U.S. patent application number 11/446292 was filed with the patent office on 2006-12-14 for method and apparatus for estimating relative proportion of wood chips species to be fed to a process for producing pulp.
This patent application is currently assigned to Centre de recherche industrielle du Quebec. Invention is credited to Feng Ding.
Application Number | 20060278353 11/446292 |
Document ID | / |
Family ID | 37523063 |
Filed Date | 2006-12-14 |
United States Patent
Application |
20060278353 |
Kind Code |
A1 |
Ding; Feng |
December 14, 2006 |
Method and apparatus for estimating relative proportion of wood
chips species to be fed to a process for producing pulp
Abstract
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, use light
reflection-related and density-related properties as input in a
model characterizing a relation between such wood chip properties
and species information. This principle allows 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 parameters adjustment.
Inventors: |
Ding; Feng; (Quebec,
CA) |
Correspondence
Address: |
JEAN-CLAUDE BOUDREAU
CRIQ BUILDING
8475, CHRISTOPHE-COLOMB
MONTREAL
QC
H2M 2N9
CA
|
Assignee: |
Centre de recherche industrielle du
Quebec
|
Family ID: |
37523063 |
Appl. No.: |
11/446292 |
Filed: |
June 5, 2006 |
Current U.S.
Class: |
162/49 ; 162/252;
162/DIG.10; 73/53.03 |
Current CPC
Class: |
G01N 21/3559 20130101;
D21B 1/02 20130101; D21D 1/002 20130101 |
Class at
Publication: |
162/049 ;
162/DIG.010; 073/053.03; 162/252 |
International
Class: |
D21C 7/14 20060101
D21C007/14 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 3, 2005 |
CA |
2,509,075 |
Claims
1. 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, said wood
chips of each said source being characterized by one of a pure wood
species and a mixture of wood species, said method comprising 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 said 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 said source, to
obtain an estimation of said wood chips relative proportion.
2. The method of claim 1, wherein said at least one density-related
property includes one of basic density and bulk density of the wood
chips of said mass.
3. The method of claim 1, wherein said at least one density-related
property includes basic density and bulk density of the wood chips
of said mass.
4. The method of claim 1, wherein said set of wood chip properties
further includes moisture content.
5. The method of claim 1, wherein said at least one light
reflection-related wood chip property data is expressed as at least
one optical parameter representing light reflection characteristics
of the wood chips of said mass.
6. The method of claim 5, wherein said optical parameter is
luminance.
7. The method of claim 5, wherein said optical parameter is
selected from the group consisting of hue, saturation, luminance
and darkness indicator.
8. The method of claim 7, wherein said set of wood chip properties
further includes moisture content.
9. The method of claim 1, wherein said at least one light
reflection-related wood chip property data is expressed as a
plurality of optical parameters representing light reflection
characteristics of the wood chips of said mass, including hue,
saturation and luminance.
10. The method of claim 9, wherein said plurality of optical
parameters further include darkness indicator.
11. The method of claim 1, wherein said set of wood chip properties
further includes moisture content.
12. 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, said wood
chips of each said source being characterized by one of a pure wood
species and a mixture of wood species, said method comprising 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 said 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 said source, to obtain
an estimation of said wood chips relative proportion.
13. The method of claim 12, wherein said at least one
density-related property includes one of basic density and bulk
density of the wood chips of said mass.
14. The method of claim 13, wherein said step i) includes the steps
of: a) measuring weight of the wood chips of said mass; b)
measuring volume of the wood chips of said mass, c) deriving bulk
density data from said measured weight and volume of the wood chips
of said mass.
15. The method of claim 14, wherein said step i) further include
the steps of: d) measuring moisture content of the wood chips of
said mass; e) deriving basic density data from said measured
weight, volume and moisture content of the wood chips of said
mass.
16. The method of claim 12, wherein said set of wood chip
properties further includes moisture content as estimated by said
measured moisture content of the wood chips of said mass.
17. The method of claim 1, wherein said at least one light
reflection-related wood chip property data is expressed as at least
one optical parameter representing light reflection characteristics
of the wood chips of said mass.
18. The method of claim 17, wherein said optical parameter is
luminance.
19. The method of claim 17, wherein said optical parameter is
selected from the group consisting of hue, saturation, luminance
and darkness indicator.
20. The method of claim 19, wherein said set of wood chip
properties further includes moisture content.
21. The method of claim 12, wherein said at least one light
reflection-related wood chip property data is expressed as a
plurality of optical parameters representing light reflection
characteristics of the wood chips of said mass, including hue,
saturation and luminance.
22. The method of claim 21, wherein said plurality of optical
parameters further include darkness indicator.
23. The method of claim 12, wherein said set of wood chip
properties further includes moisture content.
24. 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, said wood
chips of each said source being characterized by one of a pure wood
species and a mixture of wood species, said apparatus comprising:
illumination means for directing light onto an area of wood chips
included in said mass of wood chips, said illuminated wood chips
presenting light reflection characteristics being substantially
representative of the wood chips of said mass; 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 said
mass; a density measuring unit for generating data representing at
least one density-related property characterizing the wood chip of
said 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 said
source, said computer processing all said data with said model to
obtain an estimation of said wood chips relative proportion.
25. The apparatus of claim 24, wherein said at least one
density-related property density includes bulk density, said
density measuring unit including: a weighing device for measuring
weight of at least a representative portion of the wood chips of
said mass; a volume meter for measuring volume of said
representative portion of the wood chips of said mass; a data
processor for deriving bulk density data from said measured weight
and volume of the wood chips of said mass.
26. The apparatus of claim 25, wherein said data processor is
included in said computer.
27. The apparatus of claim 24, wherein said at least one
density-related property density includes basic density, said
apparatus further comprising: a moisture sensor for measuring
moisture content of the wood chip of said mass; said density
measuring unit including: a weighing device for measuring weight of
at least a representative portion of the wood chips of said mass; a
volume meter for measuring volume of said representative portion of
the wood chips of said mass; a data processor for deriving basic
density data from said measured weight, volume and moisture content
of the wood chips of said mass.
28. The apparatus of claim 27, wherein said data processor is
included in said computer.
29. The apparatus of claim 24, wherein said set of wood chip
properties further includes moisture content, said apparatus
further comprising: a moisture sensor for producing data
representative of the moisture content of the wood chip of said
mass, which data being processed by said computer with said model
to obtain the estimation of said wood chips relative
proportion.
30. 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, said wood chips of each said source being characterized by
one of a pure wood species and a mixture of wood species, said
method comprising 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 said 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 said source, to
obtain estimation data representing said wood chips relative
proportion; iii) comparing said estimation data with predetermined
target data to produce error data; and iv) selectively modifying
the discharge rate of one or more of said wood chip sources on the
basis of the error data, to adjust the relative proportion of wood
chips in said mass.
31. A system for controlling relative proportion of wood chips
originating from a plurality of sources of wood chips in
communication with means for discharging thereof to form a mass of
wood chips to be fed to a process for producing pulp, said wood
chips of each said source being characterized by one of a pure wood
species and a mixture of wood species, said system comprising:
illumination means for directing light onto an area of wood chips
included in said mass of wood chips, said illuminated wood chips
presenting light reflection characteristics being substantially
representative of the wood chips of said mass; 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 said
mass; a density measuring unit for generating data representing at
least one density-related property characterizing the wood chip of
said mass; 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 said
source, said computer processing all said data with said model to
obtain estimation data representing said wood chips relative
proportion, said computer being further programmed to compare said
estimation data with predetermined target data to produce error
data; and a controller operatively connected to said discharging
means for selectively modifying the discharge rate of one or more
of said wood chip sources on the basis of the error data, to adjust
the relative proportion of wood chips in said mass.
32. The system of claim 31, wherein said at least one
density-related property density includes bulk density, said
density measuring unit including: a weighing device for measuring
weight of at least a representative portion of the wood chips of
said mass; a volume meter for measuring volume of said
representative portion of the wood chips of said mass; a data
processor for deriving bulk density data from said measured weight
and volume of the wood chips of said mass.
33. The system of claim 32, wherein said data processor is included
in said computer.
34. The system of claim 31, wherein said at least one
density-related property density includes basic density, said
system further comprising: a moisture sensor for measuring moisture
content of the wood chip of said mass; said density measuring unit
including: a weighing device for measuring weight of at least a
representative portion of the wood chips of said mass; a volume
meter for measuring volume of said representative portion of the
wood chips of said mass; and a data processor for deriving basic
density data from said measured weight, volume and moisture content
of the wood chips of said mass.
35. The system of claim 34, wherein said data processor is included
in said computer.
36. The system of claim 31, wherein said set of wood chip
properties further includes moisture content, said system further
comprising: a moisture sensor for producing data representative of
the moisture content of the wood chip of said mass, which data
being processed by said computer with said model to obtain the
estimation of said wood chips relative proportion.
37. A software product data recording medium in which program code
is stored, which program code will cause a computer to perform 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, said wood chips of each
said source being characterized by one of a pure wood species and a
mixture of wood species, said method comprising 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
said 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 said source, to obtain an estimation of said wood chips
relative proportion.
Description
FIELD OF THE INVENTION
[0001] 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
[0002] 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, Minn., 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. 4347.
Generally, 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.
[0003] 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. Pat. 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, Jun. 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" 89.sup.th Pulp and Paper Annual Conference Proceedings,
Jan. 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, Jun. 2-5, 2003, pp.
367-373.
[0004] 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.
[0005] 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 O Y, 66-104 (1999). Although chip aging can 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", Wood 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,
September-October 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" 89.sup.th
Pulp and Paper Annual Conference Proceedings, Jan. 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 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 .+-.10%,
which is generally insufficient to allow an efficient control over
species variation in wood chips fed to the pulping process.
[0006] 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.degree. C.) and pressure in "white
liquor", which is a mix of sodium sulphide (Na.sub.2S) 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.
[0007] 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, MacLeod 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.
[0008] 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.
SUMMARY OF THE INVENTION
[0009] 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.
[0010] 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.
[0011] 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.
[0012] 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.
[0013] 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.
[0014] 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 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.
[0015] According to the same main object, from a further broad
aspect, there is provided a software product data recording medium
in which program code is stored, which program code will cause a
computer to perform 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 said source being characterized by one of a
pure wood species and a mixture of wood species. The method
comprising the steps of: i) estimating a set of wood chip
properties characterizing the wood chips of the mass to generate
corresponding wood chip properties data, the 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 said source, to
obtain an estimation of the wood chips relative proportion.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Preferred embodiments of the present invention will now be
described in detail with reference to the accompanying drawings in
which:
[0017] 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;
[0018] 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;
[0019] 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;
[0020] 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;
[0021] 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;
[0022] FIG. 6 is a bar graph showing the results of online
measurement of the mass of wood chips fed to the measurement
station;
[0023] FIG. 7 is a PCA-X loading scatter plot of test results of
chip species effect analysis;
[0024] FIG. 8 is a graph presenting the results of a validation of
online moisture content measurement;
[0025] 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;
[0026] 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;
[0027] 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;
[0028] 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;
[0029] FIG. 13 is a graph showing variation of wood species
mixtures for different batches of wood chips fed to a digester;
[0030] FIG. 14 is a graph showing variation of density for
different batches of wood chips fed to the digester;
[0031] FIG. 15 is a graph showing variation of luminance and
moisture for different batches of wood chips fed to the
digester;
[0032] FIG. 16 is a graph showing variation of bark content for
different batches of wood chips fed to the digester;
[0033] FIG. 17 is a graph showing variables and PCA model goodness
of fit and prediction for the digester;
[0034] FIG. 18 is a graph showing variable importance in the
model;
[0035] FIG. 19 is a graph showing coefficients used in the model;
and
[0036] FIG. 20 is a graph comparing laboratory pulp yield to the
prediction of the model.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0037] 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.
[0038] 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
arrows 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 at 10 for measuring weight of at
least 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 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, Mass., can be used. A load cell is a
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.
TABLE-US-00001 TABLE 1 W.sub.Measurement (kg) Test No.
W.sub.Standard (kg) 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
It is to-be understood that any other suitable weighing device
based on a different weight measurement principle may be used.
[0039] 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 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 SAlD
from IDEC Corporation, Sunnyvale, Calif., 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 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.
[0040] 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.times.1.5 m.sup.2 in the present example. Supposing that the
average wood chip thickness is 5 mm, fallen chip volume is about:
V=0.31.times.1.5.times.0.005=2.325.times.10.sup.-3 (m.sup.3) (1)
Assuming an average basic density p of wood chip is 450 kg/m.sup.3,
the fallen chip mass is:
m=.rho..times.V=450.times.2.325.times.10.sup.-3=1.04625 (kg) (2)
the chip's gravitational potential energy is:
E.sub.C=m.times.g.times.h=1.046253.times.9.81.times.1=10.26 (N.m)
(3) wherein:
[0041] g=acceleration of gravity=9.81 (m/s.sup.2)
[0042] h=chip falling height (m)
The idler reaction work is: W=F.times.L (4) Wherein:
[0043] F=idler reaction force (N),
[0044] L=conveyer length (m).
According to the energy conservation law, the chip's gravitational
potential energy equals the idler reaction work (E.sub.C=W). Thus,
by transferring values between equations (3) and (4):
F=E.sub.C/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).
[0045] 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: m m = m c + C g .times. h fall L .times. ( kg )
##EQU1## Chip Unit Length Mass: m .mu. = m m l c .times. ( kg
.times. / .times. m ) ##EQU2## Belt Feed Forward Length:
l.sub.f=.upsilon..sub.b.times.t (m) Chip Fall Mass:
m.sub.d=m.sub.u.times.l.sub.f (kg) Chip Flow Profile:
A.sub.s=l.sub.p.times.(h.sub.CMS-h.sub.c).times.C.sub.pc (m.sup.2)
Fall Volume: V.sub.d=l.sub.f.times.A.sub.s (m.sup.3) Fall Bulk
Density: .rho. bulk_d = m d V d .times. C bulk .function. ( kg
.times. / .times. m 3 ) ##EQU3## Fall Basic Density: .rho. basic_d
= m dry - d V d .times. C basic .function. ( kg .times. / .times. m
3 ) ##EQU4## Dry chip mass: m.sub.dry.sub.--d=m.sub.d(1-H.sub.m)
Measured parameters are: [0046] Belt speed: v.sub.b (m/s) [0047]
Chip Covered Length on Belt: I.sub.c (m) [0048] Wet Chip Mass
Measured: m.sub.c (kg) [0049] Global Moisture Content: H.sub.m (%)
[0050] Height of CMS to Chip Bed: h.sub.c (m) Exemplary chip
feeding configuration parameter values are: [0051] Chip Passage
Width: I.sub.p=0.31 (m) [0052] Height of CMS to Belt:
h.sub.CMS=0.18 (m) [0053] Chip Fall Height: h.sub.fall=1 (m) [0054]
Gravity Acceleration: g=9.81 (m/s.sup.2) [0055] Conveyer Length
L=16.7 (m) Coefficients and exemplary set values are: [0056] Chip
Nominal Mass that Hits the Belt: C.sub.g=0 [0057] Chip Flow Profile
Correction Coefficient: C.sub.pc=1 [0058] Chip Bulk Density
Correction Coefficient: C.sub.bulk=1 [0059] Chip Basic Density
Correction Coefficient: C.sub.basic=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.
[0060] 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 1, 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: m d .function. ( k ) = m m .function. ( k ) l c
.function. ( k ) .times. v b .function. ( k ) .times. t .function.
( k ) ( 6 ) ##EQU5## For a given start time t.sub.0 to end time
t.sub.end, the total chip mass measured can be expressed as: m
total = k = t .times. .times. 0 t end .times. m d .function. ( k )
.times. .times. where .times. : .times. .times. k = t 0 , t 1 ,
.times. .times. t end ( 7 ) ##EQU6## 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 t.sub.0,
and the main discharging screw device 17 is stopped at end of
sampling time t.sub.end. 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 measurement station used in the example
described above, the accuracy of load cells is better than
.+-.0.5%. 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 for during
the feeding stage.
[0061] 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 properties, as well
as volume and moisture content data. Such wood chip inspection
apparatus is basically described in U.S. Pat. No. 6,175,092 B1
issued on Jan. 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.
[0062] 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 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.
[0063] 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 superficial 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
superficial 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 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.
[0064] 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 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, Calif. The sensor
47 generates at an output 79 thereof electrical signals
representing mean surface moisture values for the superficial wood
chips 6'.
[0065] 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-2.TM. 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.
[0066] 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 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 = L
R L S - 1 ( 8 ) ##EQU7## Wherein I is the optical response index,
L.sub.R is a mean luminance value associated with the reference
samples and L.sub.S 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 = P D P T ( 9 ) ##EQU8##
Wherein D is the chip darkness index, P.sub.D 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 P.sub.T 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.
[0067] 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 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. The initialization is completed by camera
and image processing calibration through the operator interface
module 126.
[0068] 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 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 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. The PLC configuration relates to
parameters governing communication between computer unit 25 and PLC
33, such as master-slave 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 interface, 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 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.
[0069] 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 superficial
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
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.
[0070] 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=0.2125R+0.7154G+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.
[0071] 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. 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.
[0072] 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.
[0073] 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 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.
[0074] 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%.
[0075] 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
Mathlab.TM., typically involves the following steps: [0076] 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; [0077] 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. [0078] Defuzzification method:
weighted average.
[0079] 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
compared with laboratory trials are shown in Table 2.
TABLE-US-00002 TABLE 2 SF % PH % HW % No Date 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 70
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 15 11/11/04 65 69 15 13 20 18
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 (.+-.5-10%). If the SF, PH and HW chips are
pure species, prediction accuracy can be increased further.
[0080] Similar results were obtained using a model based on
Projections to Latent 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: i = 1 n
.times. P .function. ( i ) = 100 .times. % ( 11 ) P .function. ( i
) = a i .times. H + a 2 .times. S + a 3 .times. L + a 4 .times. D +
a 5 .times. M + a 6 .times. .rho. bulk + a 7 .times. .rho. Basic +
C ( 12 ) ##EQU9## wherein a.sub.1-a.sub.7 are coefficients and C is
a constant.
[0081] 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 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.
[0082] 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.
[0083] 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.
[0084] 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 chip feeding control was based either on chip
level measurement in the batch digester or on estimated dry
mass.
[0085] 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 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 m.sup.3, which are abnormal, the average volume is about 99.69
m.sup.3 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.
[0086] 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(log H)(EA).sup.n (13)
where Y=total pulp yield (%); [0087] H=H-factor, a pulping variable
that combines cooking temperature and time into a single variable
indicating the extent of the reaction; [0088] EA=effective alkali
charge, i.e. ingredients that will actually produce alkali under
pulping conditions; and [0089] A, B, n=species-dependent constants.
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.
[0090] 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.
[0091] 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.
[0092] 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. TABLE-US-00003 TABLE 3 Wood Chips
Species Bulk Moisture Luminance Dry Density Content Mass Volume
Basic Bark Content H, S, L Wet Density Mass Cooking % sulfity
Liquor Cooking EA on O.D. wood H factor Temperature cycle Control
Control Permanganate Residual Pulp yield Parameters number
alkali
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 R.sup.2X=0.975 (explained variation) and the
goodness of prediction of the model was Q.sup.2=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: PulpYield = i = 1 m .times. .times. k i .times. V
i + C ( 14 ) ##EQU10## where V.sub.i=value of parameter i; [0093]
k.sub.i=model coefficients for parameter i; [0094] C=constant
[0095] 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 savings of 12.5 metric tonnes of wood chips, to which one may
add savings in white liquor and improved pulp quality.
[0096] 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 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.
* * * * *