -4 10:44.
windupdifferent
o MVs
To keep
TAD1 Moisture
the
costs,MVs
all CVs remained
undisturbed.
units of Cost /% Moi. This can be accom- errors were encounteredenergy
with some
same, TAD1
Sup Temp (rank 4),
4
plished by taking the linear objective on the DCS that prevented
TAD2
Gap
and of
TAD1
Naturalthe
gasProfit
costs varied
greatly
in Pres
2008.(rank
The5)peak
the
natural
gascaused
cost was in
thePres
summer
and to
it was
coefficients and dividing by their respec- controller from optimizing.
This
Gap
(rank of
6) 2008
are driven
their
double themaximum
cost in trial
1. Trial 2limits
was performed
tive process gains. Table II shows each some abnormal behaviorapproximately
and hence higher
operating
(450 deg
with the cost of naturalF,gas
to itsH2O
peak.
With the
MV along with their respective linear energy costs.
0.2 at
andclose
0.4 inch
respectively).
increased
price
of
natural
gas,
the
natural
gas
costs
objective coefficients, process gains and - 10:44 - 12:30 - all windup errors were
These are the low cost MVs. Seewere
Figs.
higher than the electrical costs. This is reflected during this
cost rankings.
cleared and optimizer on
5 and 6.
trial as natural gas usage decreased as electrical usage
Figure 4. Total Costs – Trial 1
increased (Figure 8). Table 3 shows each MV along with their
Figure
5: TAD1
Manipulated
Variables – Trial 12010
respective
objective– coefficients,
process gains and cost
32 Pulp &
Paper
Canada November/December
Figure
7: CVslinear
undisturbed
Trial 1 pulpandpapercanada.com
rankings. As expected, TAD2 exhaust temperature and TAD1
12.7
25
12.6
12.5
20
DW (lb/ream)
12.3
15
12.2
12.1
10
12
11.9
5
11.8
11.7
ReelDwt PV
ReelMoi PV
ExpressMoi PV
12:19:52
12:13:25
12:06:58
12:00:31
11:54:04
11:47:37
11:41:10
11:34:43
11:28:16
11:21:49
11:15:22
11:08:55
11:02:28
10:56:01
10:49:34
10:43:07
10:36:40
10:30:13
10:23:46
10:17:19
10:10:52
10:04:25
9:57:58
9:51:31
9:45:04
9:38:37
9:32:10
9:25:43
9:19:16
9:12:49
9:06:22
8:59:55
8:53:28
8:47:01
8:40:34
0
8:34:07
11.6
Time
12.7
25
12.6
12.5
20
15
12.2
12.1
10
12
11.9
5
11.8
11.7
ReelDwt PV
ReelMoi PV
ExpressMoi PV
Time
12:19:52
12:13:25
12:06:58
12:00:31
11:54:04
11:47:37
11:41:10
11:34:43
11:28:16
11:21:49
11:15:22
11:08:55
11:02:28
10:56:01
10:49:34
10:43:07
10:36:40
10:30:13
10:23:46
10:17:19
10:10:52
10:04:25
9:57:58
9:51:31
9:45:04
9:38:37
9:32:10
9:25:43
9:19:16
9:12:49
9:06:22
8:59:55
8:53:28
8:47:01
0
8:40:34
11.6
8:34:07
DW (lb/ream)
12.3
Moisture (%)
12.4
Moisture (%)
12.4
EC
Sin
upd
big
tha
ene
Na
nat
app
wit
inc
hig
tria
inc
res
ran
sup
MV
Th
Throughout the trial (3:00 – 5:00) all CVs (Reel Dry Weight,
Reel Moisture and TAD Moisture) were
undisturbed, see
PEER REVIEWED
Figure 12.
Total energy costs during steady state optimization (4:25 –
5:00) = 99.4 relative
cost units Cost
of energy, see Figure 9. The
Linear Obj Process
energy
cost
reduction
was
0.6%.
Coef Gain
(Cost/%
Optimization
Table III. MV cost ranking - Trial 2.
MV
Energy Unit Low Limit High Limit
Cost/eng unit
(%Moi/eng unit) Moi)
Table 3: MV Cost Ranking – Trial 2
TAD1 Supply Temp
Deg F
300.0
450.0
TAD1 DE DP
Inch H2O
1.0
3.7
TAD1 Gap Pres
Inch H2O
0.4
1.5
TAD2 Exh Temp
Deg F
175.0
250.0
TAD2 DE DP
Inch H2O
1.0
2.9
Figure
6: TAD2
Manipulated
– Trial
o Pres
100.0
relative
unitsVariables
of energy.
See 1
TAD2 Gap
Inch H2cost
O
0.2
1.5
1.41
55.64
MV
-5.43
TAD1 Supply Temp
TAD1 DE DP
10.40
TAD1 Gap Prs
TAD2 Exh Temp
28.01
TAD2 DE DP
-24.97
TAD2 Gap Prs
eng unit
deg F
inch H2O
inch H2O
deg F
inch H2O
inch H2O
-0.12
-5.12
Low1.95
Limit High Limit
300.0
450.0
1.0
3.7
-0.45
0.4
1.5
175.0
-3.14 250.0
1.0
2.9
4.25 1.5
0.2
11.40
10.86
2.78
23.11
8.92
5.88
Rank
2
3
6
1
4
5
T90
Behavior
Controlling Moi
3.7 (max)
Optimization
Rank (max)
Behavior
0.4
2
controlling Moi
3.7 (max)
3
175
(min)
0.4 (max)
6
175 (min)
1
2.9
(max)
4
2.9 (max)
0.2
(max)
0.2 (max)
5
Linear Obj
Process
Cost
Coef
Gain
(Cost/%
(Cost/eng unit) (%Moi/eng unit)
Moi)
1.41
-0.12
11.40
55.64
-5.12
10.86
-5.43
1.95
2.78
10.40
-0.45
23.11
28.01
-3.14
8.92
-24.97
4.25
5.88
Figure 9.
The Optimizer was turned on at 3:25. Natural gas
Controlled Variables
usage decreased and electrical
usage increased. See
Figure 8.
o Some sheet breaks and machine upsets were
encountered during the trial between 3:35 –
4:25.
o Machine settles down to steady state
conditions after 4:25.
o TAD2 Exh Temp (rank 1) is driven to its
lowest cost operating limit (175 deg F). See
Figure 11.
o To offset the low TAD2 Exh Temp (rank 1),
Figure 9: Total Costs – Trial 2
TAD1 DE DP (rank 3), TAD2 DE DP (rank
4), TAD2 Gap Pres (rank 5), and TAD1 Gap
Pres
(rank 6) are
driven
to their maximum
Fig. 7.7:
CVs
undisturbed
- Trial
1. 1
Fig. 8:
8. TAD
TAD natural
gas
costs
andand
electrical
costsCosts
- Trial– 2.
Figure
Natural
Gas
Costs
Electrical
Figure
CVs
undisturbed
– Trial
operating limits (3.7, 2.9, 0.2 and 0.4 inch
Trial
2
H2O respectively) to help dry the sheet.
These
are the low–cost
MVs.2 See Figures 10
ECONOMIC
OPTIMIZER
TRIAL
and
11.
5
Since the energy costs change with time, the cost of energy is
o in TAD1
Sup system
Temp periodically,
(rank 2) is which
withincan
its have a
updated
the control
limits
and therefore
performing
big impactoperating
on how the
machine
is optimized.
Trial 2 shows
TAD Moisture
and Reel
Moisture control.
that even though
different MVs
were manipulated
to minimize
SeeallFigure
10.
energy costs,
CVs remained
undisturbed.
Throughout
thegas
trialcosts
(3:00varied
– 5:00)greatly
all CVsin(Reel
Weight,
Natural
2008.Dry
The
peak of the
Reel Moisture
andcost
TADwas
Moisture)
were undisturbed,
see it was
natural gas
in the summer
of 2008 and
Figure approximately
12.
double the cost in trial 1. Trial 2 was performed
with thecosts
costduring
of natural
at close
to its peak.
the
Total energy
steadygasstate
optimization
(4:25With
–
increased
price
of
natural
gas,
the
natural
gas
costs
were
5:00) = 99.4 relative cost units of energy, see Figure 9. The
than thewas
electrical
energy higher
cost reduction
0.6%. costs. This is reflected during this
trial as natural gas usage decreased as electrical usage
increased (Figure 8). Table 3 shows each MV along with their
Table 3:
MV Cost
Ranking
– Trial
2
respective
linear
objective
coefficients,
process gains and cost
Fig. 9.
Total
costs
-–
Trial
2. 2
Fig. 10. TAD1 manipulated variables - Trial 2.
Figure
9:
Total
Costs
Trial
Obj
Process
Cost
rankings. As expected, Linear
TAD2
exhaust
temperature
and TAD1 Figure 10: TAD1 Manipulated Variables – Trial 2
Coef
Gain
(Cost/%
Optimization
unit) (%Moi/eng
Moi)
Rank
Behavior
MV
eng unit
Low Limit High Limit
supply
temperature
are(Cost/eng
ranked
1 andunit)
2 respectively
since both
TAD1 Supply Temp
F
300.0
450.0
1.41
11.40
2
controlling Moi
oinchdeg
TAD1
DP gas.
(rank
3) is-0.12
ECONOMIC
OPTIMIZER - TRIAL 2
price of natural gas, the natural gas costs
3.7 (max)
TAD1 DE DP MVs
H2O
1.0 DE
3.7
55.64
-5.12within
10.86
3
consume
natural
0.4 (max)
TAD1 Gap Prs
inch H2O
0.4
1.5
-5.43
1.95
2.78
6
limits
and
Since
the
energy
costs
change
with
time,
were higher than the electrical costs. This
175 (min)
TAD2 Exh Temp
deg F
175.0 controlling
250.0
10.40Reel Moisture
-0.45
23.11
1
trial
was as28.01
follows:-3.14
TAD2 DE DP The inch
H2O sequence
1.0
2.9
8.92
4
2.9 (max)
0.2 of
(max)energy is updated in the control
TAD2 Gap Prs
inch H2O
0.2 Moisture.
1.5
-24.97 Fig. 5.
4.25
5.88
5 cost
and
TAD
See
the
is reflected during this trial as natural
- Baseline data was collected between 3:00 – 3:24.
Throughout the trial (8:30 - 12:20) all system periodically, which can have a big gas usage decreased as electrical usage
CVs (Reel Dry Weight, Reel Moisture impact on how the machine is optimized. increased (Fig. 8). Table III shows each
4
and Express Moisture) were undisturbed, Trial 2 shows that even though different MV along with their respective linear
see Fig. 7.
MVs were manipulated to minimize ener- objective coefficients, process gains and
98.8 relative cost units of energy is gy costs, all CVs remained undisturbed.
cost rankings. As expected, TAD2 exhaust
achieved while the Energy Optimizer is
Natural gas costs varied greatly in 2008. temperature and TAD1 supply temperaon, i.e. a 1.2% energy saving, see Fig. 4.
The peak of the natural gas cost was in the ture are ranked 1 and 2 respectively since
In this trial, the cost of natural gas is summer of 2008 and it was approximately both MVs consume natural gas.
less than electricity. Natural gas usage double the cost in Trial 1. Trial 2 was
The trial sequence was as follows:
increased and electricity usage decreased, performed with the cost of natural gas - Baseline data was collected between
see Fig. 3.
at close to its peak. With the increased 3:00 - 3:24.
Fi
-
12.7
25
12.6
12.5
20
DW (lb/ream)
12.3
15
12.2
12.1
10
12
Moisture (%)
12.4
11.9
5
11.8
11.7
ReelDwt PV
ReelMoi PV
ExpressMoi PV
12:19:52
12:13:25
12:06:58
12:00:31
11:54:04
11:47:37
11:41:10
11:34:43
11:28:16
11:21:49
11:15:22
11:08:55
11:02:28
10:56:01
10:49:34
10:43:07
10:36:40
10:30:13
10:23:46
10:17:19
9:57:58
10:10:52
9:51:31
10:04:25
9:45:04
9:38:37
9:32:10
9:25:43
9:19:16
9:12:49
9:06:22
8:59:55
8:53:28
8:47:01
8:40:34
0
8:34:07
11.6
Time
pulpandpapercanada.com
Figure 10: TAD1 Manipulated Variables – Trial 2
November/December 2010 Pulp & Paper Canada 33
Fi
Figure 10: TAD1 Manipulated Variables – Trial 2
T91
TISSUE
Fig. 11. TAD2 manipulated variables - Trial 2.
Figure
12: CVs
CVs undisturbed
undisturbed –- Trial
Trial 2.
2
Fig. 12.
Figure 11: TAD2 Manipulated Variables – Trial 2
5
o 100.0 relative cost units of energy.
See Fig. 9.
- The Optimizer was turned on at 3:25.
Natural gas usage decreased and electrical
usage increased. See Fig. 8.
o Some sheet breaks and machine
upsets were encountered during the
trial between 3:35 - 4:25.
o Machine settles down to steady state
conditions after 4:25.
o TAD2 Exh Temp (rank 1) is driven
to its lowest cost operating limit (175
deg F). See Fig. 11.
o To offset the low TAD2 Exh Temp
(rank 1), TAD1 DE DP (rank 3),
TAD2 DE DP (rank 4), TAD2 Gap
Pres (rank 5), and TAD1 Gap Pres
(rank 6) are driven to their maximum
operating limits (3.7, 2.9, 0.2 and 0.4
inch H2O respectively) to help dry the
sheet. These are the low cost MVs. See
Figs. 10 and 11.
o TAD1 Sup Temp (rank 2) is within its operating limits and therefore
performing TAD Moisture and Reel
Moisture control. See Fig. 10.
Throughout the trial (3:00 - 5:00) all
CVs (Reel Dry Weight, Reel Moisture
and TAD Moisture) were undisturbed,
see Fig. 12.
Total energy costs during steady state
optimization (4:25 - 5:00) = 99.4 relative
cost units of energy, see Fig. 9. The energy
cost reduction was 0.6%.
CONCLUSION
New sensor technology now permits the
placement of high precision moisture measurements at almost any location between
34 CONCLUSION
the press and reel on tissue machines. This LITERATURE
New sensor technology now
permits the
high
F. Haran,
R. placement
Beselt, R.ofMacHattie,
technology is well proven and reliable 1. Embedded High-speed
Solid State
Sensor, Pulp &
precision moisture measurements
at almost
any Optic
location
enough for continuous
control,
Canada,
(2007). This
between
the providpress andPaper
reel
on 108:12,
tissuepp.57-60
machines.
2. J.U. Backström, P. Baker, A Benefit Analysis
ing positive results fortechnology
producersisglobally.
well provenof and
reliable
enough
for
continuous
Model Predictive Machine Directional
Control of
When combined withcontrol,
multivariable
con-positive
Paper results
Machines,for
Proceedings
fromglobally.
2008 Control Sysproviding
producers
Pacific Conference,
16-18, Vancouver,
When combined
with tems/Pan
multivariable
control, itJuneproduces
trol, it produces consistent
drying along
BC, Canada, pp. 197-202 (2008).
consistent
drying
along
the
length
of
the
machine,
increasing
the length of the machine, increasing 3. S. Chu, Wet End Control Applications using a Mulproduct quality and reducing
manufacturing
costs. Strategy,
With the
Model Predictive Control
Proceedings
product quality and reducing
manufactur- tivariable
Jasper, the
AB, Canada,
addition of energy costs,from
thePACWEST
system is2008,
ableJune
to18-21,
optimize
ing costs. With the machine
additiontoofstay
energy
within (2008).
product quality requirements, while
4. T. Steele, R. MacHattie, A. Paavola, B.
costs, the system is able
to
optimize
the possible
running at the lowest
balancing
various& Control
Vyse, energy
Tissue & costs,
Towel Quality
Measurement
machine to stay within
product
Presentation
from
Tissue
America 2008
energy
forms quality
and their Advances,
associated
costs as
well
asWorld
product
Conference,
Marchwas
11-14,
Miami, FL
(2008).
quality. at
A the
1.2%lowest
energy cost
reduction
achieved
with
the
requirements, while running
5. P. Baker, R. MacHattie, B. Vyse, Early
optimization
enabled. and Control of Paper Machine Moisture,
possible energy costs,energy
balancing
variouslayerMeasurement
from 2008 Control Systems/Pan Pacific
energy forms and their associated costs as Proceedings
Conference, June 16-18, Vancouver, BC, Canada, pp.
well as product quality.REFERENCES
A 1.2% energy cost 105-110 (2008).
reduction was achieved
the energy
[1] with
F. Haran,
R. Beselt, R. MacHattie, “Embedded Highoptimization layer enabled.speed Solid State Optic Sensor”, Pulp & Paper Canada,
108:12, pp.57-60 (2007).
[2] J. U. Backström, P. Baker, “A Benefit Analysis of Model
Predictive Machine Directional Control of Paper
Machines”, Proceedings from 2008 Control Systems/Pan
Résumé: Les fabricants de papier visent tous à accroître leurs profits en réduisant les coûts
PacificlaConference,
Juneet16-18,
Vancouver,
BC,
d’exploitation, mais sans sacrifier
qualité du papier
l’aptitude
au passage
surCanada,
machine. Les
pp. 197-202
machines à papier mince modernes
sont (2008).
en général dotées de plus de vingt boucles de régulation de faible niveau et de multiples éléments permettant de mesurer les propriétés de la feuille à
[3] S. Chu, “Wet End Control Applications using a
divers endroits le long de la machine. C’est un vaste procédé compliqué en raison de son imporMultivariable
Model le
Predictive
Control
Strategy”,
tant couplage et les préposés
aux services techniques
trouvent difficile
à optimiser
sans avoir
recours à des techniques deProceedings
régulation multivariables
perfectionnées.
présente
from PACWEST
2008, La
June
18-21,communication
Jasper,
évalue les interactions du procédé
et les réductions
AB, Canada,
(2008). du coût de l’énergie possibles à l’aide d’un
modèle prévisionnel de commande avec un module d’optimisation qui entraîne le processus
automatiquement vers [4]
le coût
plus bas,
en tenantA.
compte
de laB.
nature
du “Tissue
processus
T. leSteele,
R. tout
MacHattie,
Paavola,
Vyse,
&et des
contraintes de qualité. La machine
papier à Measurement
l’étude était dotée
appareilAdvances”,
de mesure de la
Towel àQuality
& d’un
Control
teneur en eau à balayage rapide installé avant la sécherie monocylindrique (Yankee), en plus des
from
Tissue World America 2008
mesures prises à l’aide d’unPresentation
scanner classique
à l’enrouleuse.
Conference, March 11-14, Miami, FL (2008).
[5] P. Baker, R. MacHattie, B. Vyse, “Early Measurement
Reference: Chu, S., MacHattie, R., Backström, J. Multivariable Control and Energy
and Control of Paper Machine Moisture”, Proceedings
Optimization of Tissue Machines. Pulp & Paper Canada 111(6): T87-T91 (Nov/Dec 2010). Paper prefrom 2008
Systems/Pan
PacificSystems
Conference,
June15-17, in
sented at PacWest 2009, June 10-13,
2009 inControl
Sun Peaks,
B.C. and Control
2010, Sept.
Vancouver,
BC, Canada,
pp. 105-110
(2008).received January
Stockholm, Sweden. Not to be 16-18,
reproduced
without permission
of PAPTAC.
Manuscript
01, 2009. Revised manuscript approved for publication by the Review Panel July 12, 2010.
Keywords: Multivariable Control; Model Predictive Control (MPC)
technology; Machine Direction (MD); Control; Energy Optimization;
6
Economic Optimization; Tissue Machines; Maximizing Profit; Express
Moisture Sensor; Moisture Control; Yankee Dryer Control; Through
Air Dryer (TAD) Control.
Pulp & Paper Canada November/December 2010
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