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Cost Reduction Through Advanced Control of Chlorine Dioxide Generators
Bruce Allison, Leonardo Kammer and
Thanh Trung
Pulp and Paper Research Institute of
Canada
Vancouver, BC, Canada
Abstract
The cost of producing chlorine dioxide (ClO2) is a
major contributor to the overall bleaching cost in a
kraft mill. Therefore, methods to reduce this cost by
increasing ClO2 generator efficiency are needed. A
major factor that adversely affects generator
efficiency is poor process stability, and high
variability due to a lack of adequate process control.
In this report, we present an advanced control
strategy for ClO2 generators that consists of an
inferential feedback scheme for liquor composition
control, averaging level control of the ClO2 storage
tanks for production rate smoothing, and a method to
tune the generator liquor level controller. The
strategy was applied at two mills, and on two
different types of generator (SVP-LITE and R8). In
both cases, substantial reductions in the variability of
liquor composition, production rate, and generator
level were achieved. Efficiency gains due to reduced
consumption of sodium chlorate resulted in savings
estimated to be between $300,000 and $500,000 per
year.
1. Introduction
According to 1999 data [1], the single-vessel,
methanol-based (R8®/SVP-LITE®) process now
accounts for roughly 60% of chlorine dioxide (ClO2)
generation capacity in Canada. Other single-stage
processes represent another 21% of capacity. Despite
improvements in process technology, the cost of
producing ClO2 is still a major contributor to the
overall bleaching cost in a kraft mill. Therefore,
methods to reduce this cost by increasing generator
efficiency are needed. Trace contaminants that enter
with the chemical feeds are a major source of
efficiency loss due to decompositions for some mills,
but this problem can usually be solved by using
chemical filters. Another major factor that adversely
affects efficiency is poor process stability and high
Rob Laurendeau
Canfor Corporation,
Intercontinental Mill
Prince George, BC,
Canada
Gerry Pageau
Howe Sound Pulp and
Paper Limited
Port Mellon, BC,
Canada
variability due to a lack of adequate process control.
This is the topic of the present report.
Chlorine dioxide generator stability and
efficiency are highly dependent on tight control of the
chlorate and acid concentrations in the generator
liquor. However, these variables are seldom, if ever,
under closed-loop control. Early generator control
systems focused on regulating the ClO2 solution
strength [2]. While this helps to stabilize downstream
operations in the bleach plant, it has no impact on the
operation of the ClO2 generator itself. In [3], a control
strategy for a R8 generator was proposed, but no
consideration was given to the control of liquor
composition, and the strategy was tested in simulation
only. A multivariable predictive controller (MPC)
was applied to a dual-vessel ClO2 plant in [4].
Although the authors reported controlling chlorate
and acid concentrations in the generator liquor, it is
not clear from their paper how the measurements
were made. The need to coordinate two reactors was
cited as the justification for using MPC, however, it is
questionable if MPC would be required for the
control of a single-vessel plant. Recently, an
advanced control strategy called ERCO Smarts™ was
applied to a R8 generator [5]. This strategy uses
thermodynamic relationships between process
variables, such as temperature, pressure and
concentration, as well as balance equations to control
liquor composition. As the control system is being
sold commercially, very little detail is given in their
paper.
In recent work at Paprican [6], we proposed a
novel generator liquor composition control strategy
for single-vessel chlorine dioxide processes. In the
scheme, Paprican’s infrared sensor technology is used
to measure the composition of the generator liquor. A
Kalman filter is then used to filter the FT-NIR and
laboratory concentration measurements, and to
provide estimates of the liquor composition between
measurements. Finally, the Kalman filter estimates
are employed in a decentralized, inferential feedback
2. Process Description
In this report, we are mainly concerned with the
single-vessel, methanol-based (R8®/SVP-LITE®)
chlorine dioxide process. Several references that
describe the process technology are available (see [1],
[10]), so only a brief description is given here. Figure
1 shows a simplified schematic diagram of the singlevessel process. Here, methanol (CH3OH) is used to
reduce sodium chlorate (NaClO3) in an acidic
solution (H2SO4). The main chemical reaction is:
12NaClO3 + 8H2SO4 + 3CH3OH → 12ClO2 + byproducts
(1)
The heart of the process is a titanium reactor,
which
combines
ClO2 generation, sodium
sesquisulphate crystallization, and evaporation in a
single-vessel. The main body of the generator is
essentially a forced-circulation evaporator. During
normal operation, the generator is about half full of a
liquid slurry composed of sodium sesquisulphate
crystals (a by-product), and unreacted chlorate and
acid in solution. Recirculating liquor passes from the
body through an in-line circulating pump, up through
a steam-heated reboiler, and back into the generator
body to complete the loop. The reboiler is a shell and
tube heat exchanger to which steam is added to heat
the liquor and sustain evaporation. Sodium chlorate is
fed into the recirculation loop just ahead of the pump.
Sulphuric acid is added just after the heat exchanger.
The point for methanol addition depends on the
process. Methanol is mixed with sodium chlorate
prior to addition in the SVP-LITE process, and after
the heat exchanger in the R8 process (see Figure 1).
Chlorine dioxide is formed in the recirculating liquor
so that the mixture returning to the generator body
contains a significant volume of ClO2 gas and water
vapour. This gas mixture enters a vapour space above
the liquor and is separated from unreacted material
that falls back into the liquor reservoir. Removal of
gas is facilitated by the fact that the generator
operates under a vacuum.
Steam
ClO2
Storage
Acid
Condenser
ClO2
Generator
Methanol†
Saltcake
Absorber
Chilled
Water
To Ejector
Filter
Reboiler
control scheme. In related work, Paprican’s averaging
level control algorithm (Paprican ALeC) was applied
to ClO2 storage tank level control in [8]. Averaging
level control aims to control a storage tank level
within upper and lower limits while minimizing
changes to the manipulated variable, in this case
generator production rate. In addition, closed-loop
process identification was used in [9] to improve the
tuning of a generator liquor level controller.
The purpose of this report is to review results of
the work mentioned above at two mills. In particular,
we are interested in assessing the impact of advanced
control on generator efficiency. The remainder of this
report is organized as follows. The single-vessel
chlorine dioxide process is described in Section 2.
Section 3 outlines the overall control strategy for the
generator. Results in terms of variability reduction
and efficiency gains at two mills, one equipped with a
SVP-LITE generator and the other with a R8, are
covered in Section 4. Section 5 discusses reasons for
the difference in results between the mills, and the
potential opportunity for additional efficiency gains
by optimization of composition target values. Finally,
the conclusions of the study are stated in Section 6.
A
Chlorate/
Methanol*
To
Bleach
Plant
Figure 1. Simplified schematic diagram of the single-vessel chlorine dioxide process.
Entry point for methanol; SVP-LITE (*) and R8 (†). Location of ClO2 flow and
concentration measurements (“A”).
Gases from the top of the generator pass into an
indirect contact cooler where the water vapour is
condensed. The ClO2 gas then passes into an
absorption tower, where it is dissolved in chilled
water forming a solution that is approximately 10 g/L
ClO2, and sent to storage. Sodium sesquisulphate
crystallized in the generator body is removed when a
portion of the slurry is pumped to a rotary drum filter.
Filtrate returns to the generator and the dewatered
saltcake drops into a dissolving tank. The saltcake is
dissolved in black liquor as a means of conveying it
into the chemical recovery system.
3. Control Strategy
3.1
Mass Flow Control of Chemical Feeds
As a starting point, it is assumed that mass flow
controllers exist for all three chemical feeds, i.e.
methanol, acid and chlorate. Typically, the flows are
measured volumetrically. For methanol and acid
(93% by weight), conversion to mass flow is usually
done using an assumed density. Sodium chlorate is
usually supplied as a solution that contains a nominal
sodium chlorate concentration and an unknown but
usually very low sodium chloride concentration.
Conversion to sodium chlorate mass flow may be
done either by assuming a nominal sodium chlorate
concentration, or by measuring the density and
temperature and calculating the mass flow using
standard correlations for chlorate solutions.
3.2
Chlorine Dioxide Mass Flow
Chlorine dioxide flow is typically measured
volumetrically in the line between the absorption
tower and the storage tank (point marked “A” in
Figure 1). An optical analyser in the same line
provides a ClO2 concentration measurement so that
the mass flow can be calculated.
3.3
Ratio Control of Chemical Feeds
As shown in Figure 2, the methanol mass flow
setpoint is proportioned (ratioed) to the target ClO2
production rate in a ratio controller. Chlorate and acid
mass flow setpoints are determined in one of two
ways. Figure 2a shows chlorate and acid being ratioed
to the methanol setpoint, while Figure 2b shows them
being ratioed to the target ClO2 production rate. Note
that as a result of this ratio control structure, all three
feeds are automatically manipulated in a feedforward
manner when the target production rate is changed.
3.4
Liquor Composition Control
Figure 3 is a simplified schematic diagram of the
liquor composition control strategy for chlorate, first
described in [6]. The heart of the strategy is the
Kalman filter block. The Kalman filter is a DCS
algorithm that executes once per minute. The inputs
to this block are real-time measurements of chlorate
and ClO2 mass flow, the generator liquor chlorate
concentration test, and FT-NIR measurement when
available. The output is the liquor chlorate
concentration estimate. This estimate is input as the
process variable to a standard PID controller. The
PID controller setpoint is the chlorate concentration
target. The PID controller manipulates a ratio (here
kg chlorate / kg methanol is assumed) which is
multiplied by the methanol mass flow setpoint in a
ratio controller to get the chlorate mass flow setpoint
(see Figure 2a). The generator liquor acid
concentration is controlled in the same way. Note that
the Kalman filter sampling interval (one minute) is
generally much faster than the sampling interval for
laboratory tests (2-4 h) and the FT-NIR analyzer (~15
min). Thus, the PID controller uses Kalman filter
estimates (model predictions) for feedback between
tests.
ClO2
Production
Target
(kg/min)
Ratio
Chlorate / Methanol
(kg/kg)
Ratio
Methanol / ClO2
(kg/kg)
ClO2
Production
Target
(kg/min)
RATIO
Controller
RATIO
Controller
Methanol Feed
Flow (kg/min)
RATIO
Controller
Ratio
Methanol / ClO2
(kg/kg)
Methanol Feed
(kg/min)
Chlorate Feed
Flow (kg/min)
ClO2
Generator
ClO2
Production
(kg/min)
ClO2
Production
Target
(kg/min)
RATIO
Controller
Methanol Feed
Flow (kg/min)
RATIO
Controller
Ratio
Acid / ClO2
(kg/kg)
+
−
PID
Controller
Ratio
RATIO
Controller
Chlorate Feed
(kg/min)
ClO2
Generator
ClO2
Production
Measurement
(kg/min)
Chlorate
Test
(g/L)
Kalman
Filter
Chlorate Concentration
Estimate (M)
a)
Ratio
Chlorate / ClO2
(kg/kg)
RATIO
Controller
Chlorate
Concentration
Target
(g/L)
Acid Feed
Flow (kg/min)
Ratio
Acid / Methanol
(kg/kg)
Ratio
Methanol / ClO2
(kg/kg)
RATIO
Controller
Figure 3. Block diagram of liquor composition estimation and control strategy for
chlorate. The acid concentration in the liquor is controlled in a similar way.
3.5
Chlorate Feed
Flow (kg/min)
ClO2
Generator
ClO2
Production
(kg/min)
Acid Feed
Flow (kg/min)
b)
Figure 2. Ratio control of chemical feeds. Methanol mass flow is ratioed to the target ClO2
production rate. (a) Chlorate and acid mass flows are ratioed to methanol. (b) Chlorate and
acid mass flows are ratioed to production rate.
ClO2 Storage Tank Level Control
Figure 4 is a simplified schematic diagram of the
ClO2 storage tank level control loop, first described in
[8]. The heart of this loop is the optimal averaging
level controller (Paprican ALeC). The ALeC block is
a DCS algorithm that executes once every ten
seconds. The inputs to ALeC are the tank level
measurement, the setpoint, and upper and lower level
constraints. The output is the ClO2 production rate
target. The methanol feed flow setpoint is ratioed to
the target ClO2 production rate, as shown in Figure 2.
Ratio
Methanol / ClO2
(kg/kg)
Tank Level
Constraints
(%)
Tank
Level
Setpoint
(%)
ALeC
ClO2
Production
Target
(kg/min)
RATIO
Controller
Methanol
Feed
(kg/min)
ClO2
Generator
ClO2
Solution
(L/s)
Storage
Tank
Tank
Level
(%)
1.
2.
3.
Figure 4. Block diagram of optimal averaging level control strategy (Paprican ALeC)
applied to chlorine dioxide storage tank level control.
3.6
Generator Liquor Level Control
The generator liquor level control loop is a
simple PID feedback controller from the liquor level
(calculated from differential pressure and density
measurements) to the reboiler steam flow. This
controller also contains feedforward compensation for
the total inlet water flow. A closed-loop process
model identification method and PID tuning rules for
this loop were reported in [9].
4. Results
4.1
Mill A
Mill “A” is equipped with a SVP-LITE generator
(see Figure 1) designed to produce 45 t/d of ClO2.
This generator is very well instrumented. Pure
sodium chlorate mass flow is calculated on-line from
measurements of volumetric flow, density and
temperature of the chlorate feed solution using a
Micro Motion device. Sulphuric acid is supplied at
93% by weight and, under some circumstances, may
be diluted prior to addition. The ClO2 solution
strength is monitored and controlled via a continuous
photometric analyser. Laboratory tests of liquor
composition (chlorate and acid concentration) are
made once every 2 hours. This mill was also the first
to install a FT-NIR analyser to measure the
concentrations of chlorate and acid in the liquor. The
FT-NIR measurements are made once every 15 min.
Details on the development and commissioning of the
analyzer were obtained from [7].
Mill A is also equipped with a Foxboro IA
distributed control system (DCS) to handle many of
the low level loops, such as flow rate of chemical
feeds, generator pressure and level. All chemical
feeds are metered by mass using mass flow
controllers as discussed in the previous section. The
setpoints of the mass flow controllers are determined
by ratio controllers (see below). The Kalman filters
and ALeC were each implemented as sequence block
algorithms in the Foxboro system.
The new control strategy was commissioned
between May and September, 2002. During this
period, the following steps were taken:
4.
ALeC implemented – May, 2002.
Chlorate and acid ratioed to methanol instead of
methanol and acid ratioed to chlorate – July,
2002.
Liquor composition control commissioned –
September, 2002.
Generator liquor level controller retuned –
November, 2002.
Regarding step 2, the original configuration was
to ratio acid and methanol to chlorate. This appears to
be standard practice for this make of generator.
However, this clearly represents a conflict since,
under such a strategy, the chlorate mass flow must be
manipulated to control both production rate and
chlorate concentration. By ratioing chlorate and acid
to methanol, this conflict is resolved since methanol
can be used to control production rate and the ratio of
chlorate to methanol used to control chlorate
concentration.
Results from Mill “A” are contained in Figures 5
to 9 and Tables I and II. Here, “manual operation”
refers to operation with ratio control only, i.e. as
shown in Figure 2a. The term “automatic control”
refers to a fully automated system where ALeC
determines the target production rate (Figure 4) and
chlorate and acid ratios are determined by
composition feedback (Figure 3).
Figure 5. Comparison of conventional (PID) control and ALeC showing reduction in
production rate (methanol) variability with ALeC. Level setpoint – blue line, level
measurement – green line, level constraints – red dotted line. Light red shaded areas
correspond to periods of manual operation.
Figure 5 shows a comparison between tight
storage tank level control (PID) and ALeC. Under
ALeC, the storage level is allowed to deviate farther
from the setpoint, provided it does not exceed upper
or lower level constraints, which are shown by the
dotted red lines. The main benefit of allowing the
level to vary over a wider range is that the production
rate manipulations (methanol flow) are generally
much smoother. Thus, ALeC uses the available
storage tank capacity to partially buffer the chlorine
dioxide plant from the changing demands of the
bleach plant.
switch to auto
a)
be more reliable than the laboratory tests and so the
Kalman filter for acid was designed to put more
weight on the FT-NIR measurements. This difference
between the Kalman filters for chlorate and acid can
also be seen in Figure 8.
Returning to Figure 6, the chlorate concentration
continues to drift slightly, even after the controller is
switched on at the one day mark, but then it lines out
nicely once the acid controller is switched on at the
two day mark, as shown in Figure 6b. Also, the
controller output is less variable after the controller is
switched on. Note that the variability of the Kalman
filter estimates is much smaller than the variability in
either the laboratory tests or the FT-NIR readings,
particularly under automatic control. This is because
most of the “true” liquor composition variability is
removed by the feedback controller, and the
variability that remains is essentially random noise
that is filtered out.
1.
2.
3.
4.
switch to auto
b)
Figure 6. Two ten day periods of operation. “Manual” refers to a period during
August 2002 before the liquor composition controllers were commissioned, and
“Automatic” a period from September 2002 when the controllers were first
commissioned. (a) Chlorate controller. (b) Acid controller.
Figure 6 shows two ten day periods of operation,
one (titled “Manual”) from August 2002 before the
liquor composition controllers were commissioned,
and the other (titled “Automatic”) from September
2002 during the period when the controllers were first
commissioned. Figure 6a shows the chlorate
concentration data – setpoint, laboratory tests, and
Kalman filter estimates – in the upper plot, and
controller output – essentially a scaled and
normalized ratio where 100% corresponds to
stoichiometric chlorate addition – in the lower plot.
Figure 6b shows the acid data, including the FT-NIR
readings. The FT-NIR readings are not shown for
chlorate because the data was very noisy. To reflect
this, the chlorate Kalman filter was designed to use
laboratory tests primarily. The opposite situation was
true for acid, that is, the FT-NIR data was thought to
Figure 7. Comparison of liquor chlorate and acid composition variability
(laboratory test data) under manual operation and automatic control for Mill “A”.
1. Implementation of ALeC. 2. Feed chemicals ratioed to methanol. 3.
Implementation of liquor composition controls. 4. Retuning of generator liquor
level controller.
TABLE I:
Liquor composition variability reduction – Mill “A”.
Control
Mode
Manual
Operation
Automatic
Control
SVP-Lite Generator
Chlorate
Acid
Standard
Standard
Target
Target
Deviation
Deviation
39 g/L
27 g/L
280
350
(0.37 M)
(0.55 N)
g/L
g/L
(2.6
(7.1
18 g/L
14 g/L
M)
N)
(0.17 M)
(0.29 N)
Figure 7 and Table I compare liquor composition
variability under manual operation and automatic
control. Figure 7 shows chlorate and acid laboratory
test data for the year 2002 when all of the control
improvements were implemented. The numbers in the
figure refer to the steps listed above. In Table I, the
variability is compared under manual and automatic,
where the first four months were taken to represent
manual operation, and the last four months to
represent automatic control. As seen from the table,
the standard deviation of chlorate was reduced by
about 55%, and acid by about 45%. Note that this
comparison, and others made later on in this report, is
based on laboratory test data. Given that there is a
certain amount of random variability (due to
sampling, testing, etc.) in laboratory data (see Figure
6 for example), it is highly likely that the variability
reduction in the true underlying concentrations is
even greater. Thus, the values reported here may be
considered conservative estimates of variability
reduction.
In Figure 8, a two and one-half day period of
operation is shown where the mill experienced a
power outage due to a severe electrical storm. As
indicated in the figure, the power outage occurred at
about the 24-hour mark. The reason for showing this
figure is that, during the period immediately after the
outage and for about the next 18 hours, laboratory
tests of the generator liquor were unavailable as the
operators and mill workers were busy getting the mill
back up and running. During this period, the
generator controls operated with FT-NIR analyser
feedback only. As can be seen in the figure, tight
control of the liquor composition was maintained
during this time, and when a laboratory test was
finally made at about the 42 hour mark, it agreed
very well with the FT-NIR measurements and the
Kalman filter estimates.
Clearly, the consumption of all three chemical feeds
decreased after implementation of the new control
system. The decrease in methanol use observed in the
first six months of 2002 (prior to commissioning the
new controls) is believed to be associated with a
change in liquor composition target values made in
the fall of 2001. This also appears to have had a small
effect on chlorate use, but no apparent effect on acid.
Acid use clearly decreased after implementation of
the new controls, and chlorate use continued to
exhibit a downward trend. Taking the period January
1, 2001 to June 30, 2002 to represent “manual
operation”, and the period July 1, 2002 to June 30,
2003 to represent “automatic control”, then the
savings associated with this improvement may be
calculated, as shown in Table II. Here we can see, for
example, that chlorate use dropped by 0.062 kg per
kg ClO2. This represents an efficiency gain of 3.6%.
The efficiency gain for acid was 4.3%. No attempt
was made to identify the source of efficiency gain,
but one plausible explanation is a decrease in the
number of decompositions caused by chemical
imbalance. The values in the column labelled
“Savings” in Table II were calculated by
multiplying
the
difference
in
chemical
consumption (manual minus automatic) by the
chemical cost and total yearly ClO2 production.
After summing the values in this column (not
including methanol), the total savings associated
with making the transition from manual to
automatic were estimated to be over $500,000 per
year for this mill.
Control System
Implemented
Outage
Figure 8. Generator start-up after a power outage showing controls operating with
FT-NIR analyser feedback only.
Figure 9 shows chemical consumption (kg feed
per kg ClO2 produced) going back to the period
starting January 1, 2001. Chemical consumption was
computed from accumulated values (flow times
concentration) from the DCS data base over
successive six-month periods (i.e. semi-annually).
Figure 9. Reduction in chemical consumption over time. The new controls were
commissioned between May and September 2002. Chlorate and acid efficiency gains
were estimated to be 3.6% and 4.3%, respectively.
4.2
Mill B
Mill “B” has a R8 generator with a design
capacity of 35 t/d. The layout of the R8 process is
TABLE II:
Savings – Mill “A”.
Feed
Chemical
Chlorate
Acid
Methanol
Chemical Consumption
( kg per kg ClO2 produced)
Manual Automatic Difference
1.682
1.620
-0.062
1.064
1.016
-0.048
0.227
0.214
-0.013
essentially that of the SVP-LITE, except for the
location of methanol addition as indicated in Figure 1.
Mill B was not equipped with either the Micro
Motion or FT-NIR devices. Thus, it was necessary to
assume a value for the sodium chlorate concentration
in the chlorate feed solution, and to base the liquor
composition control strategy on laboratory tests only,
which were done on average once every four hours.
Mill B was equipped with an ABB Master DCS, and
the Kalman filters and ALeC were implemented as
program modules using algorithms written in AMPL,
which is a process-oriented high level language that
uses graphic symbols. It is worth mentioning that, in
this case, the mill engineer was able to complete the
implementation and commissioning on his own.
Examples of the Foxboro code from Mill A were
provided along with a default set of tuning
parameters. Consultations with Paprican staff were
done via telephone and Email. No capital
expenditures were required, and the only cost was the
time required to implement the new algorithms in the
DCS.
1.
2.
Figure 10. Comparison of liquor chlorate and acid composition variability
(laboratory test data) under manual operation and automatic control for Mill “B”.
1. Implementation of ALeC. 2. Implementation of liquor composition controls.
In the case of Mill B, ALeC was commissioned
in November 2002 and the liquor composition control
strategy in January 2003. The results are shown in
Figure 10 and Table III, and are once again based on
laboratory test data. Despite the lack of
Savings
(k$/y)
493
49
96
Efficiency (%)
Manual
93.8
91.1
-
Automatic
97.4
95.4
-
Difference
3.6
4.3
-
instrumentation, substantial reductions in liquor
composition variability were still achieved. As shown
in Table III, chlorate standard deviation was reduced
by about 40% and acid by about 30%. Table IV
compares chemical efficiencies in September 2003 to
the yearly average for 2002. In this case, chlorate
efficiency increased by 2.2% under automatic control.
This represents a yearly savings of almost $300,000
for this mill.
TABLE III:
Liquor composition variability reduction – Mill “B”.
R8 Generator
Control
Mode
Manual
Operation
Automatic
Control
Chlorate
Standard
Target
Deviation
(34 g/L)
(170
0.32 M
g/L)
1.60
(20 g/L)
M
0.19 M
Target
(440
g/L)
9.0 N
Acid
Standard
Deviation
(25 g/L)
0.50 N
(17 g/L)
0.34 N
5. Discussion
There are potentially many reasons why Mills A
and B have different absolute efficiencies. Despite
sharing common chemistry, the SVP-LITE and R8
processes run at very different operating points. For
example, the R8 process typically operates at a much
higher acidity (compare target values in Tables I and
III). Furthermore, the chemical feeds, particularly the
sodium chlorate, are purchased from different sources
and, despite having similar production rates, the
generators differ in their design capacity. Finally, and
probably most important, in many cases the data used
to evaluate efficiency comes from field devices that
are prone to error. Thus, as a rule, one should not put
too much emphasis on an absolute efficiency
calculation. Differences in operation from one period
to another, on the other hand, are much more
meaningful. This is because any bias errors cancel
out. Thus, if we limit the discussion to changes in
performance brought about by the implementation of
automatic control, we can say that the variability
TABLE IV:
Savings – Mill “B”. (Sept 2003 vs 2002 average).
Feed
Chemical
Chlorate
Acid
Methanol
Chemical Consumption
( kg per kg ClO2 produced)
Manual
1.753
1.200
0.182
Automatic
1.711
1.200
0.183
Difference
-0.042
0.
0.
reduction and efficiency improvement for Mill A
were seen to be, roughly speaking, twice that of Mill
B. The question is why?
The most likely explanation is the difference in
instrumentation. Mill A is instrumentation rich. Since
pure chlorate mass flow is measured on-line, changes
in the composition of the chlorate feed – one of the
main disturbances – may be detected and corrected by
the chlorate mass flow controller in a relatively short
period of time. In the case of Mill B, corrections
cannot be made until these disturbances pass through
the process and are detected in the laboratory liquor
composition measurement, which is made only once
every four hours. In another scenario, if some
unmeasured disturbance, for example a change in
efficiency, occurs in the process, then Mill A again
has the advantage. Since the FT-NIR analyser
measures the liquor composition once every 15
minutes, any change will be detected and corrected
relatively quickly. Once again, Mill B may have to
wait up to four hours to detect the same disturbance.
M+
M+
NH
igh
NL
ow
Lim
White-Out
Zone
it
Automatic Control
plus
Target Shift
Manual
Operation
Lim
it
Sluggish
Reaction
Zone
Automatic
Control
Figure 11. Illustration of how variability reduction, followed by a target shift,
might allow operation at a higher overall efficiency, while still operating in the
target zone.
The results presented in this report support the
idea that efficiency gains may be achieved simply by
stabilizing the process and reducing variability.
However, variability reduction alone may not be
enough to realize the full potential of automatic
Savings
(k$/y)
294
0
0
Efficiency (%)
Manual
90.0
-
Automatic
92.2
-
Difference
2.2
-
control. Figure 11 illustrates how variability
reduction, followed by a target shift might allow
operation at a higher overall efficiency, while still
maintaining operation within the target liquor
composition zone. As there is very little, if any,
information in the open literature on what
concentration targets might yield the best efficiency,
it is likely that each mill would have to conduct a set
of trials to find the best operating point for their
particular operation. The generator manufacturer
should be involved to ensure that safe operating
conditions are maintained at all times. However, it is
important to note that it is only by first reducing the
variability about a set of target values through the
implementation of automatic control, and then
shifting those targets, that such a process
investigation even becomes possible.
6. Conclusions
In this report, we have proposed a control
strategy for the methanol-based single-vessel chlorine
dioxide process. The strategy was applied at two mills
and on two different types of generator (SVP-LITE
and R8). In both cases, substantial reductions in the
variability of liquor composition, production rate and
generator level were achieved. Efficiency gains due
to the reduced consumption of sodium chlorate
resulted in savings estimated to be between $300,000
and $500,000 per year, depending on the available
instrumentation. In addition, the control strategy has
been well received by the plant operators and process
engineers alike. The main conclusion that one can
draw from this work is that substantial savings may
be achieved by reducing variability and increasing
process stability, particularly with respect to
variability in the composition of the generator liquor.
7. References
1.
2.
3.
4.
5.
D. Owen, “Chlorine Dioxide Generation”, Course
notes from: TECH 99: Pulp Bleaching Course,
PAPTAC, Thunder Bay, ON, Oct. 3-8 1999.
J.W.C. Evans, “Automated ClO2 Generation Improves
Bleaching, Cuts Effluent”, Pulp & Paper, Vol. 57, No.
2, 1983, p. 69-71.
P. Tessier, M. Pudlas, and Y. Ying, “Dynamic
Modelling, Simulation and Control of a R8
Generator”, In PacWest Conference, Jasper, AB,
Session 2A, Paper 1, May 17-20 2000.
D.B. Smith, J. Robinson, P. Aggarwal, and H.
Lindstrom, “Advanced Control of a Chlorine Dioxide
Plant”, In TAPPI/ISA-PUPID Process Control,
Electrical, and Information Conference, San Antonio,
TX, Session 12, Mar. 25-29 2001.
C. Pu, J. Birks, and J. Hopmans, “ERCO Smarts™
Advanced Control of a Chlorine Dioxide Plant: The
First Year”, In PacWest Conference, Harrison Hot
Springs, BC, Session 4B, Paper 3, May 7-10 2003.
6.
B.J. Allison, “A Liquor Composition Control Strategy
for Chlorine Dioxide Generators”, In PacWest
Conference, Jasper, AB, Session 3, Paper 3, May 1922 2004.
7.
T. Trung, Personal Communications, July 5 (2001).
8.
L.C. Kammer, B.J. Allison, R. Laurendeau, “Optimal
Averaging Level Control of Chlorine Dioxide Storage
Tanks”, In PacWest Conference, Harrison Hot
Springs, BC, Session 2A, Paper 2, May 9-12 2007.
9.
L.C. Kammer, B.J. Allison, R. Laurendeau, M. Kopat,
“Closed-Loop Identification of an Integrating Process:
Application to Chlorine Dioxide Generator Liquor
Level Control”, Pulp Paper Can. 107(3):T59-62
(2006).
10. Alkaline Pulping, T.M. Grace, B. Leopold, and E.W.
Malcolm, eds., 3rd ed. (Pulp Pap. Manuf., vol. 5), Jt.
Textbook Cttee. Pap. Ind., Montreal, 1989.