Note: Descriptions are shown in the official language in which they were submitted.
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METHOD AND APPARATUS FOR ESTIMATING RELATIVE PROPORTION OF
WOOD CHIPS SPECIES TO BE FED TO A PROCESS FOR PRODUCING PULP
Field of the invention
The present invention relates to the field of pulp and paper process
automation, and more particularly to methods and apparatus for estimating and
controlling relative proportion of wood chips originating from a plurality of
sources
characterized by various wood species, in a mass of wood chips to be fed to a
process for producing pulp.
Background of the invention
Wood chips being one of the main raw materials entering into pulp production
processes such as chemical (Kraft) and thermomechanical pulping (TMP)
processes,
variations in their physical properties have a direct impact on process
control
performance as well as on pulp and paper qualities. In the particular case of
TMP
processes, the quality of wood chips being fed to the refiners is of a great
importance, since it is known to affect, along with process operating
parameters, the
rate of wear affecting refiner plates, as discussed by Myllyneva, J. et al. in
"Fuzzy
Control of Thermomechanical Pulping", Proceedings of IMPC 1991, Minneapolis,
MN, pp. 381-384. It is well known that a typical TMP process is characterized
by
three critical operational variables, namely specific energy, production rate
and
consistency. For a given process design, specific energy consumption is the
parameter that correlates most strongly to evolving pulp properties, as
explained by
Mosbye, K., et al. in "Use of Refining Zone Temperature Measurements for
Refiner
Control", Proceedings of IMPC 2001, Helsinki, Finland, June 2001. While
specific
energy can in theory be kept constant through adjustments to motor load or
production rate, in practice the absence of online data about dry wood
chip/fibre
volume and moisture content means that the control of this variable will be
subjected
to instability, as mentioned by Cluett, W. R., et al. in "Control and
Optimization of
TMP Refiners", Pulp & Paper Canada, 96:5 (1995) pp.31-35. The production rate,
which is directly affected by the quantity of dry fibre refined, has a major
impact on
both energy consumption and pulp properties. The dilution water flow rate
depends
on chip moisture content and the consistency target as stated by Myllyneva, J.
et al.
in the above-mentioned reference. Consistency variations during normal
operation
are at least 4-6% and even higher, as reported by Hill, J., et al. in "On the
Control of
Chip Refining Systems", Pulp & Paper Canada, 94:6 (1993), pp. 43-47.
Generally,
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known TMP process control strategies work according to the hypothesis that
wood
chip qualities are stable. Any variation in chip quality will be considered as
disturbance in process control. In fact, chip quality changes quite rapidly,
and known
control strategies cannot efficiently eliminate its influence, which prompts
fluctuations
of the three operational variables of the refining process mentioned above.
Wood
species variation is an important factor that can negatively impact pulp
quality. The
lack of wood chip quality data can cause operators to conclude that the
problem is
caused by refiner plate wear.
A system for measuring optical reflection characteristics of chips such as
brightness, along with other important chip properties, such as moisture
content,
which is commercially known as the Chip Management System (CMS), is described
in U.S. Patent no. 6,175,092 B1 issued to the present assignee, and in U.S
published
Patent application no. US 20050027482. Some pulp mills have used such system
to
manage their chip piles according to chip quality, as discussed by Ding et al.
in
"Economizing the Bleaching Agent Consumption by Controlling Wood Chip
Brightness" Control System 2002 Proceedings, June 3-5, 2002, Stockholm,
Sweden,
pp. 205-209. Chip quality assessment can be defined as the synthesis of
measurements made of chip physical characteristics, as explained by Ding et
al. in
"Effects of Some Wood Chip Properties on Pulp Qualities" 89t" Pulp and Paper
Annual Conference Proceedings, January 29, 2003, p. 35. Ultimately, this
definition
depends on the importance of each chip characteristic for a given process.
Sound
chip pile management can help a mill to stabilize the input fed into refiners,
as
explained by Ding et al. in " Wood Chip Physical Quality Definition and
Measurement", 2003 International Mechanical Pulping Conference, Quebec City,
Canada, June 2-5 2003, pp. 367-373.
In pulp mills, visual evaluation of wood chip quality is widely used. From the
chip color, a specialist can determine the chip species and estimate
freshness, bark,
rot, and knot contents. A known approach consists of sorting trees according
to their
species or blend of species prior to wood chips manufacturing, to produce
corresponding batches of wood chips presenting desired characteristics
associated
with these species. Typically, hardwood trees such as poplar, birch and maple
are
known to generally produce pale wood chips while conifers such as pine, fir
and
spruce are known to generally yield darker wood chips. In practice, wood chips
batches can either be produced from trees of a same species or from a blend of
wood chips made from trees of plural species, preferably of a common category,
i.e.,
hardwood trees or conifers, to seek wood chips uniformity.
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Many studies have shown that wood species is the dominant factor in pulping
performance and pulp quality. The spruce family is the most favorable species
for
TMP as mentioned by Varhimo, A. et al, in "Raw Materials" in Sundbolm, J.
"Mechanical Pulping" Chapter 5, Fapet OY, 66-104 (1999). Although chip aging
can
S be observed from chip brightness, it is only useful for substantially
unvaried wood
species. When an unknown proportion of wood species is present, more
information
is needed to provide reliable chip quality assessment. For the purpose of wood
species identification, some optical testing methods are proposed by Sum, S.
T. et al.
in "Laser-excited Fluorescence Spectra of Eastern SPF Wood Species - An
Optical
Technique for Identification and Separation of Wood species", hVood Sci.
Technol.,
25, 1991, pp. 405-413., and by Lawrence, A. H. in "Rapid Characterization of
Wood
Species by Ion Mobility Spectrometry", Journal of Pulp and Paper Science, 15
(5),
1989, J196-J199, and a chemical vapor analysis is proposed by Fuhr, B. J. IN
"On-
line Wood Species Sensor", Paper Age, Sept.-Oct. 2001, pp. 26-29. These known
methods have been applied either off-line in laboratory or on-line for
monitoring a
specific wood species. However, these techniques cannot be used to evaluate a
mixture involving more than two wood species. An on-line measurement system
such
as described in U.S published Patent application no. US 20050027482 and
referred
to by Ding et al. in "Effects of Some Wood Chip Properties on Pulp Qualities"
8gtn
Pulp and Paper Annual Conference Proceedings, January 29, 2003, p. 35, can
produce data that is useful for identifying the proportion of pure wood
species making
up a mixture of wood chips, on the basis of optical reflection and moisture
measurements made on wood chips. For example, the brightness of Balsam Fir is
quite similar to that of Black Spruce, but Fir's moisture content is about
55%, while
Spruce moisture content is about 40%. Likewise, although Jack Pine's moisture
content is similar to that of Black Spruce, Pine is the darker species of the
two. For a
mixture of more than two species, it is possible to estimate a breakdown of
the
species present. U.S published Patent application no. US 20050027482 teaches
the
use of an estimation model based on a feed-forward neural network that is
built from
optical reflection-based measurements, namely R,G,B,H,S,L, and dark chip
content
(D), along with moisture measurement as input variables, in which chip
freshness
(ageing) and species are controlled, and the selection of the input variables
for the
FFNN has been performed using known Principal Component Analysis (PCA)
technique from the trials results. The well known Levenberg-Marquardt
algorithm has
been used to train the model, to provide at an output thereof an indication of
wood
species composition, usually representing the purity level of a main species
forming a
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chip sample. However, it has been observed that such approach provides an
estimation of the proportion of each species within a range of only about
t10%,
which is generally insufficient to allow an efficient control over species
variation in
wood chips fed to the pulping process.
In a typical chemical kraft mill, the cooking process can be either batch or
continuous. The wood chips are digested (cooked) at elevated temperature
(about
165 °C) and pressure in "white liquor', which is a mix of sodium
sulphide (Na2S) and
sodium hydroxide (NaOH). The white liquor chemically dissolves the lignin that
binds
cellulose fibres together. Cooking continues until a targeted H-factor is
reached. The
cooking time may range from 45 to 60 minutes depending on targeted pulp grade.
As the main raw material, wood chips are the largest cost factor in the kraft
pulping process. Fewer chips are currently available on the market and it is
forecasted that this reduction will accelerate over the next few years. In the
kraft
process, chip physical characteristics such as: species, density, freshness,
moisture
and bark contents, and dry mass have a direct impact on pulp quality and
yield. It is
known to take into account off line chip characteristics for kraft pulping
process
control according to strategies based on the assumption that wood
characteristics
are constant. Kappa number control strategies focus on H-factor control; these
strategies attempt ~to model the relationship between kappa number, H-factor,
sulphidity and effective alkali, as discussed by Hatton J. V in "Development
of Yield
Prediction Equations in Kraft Pulping" Tappi Journal, 56 (1973) 7, 97-100".
Usually,
pulp quality control calculates H-factor sets individually for each digester
to give the
desired kappa number, as taught by Uusitalo P. et al in "Chemical Pulping.
Papermaking Science and Technology" Book 19, Fapet Oy, Helsinki, Finland,
1999,
A510p. Pulp yield, which is another important control variable related to chip
characteristics, can be expressed as the ratio of pulp oven-dry weight to pulp
obtained from the original wood weight. It can be measured in the mill's
laboratory or
estimated from the ratio of monthly wood chip tonnage to pulp tonnage. These
off
line measurements are neither representative nor accurate. Online direct pulp
yield
measurement is very difficult as stated by Hatton J. V in the above-cited
paper. For a
batch cooking process, Macl_eod et al proposed in "Basket Cases : Kraft Pulps
Inside Digesters", Tappi Journal 70(1987) 11, 47-53, a method that involves
suspending a basket which contains a known quantity of wood chips inside the
digester. This method is time consuming and requires well-trained operators
and
scientists, as well as ancillary equipment.
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Considering the foregoing, there is still a need for improved online chip
quality
measurement methods and systems that can more accurately estimate the
proportion of wood species present in wood chips, either in pure or mixed
state, at
the input of a TMP or chemical pulping process.
S Summary of the Invention
It is a main object of the present invention to provide improved methods and
apparatus for estimating and controlling relative proportion of wood chips
originating
from a plurality of sources characterized by various wood species, in a mass
of wood
chips to be fed to a process for producing pulp, which allow efficient
monitoring of the
variation in wood species composition characterizing the wood chips to be
processed,
for the purpose of stabilizing chip feeding control and optimizing process
parameter
adjustment.
According to the above-mentioned main object, from a broad aspect of the
present invention, there is provided a method for estimating relative
proportion of
wood chips originating from a plurality of sources of wood chips, in a mass of
wood
chips to be fed to a process for producing pulp, the wood chips of each source
being
characterized by one of a pure wood species and a mixture of wood species. The
method comprises the steps of: i) estimating a set of wood chip properties
characterizing the wood chips of said mass to generate corresponding wood chip
properties data, said set including at least one light reflection-related
property and at
least one density-related property; and ii) feeding the wood chip properties
data at
corresponding inputs of a model characterizing a relation between said wood
chip
properties and said one of a pure species and a mixture of wood species wood
chips
for each source, to obtain an estimation of the wood chips relative
proportion.
According to the same main object, from another broad aspect, there is
provided a method for estimating relative proportion of wood chips originating
from a
plurality sources of wood chips, in a mass of wood chips to be fed to a
process for
producing pulp, the wood chips of each source being characterized by one of a
pure
wood species and a mixture of wood species. The method comprises the steps of:
i)
estimating a set of wood chip properties characterizing the wood chips of said
mass
to generate corresponding wood chip properties data, at least a portion of
which is
obtained by measuring at least one light reflection-related property and at
least one
density-related property; and ii) feeding the wood chip properties data at
corresponding inputs of a model characterizing a relation between the wood
chip
properties and said one of a pure species and a mixture of wood species wood
chips
for each source, to obtain an estimation of the wood chips relative
proportion.
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According to the same main object, from a further broad aspect, there is
provided an apparatus for estimating relative proportion of wood chips
originating
from a plurality of sources of wood chips, in a mass of wood chips to be fed
to a
process for producing pulp, the wood chips of each said source being
characterized
by one of a pure wood species and a mixture of wood species. The apparatus
comprises illumination means for directing light onto an area of wood chips
included
in the mass of wood chips, the illuminated wood chips presenting light
reflection
characteristics being substantially representative of the wood chips of the
mass, and
an optical imaging device for sensing light reflected from the illuminated
wood chips
to produce image data representing at least one light reflection-related
property
characterizing the wood chips of the mass. The apparatus further comprises a
density measuring unit for generating data representing at least one density-
related
property characterizing the wood chip of the mass, and a computer programmed
with
a model characterizing a relation between said wood chip properties and said
one of
a pure species and a mixture of wood species wood chips for each source, the
computer processing all the data with the model to obtain an estimation of the
wood
chips relative proportion.
According to the same main object, from another broad aspect, there is
provided a method for controlling relative proportion of wood chips
originating from a
plurality of sources of wood chips discharging to form a mass of wood chips to
be fed
to a process for producing pulp, the wood chips of each said source being
characterized by one of a pure wood species and a mixture of wood species. The
method comprises the steps of: i) estimating a set of wood chip properties
characterizing the wood chips of said mass to generate corresponding wood chip
properties data, said set including at least one light reflection-related
property and at
least one density-related property; ii) feeding the wood chip properties data
at
corresponding inputs of a model characterizing a relation between the wood
chip
properties and said one of a pure species and a mixture of wood species wood
chips
for each source, to obtain estimation data representing the wood chips
relative
proportion; iii) comparing the estimation data with predetermined target data
to
produce error data; and iv) selectively modifying the discharge rate of one or
more of
the wood chip sources on the basis of the error data, to adjust the relative
proportion
of wood chips in the mass.
According to the same main object, from a further broad aspect, there is
provided a system for controlling relative proportion of wood chips
originating from a
plurality of sources of wood chips in communication with means for discharging
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thereof to form a mass of wood chips to be fed to a process for producing
pulp, the
wood chips of each source being characterized by one of a pure wood species
and a
mixture of wood species. The system comprises illumination means for directing
light
onto an area of wood chips included in the mass of wood chips, said
illuminated
wood chips presenting light reflection characteristics being substantially
representative of the wood chips of said mass, and an optical imaging device
for
sensing light reflected from the illuminated wood chips to produce image data
representing at least one light reflection-related property characterizing the
wood
chips of the mass. The system further comprises a density measuring unit for
generating data representing at least one density-related property
characterizing the
wood chip of the mass and a computer programmed with a model characterizing a
relation between the wood chip properties and said one of a pure species and a
mixture of wood species wood chips for each said source, said computer
processing
all the data with said model to obtain estimation data representing the wood
chips
relative proportion, said computer being further programmed to compare the
estimation data with predetermined target data to produce error data. The
system
further comprises a controller operatively connected to the discharging means
for
selectively modifying the discharge rate of one or more of the wood chip
sources on
the basis of the error data, to adjust the relative proportion of wood chips
in the mass.
Brief description of the drawings
Preferred embodiments of the present invention will now be described in
detail with reference to the accompanying drawings in which:
Fig. 1 is a schematic diagram of a system using a computer unit for
controlling relative proportion of wood chips originating from a plurality of
sources
from which a mass of wood chips is formed and conveyed toward the primary
refiner
or digester used by the pulping process;
Fig. 2 is a partially cross-sectional end view of a main discharging screw
device feeding a conveyor transporting the wood chips through the optical,
moisture
and volume measurement station that can be used to perform the wood species
proportion estimation method of the invention;
Fig. 3 is a partially cross-sectional side view along section line 3-3 of the
measurement station shown on Fig. 2 and being connected to the computer unit
of
Fig. 1 shown here in a detailed block diagram;
Fig. 4 is a partial cross-sectional end view along section line 4-4 of Fig. 3,
showing the internal components of the measurement station;
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Fig. 5 is a graph showing a set of curves representing general relations
between measured optical characteristics and dark wood chips content
associated
with several samples;
Fig. 6 is a bar graph showing the results of online measurement of the mass
of wood chips fed to the measurement station;
Fig. T is a PCA-X loading scatter plot of test results of chip species effect
analysis;
Fig. 8 is a graph presenting the results of a validation of online moisture
content measurement;
Fig. 9 shows curves representing an exemplary set of rules to be
implemented in a fuzzy logic model used to perform wood species proportion
estimation according to the invention;
Fig. 10 is a schematic diagram showing a neural network structure that can
be used to generate the set of rules as shown in Fig 9;
Fig. 11 is a graph showing variation of chip volume and dry mass produced
by chip level control for a batch process digester wherein chip level
measurement is
used to control feeding volume;
Fig. 12 is a graph showing variation of chip volume and dry mass produced
by chip level control for a batch process digester wherein estimated dry mass
is used
to control feeding volume;
Fig. 13 is a graph showing variation of wood species mixtures for different
batches of wood chips fed to a digester;
Fig. 14 is a graph showing variation of density for different batches of wood
chips fed to the digester;
Fig. 15 is a graph showing variation of luminance and moisture for different
batches of wood chips fed to the digester;
Fig. 16 is a graph showing variation of bark content for different batches of
wood chips fed to the digester;
Fig. 1 T is a graph showing variables and PCA model goodness of fit and
prediction for the digester;
Fig. 18 is a graph showing variable importance in the model;
Fig. 19 is a graph showing coefficients used in the model; and
Fig. 20 is a graph comparing laboratory pulp yield to the prediction of the
model.
Detailed description of the preferred embodiments
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After further weighing the importance of many chip physical characteristics,
it
has been found that while chip density is an important parameter closely
associated
with species, that variable was lacking in prior art modeling for the purpose
of wood
species monitoring. Basically, according to the present invention, It has been
discovered that a relation between density data along with one or more light
reflection-related properties in one hand, and wood species characterizing the
wood
chips coming from a plurality of sources in the other hand, may be implemented
in a
model, which can then be used to estimate relative proportion of wood chip
species
in a mass of wood chips obtained from said sources. It has been found that
such
estimation may be advantageously used for monitoring variation of wood species
composition, so as to allow selective discharge adjustment of the chip sources
in
order to stabilize wood species composition to be fed to a TMP or chemical
pulping
process.
Referring now to Fig. 1, there is generally represented at 1 a system for
controlling relative proportion of wood chips originating from a plurality of
sources of
wood chips numbered 1 to n (n=3 in the example shown), usually in the form of
piles
of raw wood chips 4, in communication with means for discharging such as screw
devices 3, the output of which being received and transported by a main
discharging
screw device represented by a series of an-ows 5 on Fig. 1, which screw device
will
be described in detail with reference to Fig.2. The main screw discharges the
wood
chips as indicated by arrow 5' to form a mass of blended wood chips 6 to be
fed to a
process for producing pulp, which typically makes use of a primary refiner in
the case
of a TMP process, or of a cooking digester in the case of a chemical process
such as
kraft. As will be explained below in detail, the wood chips 4 of each pile may
be
characterized by either a substantially pure wood species or a mixture of wood
species of variable quality, depending upon available chips from providers.
The
system 1 includes a measurement station generally designated at 12 including
an
optical scanning unit 7 integrating illumination means for directing light
onto a
scanned area 8 of wood chips 6, and an optical imaging device for sensing
light
reflected from the illuminated wood chips, to produce through output line 9
image
data representing at least one light reflection-related property
characterizing the
wood chips 6. Although only wood chips forming the top surface of the mass of
wood
chips 6 are illuminated and sensed, the scanning mode of operation of unit 7
ensures
that these illuminated wood chips present light reflection characteristics
substantially
representative of all wood chips 6. The measurement station further includes a
density measuring unit preferably making use of a weighing unit generally
designated
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at 10 for measuring weight of at feast a representative portion of the wood
chips 6,
and of a volume meter 11 for measuring volume of the same portion of wood
chips.
The weighing device 10 preferably makes use of a plurality of weight sensors
such
as load cells 40 transversely mounted in pairs along wood chip conveyer 15 and
5 mechanically coupled to the endless belt 13 thereof to be responsive to the
weight of
wood chips transported by conveyer 15. The weight signals generated by load
cells
40 through respective output lines 41 are combined by a weighing acquisition
module
42 that produce resulting calibrated and balanced weight data. A weighing
device
such as Z-Block from BLH Electronics Inc. Canton, MA, can be used. A load cell
is a
10 transducer that converts force into a measurable electrical output. Each
load cell
included bonded strain gauges, which are positioned so as to measure applied
shear
stresses. The strain gauges are wired to a Wheatstone bridge circuit which,
when
crossed with an excitation voltage, produces changes in the electrical output
that are
proportional to the applied force. Thanks to low deflection, low mass design
and the
absence of moving parts, such load cells afford excellent high frequency
response for
dynamic force measurement. Three measurements must be considered for online
chip weighing, namely: wood chip weight, speed of belt 13 through line 19' and
position of main discharging screw device 17 through line 39'. A check was
performed on the precision of the load cells 40. While the conveyer was
running, a
standard 25-kilogram weight was placed on each load cell 40. The results are
shown
in Table 1
Test NO. wStandard ~kg) WMeasurement
~k~
Maximum Minimum
1 0 0 -0.2
2 25 24.9 25.1
3 50 49.8 50.2
4 75 74.9 75.1
5 100 99.7 100.2
6 125 124.7 125.5
7 150 149.2 150.0
8 175 174.5 175.2
9 200 199.8 200.2
TABLE 1
It is to be understood that any other suitable weighing device based on a
different
weight measurement principle may be used.
The volume meter 11 is preferably based on an optical ranging sensor
measuring the distance separating the sensor reference plane and a scanned
point
26 of the top surface of the mass of wood chips 6, from which the volume can
be
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11
derived, knowing the distance separating the sensor reference plane and the
surface
of conveyer belt 13, and also knowing width thereof. On the conveyer, chip
morphology or profile can be assumed to be constant due to the use of a proper
screw spillway design, thus making it possible to infer chip volume on the
basis of the
bed height measurement. An infrared analog distance sensor such as model SA1 D
from IDEC Corporation, Sunnyvale, CA, can be used. It is to be understood that
any
other suitable distance ranging device based on a different measurement
principle, or
any other sensor adapted to direct volume measurement, may be used. Weight and
volume measurement data generated through output lines 43 and 44 respectively,
are used to derive data representing at least one density-related property
characterizing the mass of wood chip 6, and more specifically bulk density, as
will be
explained later in more detail. The system 1 further includes a computer unit
25
whose data processor is programmed with a model characterizing a relation
between
the wood chip properties and the wood species characteristics of the wood
chips 4 of
each source or pile 1 to n. The computer unit 25 is further programmed to
process
output data from measurement station 12 with the model to obtain estimation
data
representing the wood chips relative proportion. Conveniently, the data
processor of
computer unit 25 is used to derive the data representing density-related
property data
on the basis of weight and volume measurement data received from weighing
device
10 and volume meter 11. The computer unit 25 is also programmed to compare the
estimation data with predetermined target data to produce error data through
control
output line 45, which data indicate variation in the wood species composition
of the
wood chips to be processed. The system 1 further includes a controller unit 33
operatively connected to the drive motor (not shown) provided on each
discharging
screw device 3 through control lines 35 for selectively modifying the
discharge rate of
one or more of wood chip sources or piles 1 to n , on the basis of the error
data
received from computer unit 25, to adjust the relative proportion of wood
chips
species in the mass of wood chips 6 to be processed. The controller unit 33 is
also
connected to the drive motor of the main discharging screw device through
further
control line 35', as will be explained below with reference to Figs. 2 and 3.
To obtain
better control accuracy over the discharge adjustment, a volumetric sensor 37
is
coupled to each screw device 3 to provide through feedback lines 39 a signal
indicating of the effective discharge rate as a result of commands received
from
controller 33. A similar sensor 37' is coupled to the main discharging screw
device to
provide feedback signal to controller 33 through line 39'. Conveniently, a
conventional encoder mechanically or optically coupled to the driving shaft of
each
CA 02549576 2006-06-05
12
screw device can be used as volumetric sensor. In order to provide a more
accurate
estimation, the set of wood chip properties considered by the model further
includes
moisture content, which property is preferably measured by a moisture sensor
47
provided on the measurement station 12, producing through output line 49 data
representative of the moisture content of the wood chip 6, which data is
processed by
computer unit 25 with the model to obtain the estimation of wood chips
relative
proportion on the basis of species composition. Furthermore, the moisture
measurement can be also used to derive an estimation of basic density that may
be
advantageously used as a further input to the model, as will be later
explained in
more detail.
As to the weighing function of the system, the disturbance due to the fact
that
wood chips are falling on the conveyer belt 13 under gravity will now be
defined and
analysed. As shown on Fig. 2, wood chips 6 fall from a given height of
typically
about one meter onto belt 13 of conveyer 15. The chip's gravitational
potential energy
is equal to its weight times the falling distance. It is desirable to model
this gravity
force in order to make an assessment of a possible source of measurement
error.
For a given period of time, the chips fall on an area covering about 0.31 X
1.5 m2 in the
present example. Supposing that the average wood chip thickness is 5 mm,
fallen
chip volume is about:
V = 0.31 x1.5x0.005 = 2.325x10-3 (m3~ (1)
Assuming an average basic density p of wood chip is 450 kg/m3, the fallen chip
mass
is:
m =pxV = 450x2.325x10-3 = 1.04625 (kg) (2)
the chip's gravitational potential energy is:
E~ = mxgxh = 1.04625x9.81 x1 = 10.26 (N.m) (3)
wherein:
g = acceleration of gravity = 9.81 (m/s2~
h = chip falling height (m)
The idler reaction work is:
W=FxL (4)
Wherein:
F = idler reaction force (N),
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13
L = conveyer length (m).
According to the energy conservation law, the chip's gravitational potential
energy
equals the idler reaction work (E~=W). Thus, by transferring values between
equations (3) and (4):
F = E~/L = 10.2637/17 = 0.60 N = 61.18 (g ) (5)
Taking into account equation (5), the chip gravity force equals idler reaction
force F,
and is equivalent to 61.5 (g). In practice, this force generally does not
really influence
measurement accuracy, as the typical analog/digital resolution of
instrumentation
used is about 9 (g) and its probable analog/digital system absolute error is
300 (g).
A method used by the weighing unit and computer to derive wood chips mass
and density measurements will now be explained in view of the following
parameters
and corresponding definitions:
Wet Chip Mass Modified: mm=rn~+Cg hL" (kg)
Chip Unit Length Mass: rru=ml-"'"' (kg/m)
Belt Feed Forward Length: lf=vnxt (m)
Chip Fall Mass: md=mrxlf (kg)
Chip Flow Profile: As=ZpX~hCMS-hc~CCpc (m2)
Fall Volume: Vd=lfxA.s (m3)
Fall Bulk Density: peurk-d= y~ XCbulk (kg/m3)
Fall Basic Density: pn~r~ a= Y~ d xCa~sa (kg/m3)
Dry chip mass: md,~_d=md~l-H,"
Measured parameters are:
Belt speed: vb (m/s)
Chip Covered Length on Belt: h (m)
Wet Chip Mass Measured: m~ (kg)
Global Moisture Content: Hm (%)
Height of CMS to Chip Bed: h~ (m)
Exemplary chip feeding configuration parameter values are:
CA 02549576 2006-06-05
14
Chip Passage Width: IP = 0.31 (m)
Height of CMS to Belt: hcnns = 0.18 (m)
Chip Fall Height: hfan = 1 (m)
Gravity Acceleration: g = 9.81 (m/s2)
Conveyer Length L = 16.7 (m)
Coefficients and exemplary set values are:
Chip Nominal Mass that Hits the Belt: C9 = 0
Chip Flow Profile Correction Coefficient: CP~ = 1
Chip Bulk Density Correction Coefficient: Cbulk = 1
Chip Basic Density Correction Coefficient: Cbasic = 1
For an online chip weigh measurement, the desired outputs are chip moisture
content or weight, dry weight, bulk density and basic density. Online chip
volume
data being required to calculate chip densities, a distance sensor is used to
measure
chip bed height as mentioned before. Chip dry mass and bulk and basic density
can
be calculated by using the factors of chip moisture content, chip volume and
the
online chip wet mass measurement. For the purpose of experimentation,
oversized
and undersized chips were screened out before entering the conveyor, thus
making it
possible to establish a solid correlation between basic density and bulk
density.
Assuming that load cell sampling frequency is 1/t, where t is a time interval
between two samples. Belt speed is v, and the mass of chips covering the
length of
the conveyor is I, a variable that will depend on the position of the chip
unloading
screw. For a given time, k, the chip mass falling onto the belt can be
calculated as:
ma~k~m k xv
For a given start time to to end time teed, the total chip mass measured can
be
expressed as:
fend
mc~w~=~ma~k~ where: k=to, t,, .., te~d (7)
k=ro
However, the wood chip mass being generally not homogeneously distributed over
the belt, an error will appear in the equation (7). This error can be
eliminated if the
conveyer 15 is empty at the start of sampling time to, and the main
discharging screw
CA 02549576 2006-06-05
device 17 is stopped at end of sampling time te~d. The measurement will be
halted
once and there are no longer any chips on the conveyer. As mentioned above,
important variables for evaluating chip basic density and wood chip species
variation
are the values derived from chip wet mass and dry mass measurement. With the
5 measurement station used in the example described above, the accuracy of
load
cells is better than t0.5%. Test results are shown on Fig. 6. A validation
test was
performed in a TMP mill, in which, for a given volume of dry chips
corresponding to
299.4 (t), the measurement station used gave a figure of approximately 290.3
(t), a
result which reflects the fact that some lost, unrecoverable chips were not
accounted
10 for during the feeding stage.
The measurement station 12 is preferably based on the wood chip optical
inspection apparatus known as CMS-100 chip management system commercially
available from the Assignee Centre de Recherche Industrielle du Quebec (Ste-
Foy,
Quebec, Canada), which has the capability to measure light reflection-related
15 properties, as well as volume and moisture content data. Such wood chip
inspection
apparatus is basically described in U.S. Patent no. 6,175,092 B1 issued on
January
16, 2001 to the present assignee, and will be now described in more detail in
the
context of the estimation of wood species proportion in wood chips according
to the
present invention.
Referring now to Fig.2, the measurement station 12 shown is capable of
generating color image pixel data through an optical inspection technique
whereby
polychromatic light is directed onto an inspected area of the wood chips,
followed by
sensing light reflected from the inspected area to generate the color image
pixel data
representing values of color components within one or more color spaces (RGB,
HSL)
for pixels forming an image of the inspected area. The measurement station 12
comprises an enclosure 14 through which extends a powered conveyor 15 coupled
to a drive motor 18. The conveyor 15 is preferably of a trough type having
belt 13
defining a pair of opposed lateral extensible guards 16, 16' of a known
design, for
keeping the wood chips to be inspected on the conveyor 15. In the embodiment
shown on Fig. 2, only respective outlets 21 of screw devices 3 in
communication with
a main discharging screw device 17 are shown. It can be seen that the main
discharging screw device 21 is adapted to receive through outlets 21 wood
chips to
be blended from corresponding wood chips sources. It is to be understood that
the
term "wood chips" is intended in the present specification to include other
similar
wooden materials for use as raw material for a particular pulp and paper
process,
and that could be advantageously subjected to the methods in accordance with
the
CA 02549576 2006-06-05
16
present invention, such as flakes, shavings, slivers, splinters and shredded
wood.
The main screw device 17 has an elongated cylindrical sleeve 27 of a circular
cross-
section adapted to receive for rotation therein a feeding screw 28 of a known
construction. The sleeve 27 has lateral input openings in communication with
outlet
21 allowing wood chips to reach an input portion of the screw 28. The sleeve
27
further has an output 31 generally disposed over an input end of conveyer 15
to allow
substantially uniform discharge of the wood chips 6 on the conveyer belt 13.
The
feeding screw 28 has a base disk 30 being coupled to the driven end of a
driving
shaft 32 extending from a drive motor 34 mounted on a support frame (not
shown),
which motor 34 imparts rotation to the screw 28 at a speed (RPM) in accordance
with
the value of the control signal coming from controller unit 33 through line
35', in order
to modify the discharge rate of screw 28 to a desired target value. The
driving control
of screw devices 3 is performed in a similar way.
Turning now to Figs. 3 and 4, internal components of the measurement
station 12 and particularly of the optical scanning unit 7 as shown on Fig. 1
will be
now described. The enclosure 14 is formed of a lower part 56 for containing
the
conveyor 15 and being rigidly secured to a base 58 with bolt assemblies 57,
and an
upper part 60 for containing the optical components of the station 12 and
being
removably disposed on supporting flanges 62 rigidly secured to upper edge of
the
lower part 56 with bolted profile assemblies 64. A2t the folded ends of a pair
of
opposed inwardly extending flanged portions 66 and 66' of the upper part are
secured through bolts 68 and 68' side walls 70 and 70' of a shield 72 further
having
top 74, front wall 76 and rear wall 76' to optically isolate the field of view
80 of a
camera 82 for optically covering superficial wood chips 6' that are disposed
within
scanned area 8 as shown in Figs. 1 and 4, these supertcial wood chips 6' being
considered as representative of the characteristics of substantially all wood
chips 6.
The camera 82 is located over the shield 72 and has an objective downwardly
extending through an opening 84 provided on the shield top 74, as better shown
on
Fig. 3. Ideally, the distance separating camera objective 83 and supe~cial
wood
chips 6' should be kept substantially constant by controlling the input flow
of matter,
in order to prevent scale variations that could adversely affect the optical
properties
measurements. However, the selective discharge adjustment that can be applied
to
one or more of wood chips sources 1 to n according to the wood species
proportion
controlling method of the invention does not generally allow a constant input
flow
through the measurement station 12. Therefore, the camera 82 is preferably
provided
with an auto-focus feature as well known in the art, and with a distance
measuring
CA 02549576 2006-06-05
17
feature to normalize the captured image data to compensate variation in the
inspected area due to variation of the distance separating the camera
reference
plane and the superficial wood chips 6' within scanned area 8 as shown in
Figs. 1
and 4. The camera 82 is used to sense light reflected on superficial wood
chips 6' to
produce electrical signals representing reflection intensity values. A 2D CCD
matrix,
color RGB-HSL video camera such as Hitachi model no. HVC20 is used to generate
the color pixel data as main optical properties considered by the method of
the
invention. While a 2D matrix camera is advantageously used to cover a 2D
scanning
area 8, it is to be understood that a suitable linear camera can alternatively
be used
by adapting the measurement station according to corresponding scanning
parameters. Turning again to Fig. 4, diagonally disposed within shield 72 is a
transparent glass sheet acting as a support for a calibrating reference
support 88;
whose function will be explained later in more detail. As shown on Fig. 3, the
camera
82 is secured according to an appropriate vertical alignment on a central
transverse
member 90 supported at opposed end thereof to a pair of opposed vertical frame
members 92 and 92' secured at lower ends thereof on flanged portions 66 and
66' as
shown on Fig. 4. Also supported on the vertical frame members 92 and 92' are
front
and rear transverse members 94 and 94'. Transverse members 90, 94 and 94' are
adapted to receive elongate electrical light units 96 used as illumination
means,
including standard fluorescent tubes 98 in the example shown, to direct light
substantially evenly onto the inspected batch portion of superficial wood
chips 6'. The
camera 82 and light units 96 are powered via a dual output electrical power
supply
unit 98. Electrical image data are generated by the camera 82 through output
line 7.
The camera 82 is used to sense light reflected on superficial chips 6' to
generate
color image pixel data representing values of color components within RGB
color
space, for pixels forming an image of the inspected area, which color
components
are preferably transformed into color components within standard LHS color
space,
as will be explained later in more detail. When used in cold environment, the
enclosure 14 is preferably provided with a heating unit (not shown) to
maintain the
inner temperature at a level ensuring normal operation of the camera 82. The
apparatus 10 may be also provided with air condition sensors for measuring air
temperature, velocity, relative humidity, which measurement may be used to
stabilize
operation of the measurement station.
Referring to Fig. 3, a moisture sensor 47 is shown which is preferably part of
the measurement station 12. The sensor 47 is used measure variations in the
chip
surface moisture content. As will be explained later in detail, the chip
moisture
CA 02549576 2006-06-05
18
content that can be derived from such measurement is an important property
that
may be advantageously considered as an input variable of the model, and that
can
be used to derive basic density of wood chips from bulk density measurement.
The
moisture sensor 47 is preferably a non-contact sensing device such as near-
infrared
sensor MM710 supplied by NDC infrared Engineering, irwindale CA. The sensor 47
generates at an output 79 thereof electrical signals representing mean surface
moisture values for the superticial wood chips 6'.
Control and processing elements of the measurement station 12 will be now
described with reference to Fig. 3. The computer unit 25 used as a data
processor,
which has an image acquisition module 104 coupled to line 7 for receiving
color
image pixel signals from camera 82, which module 104 could be any image data
acquisition electronic board having capability to receive and process standard
image
signals such as model Meteor-2TM from Matrox Electronic Systems Ltd (Canada)
or
an other equivalent image data acquisition board currently available in the
marketplace. The computer 25 is provided with an external communication unit
103
being coupled for bi-directional communication through lines 106 and 106' to
controller unit 33, which is a conventional programmable logic controller
(PLC)
programmed for controlling operation of each discharge screw device 3 through
control line 35' and feedback line 39', as well as conveyor drive 18 through
line 19
and feedback line 19' coupled to the drive mechanism of the conveyer 15 to
provide
a signal indicating of the effective conveyer belt speed. The PLC 33 may
receive
from line 112 wood chips source data entered via an input device 114 by an
operator
in charge of raw wood chips management operations, such as wood chips species
information. The input device 114 is connected through a further line 116 to
an image
processing and communication software module 118 outputting control data for
PLC
through line 119 while receiving acquired image data and PLC data through
lines 120
and 122, respectively. The image processing and communication module 118
receives input data from a computer data input device 124, such as a computer
keyboard, through an operator interface software module 126 and lines 128 and
130,
while generating image output data toward a display device 132 through
operator
interface module 126 and lines 134 and 136. Module 118 also receives the
moisture
indicating electrical signals through a line 49.
Turning now to Fig.5 general relations between measured optical
characteristics and dark wood chips content associated with several samples
are
illustrated by the curves traced on the graph shown, whose first axis 138
represents
dark chips content by weight percentage characterizing the sample, and whose
CA 02549576 2006-06-05
19
second axis 140 represents corresponding optical response index measured. In
the
example shown, four curves 142, 144, 146, and 148 have been fitted on the
basis of
average optical response measurements for four (4) groups of wood chips
samples
prepared to respectively present four (4) distinct dark chips contents by
weight
percentage, namely 0 % (reference group), 5%, 10% and 20%. Measurements were
made using a RGB color camera coupled to an image acquisition module connected
with a computer, as described before. To obtain curves 142 and 146, luminance
signal values derived from the RGB signals corresponding to all considered
pixels
were used to derive an optical response index which is indicative of the
relative
optical reflection characteristic of each sample. As to curve 142, mean
optical
response index was obtained according to the following ratio:
I=LR-1 (8)
s
Wherein I is the optical response index, LR is a mean luminance value
associated
with the reference samples and LS is a mean luminance value based on all
considered pixels associated with a given sample. Curve 146 was obtained
through
computer image processing to attenuate chip border shaded area which may not
be
representative of actual optical characteristics of the whole chip surface. To
obtain
curves 144 and 148, reflection intensity of red component of RGB signal was
compared to a predetermined threshold to derive a chip darkness index
according
the following relation:
D=~ (9)
Wherein D is the chip darkness index, PD is the number of pixels whose
associated
red component intensity is found to be lower than the predetermined threshold
ratio
(therefore indicating a dark pixel) and PT is the total number of pixels
considered. As
for curve 146, curve 148 was obtained through computer image processing to
attenuate chip border shaded areas. It can be seen from all curves 142, 144,
146,
and 148 that the chip darkness index grows as dark chip content increases.
Although
curve 148 shows the best linear relationship, experience has shown that all of
the
above described calculation methods for the optical response index can be
applied,
provided reference reflection intensity data are properly determined, as will
be
explained later in more detail.
Returning now to Figs. 2, 3 and 5, a preferred operation mode of the chip
optical properties inspecting function of the measurement station 12 will be
now
CA 02549576 2006-06-05
explained. Referring to Fig. 3, before starting operation, the station 12 must
be
initialized through the operator interface module 126 by firstly setting
system
configuration. Camera related parameters can be then set through the image
processing and communication module 118, according to the camera
specifications.
5 The initialization is completed by camera and image processing calibration
through
the operator interface module 126.
System configuration provides initialization of parameters such as data
storage allocation, image data rates, communication between computer unit 25
and
PLC 33, data file management, and wood species information. As to data storage
10 allocation, images and related data can be selectively stored on a local
memory
support or any shared memory device available on a network to which the
computer
unit 25 is connected. Directory structure is provided for software modules and
system
status message file. Image rate data configuration allows to select total
number of
acquired images for each batch, number of images to be stored amongst the
15 acquired images and acquisition rate, i.e. period of time between
acquisition of two
successive images which is typically of about 5 sec. for a conveying velocity
of about
10 feet/min. Therefore, to limit computer memory requirements, while a high
number
of images can be acquired for statistical purposes, only a part of these
images need
to be stored, and most of images are deleted after a predetermined period of
time.
20 The PLC configuration relates to parameters governing communication
befinreen
computer unit 25 and PLC 33, such as master-stave protocol setting (ex. DDE),
memory addresses associated with «heart beat» for indication of system
interruption,
«heart beat» rate and wood chips presence monitoring rate. Data file
management
configuration relates to parameters regarding wood chips Input data,
statistical data
for inspected wood chips, data keeping period before deletion and data keeping
checking rate. Statistical data file can typically contain information
relating to source
or batch number, supplier contract number, wood species identification
(pure/mixture), mean intensity values for RGB signals, mean luminance L , mean
H
(hue) and mean S (saturation), darkness index D and date of acquisition. Data
being
systematically updated on a cumulative basis, the statistical data file can be
either
deleted or recorded as desired by the operator to allow acquisition of new
data.
Once the camera 82 is being configured as specified, calibration of the camera
and
the image processing module can be carried out by the operator through the
operator
intertace, to ensure substantially stable light reflection intensities
measurements as a
function of time even with undesired lightning variation due to temperature
variation
and/or light source aging, and to account for spatial irregularities inherent
to CCD's
CA 02549576 2006-06-05
21
forming the camera sensors. Calibration procedure first consists of acquiring
« dark
image signals while obstructing with a cap the objective of the camera 82 for
the
purpose of providing offset calibration (L=0), and acquiring « lighting »
image signals
with a gray target presenting uniform reflection characteristics being
disposed within
the inspecting area on the conveyer belt 13 for the purpose of providing
spatial
calibration. Calibration procedure then follows by acquiring image signals
with an
absolute reference color target, such as a color chart supplied by Macbeth
Inc., to
permanently obtain a same measured intensity for substantially identically
colored
wood chips, while providing appropriate RGB balance for reliable color
reproduction.
Initial calibration ends with acquiring image signals with a relative
reference color
target permanently disposed on the calibrating reference support 88, to
provide an
initial calibration setting which account for current optical condition under
which the
camera 82 is required to operate. Such initial calibration setting will be
used to
perform calibration update during operation, as will be later explained in
more detail.
Initialization procedure being completed, the measurement station 12 is ready
to operate, the computer unit 25 being in permanent communication with the PLC
33
to monitor the operation of screw drive 34 indicating discharge of wood chips
blend
from the sources. Whenever a new batch is detected, the following sequence of
steps are performed: 1 ) end of PLC monitoring; 2) source or batch data file
reading
(species of wood chips, source or batch identification number); 3) image
acquisition
and processing for wood species proportion estimation; and 4) data and image
recording after processing. Image acquisition consists in sensing light
reflected on
the superficial wood chips 6' included in a currently inspected batch portion
to
generate color image pixel data representing values of color components within
RGB
color space for pixels forming an image of the inspected area 8 defined by
camera
field of view 80. Although a single batch portion of supe~cial chips covered
by
camera field of view 80 may be considered to be representative of optical
characteristics of a substantially homogeneous batch, wood chips batches being
known to be generally heterogeneous, it is preferable to consider a plurality
of batch
portions by acquiring a plurality of corresponding image frames of electrical
pixel
signals. In that case, image acquisition step is repeatedly performed as the
superficial wood chips of batch portions are successively transported through
the
inspection area defined by the camera field of view 80. Calibration updating
of the
acquired pixel signals is performed considering pixel signals corresponding to
the
relative reference target as compared with the initial calibration setting, to
account for
any change affecting current optical condition. Superficial wood chips 6' are
also
CA 02549576 2006-06-05
22
scanned by infrared beam generated by the sensor 47, which analyzes reflected
radiation to generate the chip surface moisture indication signals. It is to
be
understood that while the moisture sensor 47 is disposed at the output of the
measurement station 12 in the illustrated embodiment, other locations
downstream
or upstream to the measurement station 12 may be suitable.
As to image processing, the image processing and communication unit 118 is
used to derive the luminance-related data, preferably by averaging luminance-
related
image pixel data as basically expressed as a standard function of RGB color
components as follows:
L=0.21258+0.71546+0.0721B (10)
.Values of H (hue) and S (saturation) are derived from RGB data according to
the
same well known standard, hue being a pure color measure, and saturation
indicating how much the color deviates from its pure form, whereby an
unsaturated
color is a shade of gray. As mentioned before, the unit 118 derives global
reflection
intensity data for the inspected batch portions designated before as optical
response
index with reference to Fig. 5, from the acquired image data. For example,
experience has shown that spruce and balsam fir are brighter than jack pine
and
hardwood, and chip ageing and bark content decrease chip brightness.
Calibration
updating of the acquired pixel signals is performed considering pixels signals
corresponding to the relative reference target as compared with the initial
calibration
setting, to account for any change affecting current optical condition. Then,
image
noise due to chip border shaded areas, snow and/or ice and visible belt areas
are
preferably filtered out of the image signals using known image processing
techniques.
From the signals generated by moisture sensor 47, the image processing and
communication unit 118 applies compensation to the acquired pixel signals
using the
corresponding moisture indicating electrical signals.
Global reflection intensity data may then be derived by averaging reflection
intensity values represented by either all or representative ones of the
acquired pixel
signals for the batch portions considered, to obtain mean reflection intensity
data.
Alternately, the global reflection intensity data may be derived by computing
a ratio
between the number of pixel signals representing reflection intensity values
above a
predetermined threshold value and the total number of pixel signals
considered. Any
other appropriate derivation method obvious to a person skilled in the art
could be
used to obtain the global reflection intensity data from the acquired signals.
CA 02549576 2006-06-05
23
Optionally, the global reflection intensity data may include standard
deviation data,
obtained through well known statistical methods, variation of which may be
monitored
to detect any abnormal heterogeneity associated with an inspected batch.
In operation, the computer unit 25 continuously sends a normal status signal
in the form of a «heart beat» to the PLC through line 106'. The computer unit
25 also
permanently monitors system operation in order to detect any software and/or
hardware based error that could arise to command inspection interruption
accordingly. The image processing and communication module 118 performs system
status monitoring functions such as automatic interruption conditions,
communication
with PLC, batch image data file management and monitoring status. These
functions
result in messages generation addressed to the operator through display 132
whenever appropriate action of the operator is required. For automatic
interruption
conditions, such a message may indicate that video (imaging) memory
initialization
failed, an illumination problem arose or a problem occurred with the camera 82
or the
acquisition card. For PLC communication, the message may indicate a failure to
establish communication with PLC 33, a faulty communication interruption,
communication of a «heart beat» to the PLC 33, starting or interruption of the
«heart
beat. As to batch data files management, the message may set forth that
acquisition
initialization failed, memory storing of image or data failed, a file transfer
error
occurred, monitoring of recording is being started or ended. Finally, general
operation
status information is given to the operator through messages indicating that
the
apparatus is ready to operate, acquisition has started, acquisition is in
progress and
image acquisition is completed.
Details regarding chip species variation analysis in relation with the present
invention will now be presented in view of experimental data. As mentioned
above,
the measurement station is able to perform online measurement of chip physical
properties, such as moisture content, darkness indication, H (Hue), S
(Saturation)
and L (Luminance), basic and bulk densities, dry and wet mass. Using such
station,
81 tests were performed in a TMP mill over a period of nine month from spring
to fall.
Each test took 30 minutes, during which 10 samples of one kilogram of wood
chips
were taken directly from the chip feeding conveyer 15 after measurements were
performed using the measurement station. The moisture content, bulk and basic
density of the 10 samples were measured and averaged in the laboratory. The
test
results have been analyzed using Principal Component Analysis (PCA) and a PCA-
X
loading scatter plot of these results are presented in Fig 7, in which SF
stands for a
Spruce and Balsam Fir mixture, HW for a Hardwood mixture and PH for a Pine and
CA 02549576 2006-06-05
24
Hemlock mixture. The percentage of each species in the mixtures of SF, HW and
PH
is unknown, but can be assumed to be stable. As shown on Fig. 7, for the first
component p[1], bulk density is directly proportional to L, moisture content
and SF,
while basic density is directly proportional to H, S, darkness and PH. For the
second
component p[2], the SF value is significantly inversely proportional to the HW
but
only slightly so in the case of PH and bulk and basic densities. Therefore, it
would
appear that wood species data is a critical factor in online measurements
performed.
Referring now to Fig. 8, the results of a validation of online moisture
content
measurement is presented, in which the wood species is a mixture of SF, PH and
HW, and their respective proportions are unknown. The test period extended
over six
month from spring to fall, and measurement accuracy was estimated within about
~1 %.
Details concerning wood species modelling in relation with the present
invention will now be presented. Based on the PCA analysis results, a fuzzy
logic
model has been built in order to estimate the SF, HW and PH proportions in a
mixture. Fuzzy logic is a structured, model-free estimator that approximates a
function through linguistic input/output associations. As mentioned in the
previous
section, there are preferably 7 inputs (H, S, L, Moisture Content, Darkness,
Bulk and
Basic Density) and 3 outputs (SF%, PH% and HW%). Wood species estimation
model preferably includes n fuzzy logic models (3 in the presented example),
i.e.,
one model associated with each wood chip source, for estimating the percentage
of
the wood chips coming therefrom, either characterized by pure of mixed wood
species as mentioned before. The sum of the outputs of the fuzzy Logic model
equals 100%. The modelling procedure, that can be performed using any suitable
commercially available fuzzy logic software tool such as provided by
MathIabTM,
typically involves the following steps:
1. Fuzzifying inputs: determining the degree to which they belong to each of
the
appropriate fuzzy sets (the inputs) via the membership functions relating to
Gaussian distribution curves;
2. Inference: using the Takagi-Sugeno-Kang method as an inference motor and a
combination of back propagation and least squares in order to gather data from
the mill's test results and thus be able to compute the membership function
parameters. These parameters are best suited to enable the associated fuzzy
inference motor to track the given input and output data.
3. Defuzzification method: weighted average.
CA 02549576 2006-06-05
As an example, a fuzzy logic model having 4 rules defined by the graphs
associated with each of the 7 inputs as shown on Fig. 9, which rules are
generated
using a neural network structure as shown on Fig. 10, can be used. Using such
fuzzy
logic model, 15 tests were performed in a TMP mill. The prediction results
obtained
5 compared with laboratory trials are shown in Table 2.
No Date SF% PH% HW%
Lab. Model Lab. Model Lab. Model
1 22/03/04 80 81 5 4 15 15
2 26/03/04 80 74 5 9 15 17
3 02/04/04 85 79 5 6 15 15
4 20/04/04 85 80 5 7 15 15
5 05/05/04 TO 51 10 20 20 29
6 09/06/04 70 80 10 8 20 12
7 05/10/04 85 80 8 11 7 9
8 08/10/04 85 79 8 11 7 10
9 12/10/04 85 77 8 12 7 11
10 15/10/04 85 78 8 11 7 11
11 20/10/04 70 76 20 12 10 12
12 25/10/04 70 71 20 15 10 14
13 28/10/04 65 69 15 15 20 16
14 08/11/04 65 74 15 12 20 14
~, 11 /11 65 69 15 13 20 18
15 /04
TABLE 2
10 The chip samples are mixtures of SF, PH and HW. The percentage of pure
species
in each mixture of SF, PH and HW is unknown, but can be considered to be
stable.
As shown in the table, model prediction accuracy is very good (t5-10%). If the
SF,
PH and HW chips are pure species, prediction accuracy can be increased
further.
Similar results were obtained using a model based on Projections to Latent
15 Structures (PLS) Assuming P(i) = f(H, S, L, Darkness, Moisture Content,
Bulk Density,
Basic Density), with i = 1, 2, 3...n being the number of chip sources, each
containing
either pure species or a mixture of species, P being the proportion of chips
(from a
chip pile) in the mixture, we have:
n
~P~i~100% (11 )
r=~
20 P~i~aaH+azS+a3L+aaD+asM+a6 pburk+a~ paasr~+C ( 12 )
wherein a,-a~ are coefficients and C is a constant.
In operation, based on the principle of the present invention, online
measurements can be combined with control of the speed of the chip feeding
screw
3 for each pile to produce stable values for chip species before chips enter
the refiner
CA 02549576 2006-06-05
26
or digester. The invention can also help operators to better control plate
gap, dilution
water rate in view of production rate, specific energy and consistency
control, and
also can serve to warn operators whenever unacceptable chips are likely to
enter the
process and negatively impact pulp quality.
Some considerations more specific to the application of the present invention
to a chemical pulp process will now be discussed. For a given batch digester
used in
the kraft pulping process, the physical characteristics of wood chips vary
broadly
from batch to batch. One of the objectives of batch digester control is to
achieve
maximum pulp production at a predetermined degree of pulp delignification such
as
chemically measured by the permanganate number (P number) with minimum
chemicals input. For a given batch digester used in the kraft pulping process,
the
physical characteristics of wood chips vary broadly from batch to batch. For
batch
digester cooking control, the monitoring method of the present invention
preferably
makes use of an online chip characteristics measurement system as described
above. Based on online measurement information related to specific parameters,
chip feeding and alkali filling can be stabilized. More particularly, a
general pulp yield
prediction model (PLS) developed to optimize the kraft process and maximize
pulp
yield can be used.
As mentioned above, the measurement system provides online information
on chip brightness, bark content, chip dynamic weight, moisture, chip wet and
dry
mass flow rate, basic and bulk density, volume flow rate and proportions of
wood
chips from the different piles. When installed in the chip feeding process,
the
measurement system generates online chip characteristics information that can
be
used to control the mixture of chips from the different piles in order to
stabilize the dry
mass of wood chips entering the digester. Online chip information can also
help the
operator to control liquor filling for a batch digester; and production rate,
alkali-to-
wood ratio dosage, etc. for a continuous digester.
Generally, batch digester controls involves many parameters, including
production rate control, cooking cycle controls (chip feeding, liquor filling,
steam filling,
heating and cooking, blowing), scheduling, steam levelling and quality
control, as well
known in the art, which are explained in detail by Leiviska K. in "Process
Control
Papermaking Science and Technology" Book 14, Fapet Oy, Jyvaskyla, Finland,
1999,
82 p. The effect of selected online chip characteristics on chip feeding and
liquor
filling control in order to increase pulp yield will now be discussed in view
of
examples based on experimental works performed in a typical batch kraft mill
where
CA 02549576 2006-06-05
27
chip feeding control was based either on chip level measurement in the batch
digester or on estimated dry mass.
As the inner volume of the digester was constant, the chip feeding volume
was calculated from the chip level measurement. In the mill, three chip piles
were
S classified as low, medium and high density. Pile 1, the low-density chip
pile, was a
mixture of two wood species; Pile 2, the medium-density chip pile, was a
mixture of
two other wood species; and Pile 3, the high-density pile, consisted of
another 3 or 4
wood species. A fixed percentage of wood chips from the three piles was fed
into the
digester. Assuming that the moisture content, bark content, chip size
distribution,
bulk density and wood species in the mixture were arbitrary constant, the chip
dry
mass was calculated from the chip level measurement. Referring to Fig. 11, the
system measurements show large variations in wood chip volumes being fed into
the
digester on the basis of chip level control. Excluding volumes of less than 70
m3,
which are abnormal, the average volume is about 99.69 m3 with a standard
deviation
of 19.19. This error cannot be overcome when chip level measurement is used to
control feeding volume, as chip size distribution, compacting, etc. strongly
influence
chip fill-in volume. For this reason, dry mass control is preferably used
instead of chip
level to control digester fill-in. As illustrated on Fig. 12, the measurement
system
provided accurate chip dry mass measurement, the absolute error being about
0.2 kg
in the range of [0, 200] kg with a moisture content measurement accuracy of
about
~1.0%. With this online sensor and the same digester, chip dry mass was
maintained
around 16,000 kg. According to experience and laboratory test results, an
optimum
liquor-to-wood ratio was defined, allowing the operator to control liquor
dosage.
Pulp yield is a major factor for a chemical pulp mill. It can be expressed as
the
ratio of pulp oven-dry weight to pulp obtained from the original wood weight.
However,
prior to the present invention, no online pulp yield measurement system was
available to assess pulping process efficiency. The yield from kraft pulping
varies
with wood types, the extent of lignin removal and cooking conditions. Hatton
in his
above cited paper proposed a very neat yield prediction equation:
Y = A-B~logH~EA)" (13)
where Y = total pulp yield (%);
H = H-factor, a pulping variable that combines cooking temperature
and time into a single variable indicating the extent of the reaction;
EA = effective alkali charge, i.e. ingredients that will actually produce
alkali under pulping conditions; and
A, B, n = species-dependent constants.
CA 02549576 2006-06-05
28
However, this equation is not applicable to kraft mills for two main reasons.
First
wood species, moisture content, density, etc. vary with time and from batch to
batch.
If these variations cannot be measured online, the constants A, B and n should
not
be identified online. Second, equation (13) was based on a laboratory test
with four
pure wood species: western hemlock, western red cedar, jack pine and trembling
aspen. In practice, the use of other wood species or a variable species
mixture in a
mill, and even variations in density, age, etc. in a stable wood species also
affect the
precision of model predictions. Therefore, that model is very difficult to
apply in an
actual mill environment, and no known prior art method or system can
accurately
predict pulp yield in such case. Chip density and wood species affect kraft
pulp
yield. High-density and hardwood chips lead to higher pulp yield while low-
density
and softwood chips lead to lower yield due to higher initial lignin content.
For a given
liquor-to-wood ratio, i.e. total liquor in the batch digester to amount of dry
chips, the
quantity of dry chips is an indicator of alkali dosage, but moisture content
variations
lead to variations in alkali dosage. Variations in the wood species mixture
(proportion
of wood chips from three piles), volume, wet mass and moisture content affect
not
only the dosage of alkali to wood, but also pulp yield and pulp quality. Chip
brightness (luminance) is an important indicator of chip decay; older chips
contain
more decayed chips. Decayed chips contain a high lignin content requiring more
alkali to be dissolved. The lignin content of bark is generally much higher
than that of
wood chips for the same wood species; as with decayed chips, a higher bark
content
requires much more alkali to be dissolved. Both decayed chips and bark also
have a
negative impact on pulp quality.
Online measurements of relevant chip characteristics solve this problem,
allowing the control of chip pile dosage screw speed in order to reduce wood
species
fluctuation, as well as the control of digester chip feeding in order to
maintain chip dry
mass from batch to batch. Furthermore, it allows the control of alkali filling
according
to the chip characteristics fed into the digester. The variations in chip
characteristics
measured by the system for different batches in a given digester are plotted
in the
graphs of Figs. 13, 14, 15 and 16, respectively representing variations of
wood
species mixtures, density (bulk, basic), luminance-moisture and bark content.
Since online information on variations in wood chip characteristics cannot be
used in Equation (13), a new pulp yield model has been developed, which is
applicable to any batch digester cooking process irrespective of the wood
chips used.
For so doing, a test was performed in a kraft mill wherein 142 process
observations
were recorded relating to the batch digester cooking parameters listed in
Table 3.
CA 02549576 2006-06-05
29
Wood Chips Species Bulk Moisture Luminance Dry
Density Content Mass
Volume Basic Bark ContentH, Wet
S,
L
Density Mass
Cooking Liquor% sulfity
Cooking EA on H Temperature
O.D. factor cycle
wood
Control
Control Permanganate Residual Pulp yield
alkali
Parameters number
TABLE 3
Taking into account equation (13) and using a Principle Component Analysis
(PCA)
to assess the contribution of each variable to the model, we chose 15
parameters
were chosen to describe the kraft process in the batch digester, as shown in
Fig.17.
The goodness of fit of the model was R2X = 0.975 (explained variation) and the
goodness of prediction of the model was QZ = 0.651 (predicted variation). On
the
basis of wood chip online measurement information and PCA model results, a
model
based on Projections to Latent Structures (PLS) was developed to predict pulp
yield,
variable importance and coefficients of which model being graphically shown in
Figs.
18 and 19, respectively. The model can be expressed as:
m
PulpYield = ~k;V,. +C (14)
r=i
where V; = value of parameter i ;
k; = model coefficients for parameter i;
C = constant
m = variable number (14 in the example shown).
The most important variables for the model are: effective alkali, chip wet
mass, hue
and moisture content. The stabilization of wood chip wet mass, moisture
content and
effective alkali increases and stabilizes pulp yield for a given wood chip
mixture.
Referring to Fig. 20 showing a graph comparing laboratory pulp yield to the
prediction of the model of Equation (14), a good correlation is observed, with
a
coefficient about 0.99. Following experimental trials under actual mill
conditions
wherein equation (14) and related control were used, a pulp yield increase of
2%
(e.g., from 48% to 50%) was obtained for a same productivity, which results in
daily
CA 02549576 2006-06-05
savings of 12.5 metric tonnes of wood chips, to which one may add savings in
white
liquor and improved pulp quality.
Although the preferred embodiments of the present invention was described
above in detail with respect to typical TMP and kraft batch process, it is to
be
S understood that the estimation methods and system of the invention may be
used in
continuous pulping process by providing appropriate adaptation to take into
account
dynamic parameters such as flow rates and delays.
