US20060196621A1

US20060196621A1 - Virtual hand sheet method and system for estimating paper properties

Info

Publication number: US20060196621A1
Application number: US11/069,577
Authority: US
Inventors: Ola Johansson
Original assignee: J&L Fiber Services Inc
Current assignee: J&L Fiber Services Inc
Priority date: 2005-03-01
Filing date: 2005-03-01
Publication date: 2006-09-07
Also published as: SE0600398L; WO2006093581A3; WO2006093581A2

Abstract

A method and system for estimating a property of pulp or paper, such as freeness, tensile index, or tear index. In one embodiment, the method comprises receiving image data depicting a plurality of pulp fibers, virtually distributing the pulp fibers over a predetermined area to create a virtual distribution of the pulp fibers, and determining a property using the virtual distribution. In one embodiment, the system comprises an image capture device and an analyzer. The image capture device is configured to send image data depicting a plurality of pulp fibers. The analyzer is configured to receive the image data from the image capture device, create a virtual distribution of the pulp fibers depicted in the received image data, and determine a property by analyzing the virtual distribution.

Description

FIELD

Embodiments of the invention relate generally to methods and systems for automated measurement of pulp and sheet properties. More specifically, embodiments of the invention relate to methods and systems for determining pulp and sheet properties.

BACKGROUND

The manufacture of paper involves many variables. Wood itself is not a uniform raw material, and grinding of wood yields a heterogeneous pulp, which can include a liquid slurry and fibers (e.g., wood chips, wood fibers, cotton, cloth, and the like) suspended in the slurry. In addition, the process of refining pulp may destroy a portion of the fibers therein. Because of these and other variables, pulp is typically characterized before significant quantities of paper are produced. Such characterization may involve determining drainage and/or strength properties, which can be used to predict properties of paper to be manufactured from the pulp. When characterization indicates that the pulp is unsuitable-for instance, the fibers are not sufficiently long and slender-the pulp may be further refined.
Existing offline measurement technologies typically require significant amounts of time in order to adequately characterize a pulp for production purposes (e.g., 8 to 12 hours). The reason is that a physical sheet of paper, termed a “hand sheet,” must be manually made from pulp samples before actual testing can begin. Typically, a predetermined weight of fibers (e.g., 60 g/m²) is needed to make a hand sheet. Once made, the hand sheet is tested to determine such properties as tensile index, tear burst, or the like. Robotic machines can reduce the time to make and test hand sheets to between approximately 20 and 40 minutes.
With existing online measurement technologies, which interface with a production line of a mill, only basic information on fiber morphological characteristics is provided (e.g., freeness, fiber coarseness, fiber length, fiber distribution), and at relatively low measurement frequencies (e.g., 1 to 6 samples per hour). Moreover, such technologies involve costly measurement units, often on the order of hundreds of thousands of dollars. As such, the cost is often prohibitive, with relatively few process points being sampled in a mill.
FIG. 1 (Prior Art) shows an online measurement system 100 for characterizing a pulp. The system 100 includes sampling lines 110, pulp samplers 120, a measurement unit 140, and a computer 150. The sampling lines 110 respectively interface with pulp samplers 120 placed at various process points of a mill 130. The sampling lines 110 are multiplexed into the measurement unit 140, a large device that employs image analysis techniques, using a camera and processing circuitry, in order to measure physical dimensions of fibers and shives in the pulp. The measurement unit 140 includes a freeness tester that includes a measuring chamber, a measuring sensor, and a screen plate. The measurements of the measurement unit 140 are outputted to the computer 150. Typically, 40 to 60 minutes are required per sample.

SUMMARY

The following summary sets forth certain exemplary embodiments of the invention described in greater detail below. It does not set forth all such embodiments and should in no way be construed as limiting of the invention.
Embodiments of the invention relate to methods, systems, and devices for estimating pulp and sheet properties.
In one embodiment, a method of estimating a property of paper comprises receiving image data depicting a plurality of pulp fibers, virtually distributing the pulp fibers over a predetermined area to create a virtual distribution of the pulp fibers, and determining the property using the virtual distribution.
In one embodiment, a system for pulp fiber analysis comprises an image capture device and an analyzer. The image capture device is configured to send image data depicting a plurality of pulp fibers. The analyzer is configured to receive the image data; create a virtual distribution of the pulp fibers depicted in the received image data, over a predetermined area; and estimate a property of paper by analyzing the virtual distribution.
Other embodiments herein relate to pulp analyzers, image capture devices, and graphical user interfaces.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 (Prior Art) shows an online measurement system for characterizing a pulp.
FIG. 2 shows a pulp analyzer according to an embodiment of the invention.
FIG. 3 shows a process according to an embodiment of the invention.
FIG. 4 shows an exemplary received image and a counterpart binary image according to an embodiment of the invention.
FIGS. 5A and 5B show representations of exemplary virtual distributions according to an embodiment of the invention.
FIG. 6A shows a simplified illustration of flow through a virtual distribution according to an embodiment of the invention.
FIG. 6B is a graph showing the relationship between water column height and time according to an embodiment of the invention.
FIG. 7 shows an exemplary received image, a counterpart binary image, a counterpart finite element analysis mesh, and an element of the mesh according to an embodiment of the invention.
FIG. 8 shows a simplified representation of an exemplary mesh of elements according to an embodiment of the invention.
FIG. 9 shows a representation of a mesh subsection subjected to a horizontal load according to an embodiment of the invention.
FIG. 10 is a graph showing the relationship between reaction force and displacement in an exemplary mesh according to an embodiment of the invention.
FIG. 11 is an exemplary graph showing the relationship between modeled tensile index and measured tensile index according to an embodiment of the invention.
FIG. 12 is an exemplary screenshot according to an embodiment of the invention.
FIG. 13 is an exemplary screenshot according to an embodiment of the invention.
FIG. 14 shows a system for pulp fiber analysis according to an embodiment of the invention.
FIG. 15 shows a system according to an embodiment of the invention.
FIG. 16 shows a system according to an embodiment of the invention.
FIG. 17 is a block diagram of an image capture device according to an embodiment of the invention.
FIG. 18 is a perspective view of an image capture device according to an embodiment of the invention.
FIG. 19 is a perspective view of the image capture device showing the electronics module partially pulled out of the housing according to an embodiment of the invention.
FIG. 20 is an exploded view of the housing according to an embodiment of the invention.
FIG. 21 is an exploded view of subcomponents of the sampling module according to an embodiment of the invention.
FIG. 22 is an exploded view of subcomponents of the sampling module according to an embodiment of the invention.
FIG. 23 is an exploded view of subcomponents of the electronics module according to an embodiment of the invention.
FIG. 24 is an exploded view of a sampling reservoir according to an embodiment of the invention.

DETAILED DESCRIPTION

Before any embodiments of the invention are explained in detail, it is to be understood that embodiments of the invention are not limited in application to the details of operation and construction, the arrangement of components, and the pseudocode set forth in the following description or illustrated in the following drawings. Embodiments are capable of being practiced or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein are for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The order of limitations specified in any method claims does not imply that the steps or acts set forth therein must be performed in that order, unless an order is explicitly identified in the specification as essential. In addition, it should be noted that a plurality of hardware and software based devices, as well as a plurality of different structural components, may be utilized to implement embodiments of the invention.
Embodiments of the invention provide methods, systems, and devices for measuring fiber morphological characteristics. In one embodiment, images of sampled pulp fibers are processed to create an artificial computer model of a distribution of fibers. This virtual hand sheet is analyzed to calculate pulp properties, such as freeness, drainage time, density, and strength properties. Embodiments herein bypass the time-consuming, costly phases of physical hand sheet preparation, conditioning, and testing typically employed in the art. Embodiments herein provide results at frequencies that are significantly higher than those of existing technologies (e.g., 5 to 20 times higher).
In particular, embodiments herein can provide drainage and strength properties in minutes instead of hours. Moreover, instead of implied properties, sheet properties can be determined. Embodiments can be used, for example, to optimize quality of paper or medium-density fiberboard (MDF) produced by a mill, or to selectively control components in a mill.
Although embodiments herein generally focus on pulp properties in the paper industry, embodiments can be applied to substances that have similarities to pulp, such as textile and food constituents. For instance, embodiments can be applied in settings in which particles have a dimension of interest of approximately 2 to 5 micrometers.
FIG. 2 shows a pulp analyzer 200 according to an embodiment of the invention. In general, the pulp analyzer 200 can be used in a production or laboratory setting to determine pulp properties, predict paper (sheet) properties, and optionally control a production process. The pulp analyzer 200 includes an image data processor 220, a virtual distributor 230, and a virtual distribution analyzer 240.
The modules of the pulp analyzer 200 can be implemented in various combinations of software and/or hardware. In one embodiment, the pulp analyzer 200 is implemented as a computer running one or more software applications. For instance, the pulp analyzer 200 may be implemented on a computer having 1 GB of random access memory (RAM) and running the Windows XP operating system. To improve performance, the computer may include multiple processors. The pulp analyzer 200 optionally may have connectivity to one or more networks (e.g., a Megabit network and/or Gigabit Ethernet network). In one embodiment, the pulp analyzer 200 utilizes the MATLAB tool of The MathWorks, Inc. (Natick, Mass.) to perform certain image analysis operations described below. In another embodiment, the pulp analyzer is programmed in C and utilizes the library of the Lua programming language (designed by Tecgraf-Computer Graphics Technology Group, Rio de Janeiro, Brazil).
The image data processor 220 receives image data 210 depicting pulp fibers. For example, the image data 210 may be digital data representing a series of images that depict pulp fibers. The image data processor 220 processes the image data 210 to produce descriptive information for the respective pulp fibers, such as image representations and/or geometric parameters. In some embodiments, the image data processor 220 may be omitted from the pulp analyzer 200, such as embodiments wherein the pulp analyzer 200 receives data suitably preprocessed for the virtual distributor 230. The image data 210 may be received, for example, in real time (e.g., from an image capture device over a network) or from a storage device.
The virtual distributor 230 uses some or all the descriptive information to create a virtual distribution of the pulp fibers. In one embodiment, the virtual distributor 230 virtually distributes the pulp fibers over a predetermined area.
The virtual distribution analyzer 240 analyzes the virtual distribution to determine a property 250. For instance, the virtual distribution analyzer 240 may determine a drainage property or a strength property (e.g., tensile index or tear index).
The pulp analyzer 200 optionally includes a controller or controller interface (not shown), which sends information that controls, or can be used by a controller to control, one or more components of a paper production system, such as, for example, a refiner, a screen, or a paper machine. The paper production system may include a distributed control system (DCS) that interfaces with such components.
Additional examples of techniques that can be employed within or in connection with the pulp analyzer 200 are set forth below.
FIG. 3 shows a process 300 according to an embodiment of the invention. The process 300 can be used in a production or laboratory setting to determine pulp and/or sheet properties, and optionally control a production process. The process 300 can be used, for example, by the pulp analyzer 200 of FIG. 2. It is to be appreciated that the detailed implementations described herein are but examples of how embodiments of the invention can be implemented.
Task T310 receives image data depicting pulp fibers. In one embodiment, the image data is digitally received as a series of RGB images taken by camera(s) remote from the pulp analyzer 200. Each image frame may include, for instance, 10 to 15 fibers. In other embodiments, grayscale or CMYK images are received.
Task T320 processes the image data to produce descriptive information for the respective pulp fibers. The descriptive information can include, for example, image representations and/or geometric parameters for the fibers. The descriptive information can be used immediately and/or stored in a memory. Task T320 may be omitted in some embodiments, such as embodiments wherein a pulp analyzer receives data that has been suitably preprocessed for task T330 (described below).
In particular, thresholding techniques can be applied to the received images to produce binary images, wherein pixels of value 1 correspond to fibers, and pixels of value 0 correspond to a background (or vice versa). For example, thresholding can be applied to one extracted color plane of a received RGB image. Each binary image can be further processed to identify respective fibers in that image. For each identified fiber, a binary image can be produced and stored as a numerical array. FIG. 4 shows an exemplary received image 410 depicting multiple fibers, a portion 420 thereof that includes a single fiber of interest, and a binary image 430 of the fiber that is produced by a thresholding operation.

By applying image analysis techniques to the binary images, geometric parameters for the fibers can be determined and stored in a matrix. Exemplary determined geometric parameters include:



Pixels	Number of pixels in each particle.

Area (unit)	Surface area of particle in user-calibrated units.
Image area (unit)	Surface area of the entire image in user-
	calibrated units.
Ratio Area/Scanned	Ratio of the surface area of a particle to the
area (%)	scanned area.
Ratio Area/Total	Ratio of a particle's surface area to the total area.
area (%)
Center of mass X	X-coordinate of the center of gravity.
Center of mass Y	Y-coordinate of the center of gravity.
Longest segment	Length of the longest horizontal line segment.
length
Longest segment left	Leftmost x-coordinate on the longest horizontal
column (X)	line segment.
Longest segment top	Top y-coordinate on the longest horizontal line
row (Y)	segment.
Perimeter (unit)	Length of the outer contour of a particle in user-
	calibrated units.
SumX	Sum of the x-axis for each pixel of the particle.
SumY	Sum of the y-axis for each pixel of the particle.
SumXX	Sum of the x-axis squared for each pixel of the
	particle.
SumYY	Sum of the y-axis squared for each pixel of the
	particle.
SumXY	Sum of the x-axis and y-axis for each pixel of
	the particle.
Corrected projection	Sum of the vertical segments in a particle.
X
Corrected projection	Sum of the horizontal segments in a particle.
Y
Moment of inertia Ixx	Inertia matrix coefficient in xx.
Moment of inertia Iyy	Inertia matrix coefficient in yy.
Moment of inertia Ixy	Inertia matrix coefficient in xy.
Mean chord X	Mean length of horizontal segments.
Mean chord Y	Mean length of vertical segments.
Particle orientation	Direction of the major axis of a particle.
Equivalent ellipse	Total length of the ellipse axis having the same
minor axis	area as the particle and a major axis equal to half
	the max intercept.
Ellipse major axis	Total length of the major axis having the same
(unit)	area and perimeter as the particle in user-
	calibrated units.
Ellipse minor axis	Total length of the minor axis having the same
(unit)	area and perimeter as the particle in user-
	calibrated units.
Ratio of equivalent	Ratio of the length of the major axis to the minor
ellipse axis	axis.
Rectangle big side	Length of the larger side of a rectangle that has
(unit)	the same area and the same perimeter as the
	particle in user-calibrated units.
Rectangle small side	Length of the smaller side of a rectangle that has
(unit)	the same area and the same perimeter as the
	particle in user-calibrated units.
Elongation factor	Ratio of the longest segment within an object to
	the mean length of the perpendicular segments.
Compactness factor	Ratio of an object area to the area of the smallest
	rectangle containing the object.
Heywood circularity	Ratio of an object's perimeter to the perimeter of
factor	the circle within the same area. A circle has a
	Heywood circularity factor of 1.
Type factor	Complex factor that relates the surface area to
	moment of inertia.
Hydraulic radius (unit)	Ratio of an object's area to its perimeter.
Waddel disk diameter	Diameter of the disk that has the same area as
(unit)	the particle in user-calibrated units.
Diagonal (unit)	Diagonal of an equivalent rectangle in user-
	calibrated units.

In one embodiment, task T320 is performed until it is determined that no additional useful information is gained from processing of additional images. Statistical analysis techniques (e.g., involving standard deviation) may be applied to help make this determination. For instance, when the standard error with respect to one or more geometric parameters is sufficiently small, no additional fibers need to be considered. In one embodiment, a user can specify a standard error (or range) that is considered acceptable. Accordingly, superfluous analysis of data is avoided, and properties can be determined more rapidly.
Task T330 virtually distributes the pulp fibers over a predetermined area to create a virtual distribution of the pulp fibers. The virtual distribution may be a computerized representation analogous to a physical hand sheet. FIG. 5A shows a representation 500 of an exemplary virtual distribution that includes about 1,000 fibers superimposed on a circular field. FIG. 5B shows a representation 510 of an exemplary virtual distribution that includes 250,000 fibers superimposed on a circular field. It is to be appreciated that, where the fibers do not intersect, a virtual distribution has orifices or holes through which water can virtually leak. As such, the orifices represent porosity of the virtual distribution.
In one embodiment, the binary images of each fiber are randomly distributed over a field of known radius, wherein the position of each fiber is randomly oriented. The binary images can be randomly distributed as the images are produced. Accordingly, assuming 10 to 15 fibers per received image frame, the virtual distribution gradually grows by groups of 10 to 15 fibers. In other embodiments, the virtual distribution is not created until all fibers of interest are identified and associated image representations are stored in memory. In an exemplary embodiment, the amount of fibers virtually distributed is roughly equivalent to about 3 grams of actual fibers. Although embodiments described herein involve a two-dimensional field, in alternative embodiments, pulp fibers can be virtually distributed in a three-dimensional space. Moreover, the area over which the fibers are distributed need not be circular or otherwise regular in shape, extent, or volume.
Task T340 determines a property using the virtual distribution. In one embodiment, drainage properties, such as flow, freeness (e.g., Canadian Standard Freeness, CSF), and/or drainage time, can be determined by modeling drainage of water through the set of orifices in the virtual distribution. Each of the orifices contributes to the drainage of the virtual distribution by leaking water. Accordingly, drainage properties can be determined by summing up the flow through the orifices over time.
More specifically, an exemplary model can involve measuring the drainage of water in a water column sitting atop the virtual distribution. FIG. 6A shows a simplified illustration of flow in a virtual distribution 600 according to an embodiment of the invention. The virtual distribution 600 includes a plurality of orifices. A reservoir 610 filled with water sits atop the virtual distribution 600. The height h 620 of the water column 640 decreases as water leaks from the orifices. The flow q′ 630 through one such orifice 650 is shown. The flow is expressed in terms of volume per second (m³/s). Flow is a function of the height h 620, viscosity new, thickness ti of the virtual distribution, and the force of gravity g.
FIG. 6B is a graph plotting the height h 620 of the water column 640 versus time t. Initially, the high pressure of a full or nearly full reservoir 610 causes the water to leak at a fast rate, and the height h 620 decreases rapidly. As water leaks, the pressure decreases, and the height decreases more slowly. The velocity v of the leaking water is Δh/Δt.
In one embodiment, the model involves, at each instant of time, determining the total flow through the orifices of the virtual distribution, and integrating the flow over the drainage time to determine freeness. The drainage time may be defined as ranging from an initial time to a time when a minimum velocity has been reached.

Below is pseudocode setting forth basic elements of an exemplary procedure for determining drainage properties. The pseudocode is not intended to be exhaustive, and variations of this example are within the scope of embodiments of the invention.



tid=0;	-- initialize drainage time
ho=0.125;	-- define height of water reservoir
h =ho;	-- set initial height of water column
csf=0;	-- initialize freeness

Ahand=3.1415*(rad*radratio){circumflex over ( )}2;

-- define area of virtual distribution

dh=0;	-- set initial delta h
new=1.0e−6;	-- set viscosity
ti=120.0e−6;	-- set thickness of virtual distribution
g=9.81;
dt=0.025;	-- set time increment
minvel=0.10;	-- set minimum velocity

for tid=0 to time at which minimum velocity is reached

{

q=0	-- initialize flow at this instant

for every hole at this instant of time

{

determine hydraulic diameter Di and area Ai of hole

c=64*new*ti/Di{circumflex over ( )}2;

vi= c/2+math.sqrt((c/2){circumflex over ( )}2+2hg);	-- compute velocity of water
q=q+vi*Ai;	-- add flow due to this hole to the total flow
loop to next hole
}

dh=q*dt/(Ahand);	-- compute delta h
tid=tid+dt;	-- update time
h=h−dh;	-- calculate new height h
csfarea=3.1415*(45e−3){circumflex over ( )}2;	-- set area of freeness tester, assuming a diameter of 90 mm
csf=(ho−h)csfarea1e6;	-- calculate freeness
}
-- check if velocity is too low
if(dh/dt <= minvel) then
{
csftid=tid;
break;
end
}
loop to next instant of time
}
return csf, tid

In one embodiment, drainage properties are determined by dividing the virtual distribution into subregions, considering the flow in those subregions, and ignoring the flow of subregions that is statistically far different from that of other regions. Such an approach can prevent subregions that have unusually large orifices, and therefore, measurably higher flow, from dominating the determination of drainage properties. In an exemplary implementation, a virtual distribution is divided into 30 subregions for purposes of determining drainage properties. Statistical methods (e.g., standard deviation) may be used to identify subregions to ignore.
In addition to drainage properties, strength properties can be determined using a virtual distribution. In an exemplary implementation, two-dimensional finite element analysis (FEA) methods are applied. Each fiber is modeled as one or more beam elements having an associated Young's modulus E, moment of inertia I, and cross-sectional area A, which can be determined using image analysis techniques, as described above. More specifically, binary images of each fiber (see task T320 of FIG. 3) can be further processed to yield FEA mesh representations of the fiber, which include one or more elements and nodes. FIG. 7 shows an exemplary received image 410 depicting multiple fibers, a portion 420 thereof that includes a single fiber of interest, a binary image 430 of the fiber that is produced by a thresholding operation, a FEA mesh 770 of the fiber, and an element 780 of the mesh 770.
After a mesh representation of each fiber is created, and the associated length and width are determined (e.g., by using the geometric parameters that can be determined in task T320 of FIG. 3), the mesh representations are distributed randomly (e.g., at a random angle and position) over a predetermined area to produce a virtual distribution. Each element may be represented as an equation in slope-intercept form (y=Kx+L). Each point at which fibers intersect is computed, and nodes are placed at the respective point of intersection. Elements that coincide with these nodes are subdivided into two elements around the node. In one embodiment, each point of intersection is assumed to be a perfect bond, that is, it is assumed that there is no slip between the fibers. Elements that do not intersect with any other elements are discarded. Accordingly, a mesh of elements is generated. FIG. 8 shows a simplified representation 800 of an exemplary mesh of elements. Elements are depicted as lines and identified numerically by circled numbers. Nodes are identified by numbers that are not circled.
FEA techniques can be applied to the generated mesh of elements to solve for deflections and stresses in the virtual distribution according to the equation KU=F, where K is the stiffness matrix, U is the displacement, and F is the applied force. The nodes are considered to be fixed. Different loads (forces and/or displacements) are applied, and the mechanical behavior of the sheet is used to compute tensile stress, bonding, and the like. In one embodiment, a matrix is created that includes, for example, the elements, properties of the elements, and boundary conditions. Reaction forces and stresses can be determined using the matrix. In one embodiment, the mesh of elements is cut into smaller sections, which are analyzed individually as test specimens.
FIG. 9 shows a representation 900 of a mesh subsection subjected to a horizontal load having a displacement ΔU. The mesh before being subjected to the load is depicted with dashed lines. The displaced fibers are shown as gray lines. The strength properties of the virtual distribution depend, for example, on the length, width, and orientation of the fibers therein. For example, fibers that are perpendicular to the applied load contribute less to the stiffness of the virtual distribution than fibers that are aligned with the load.
FIG. 10 is a graph showing the relationship between reaction force and displacement in an exemplary mesh. From the stresses and reaction forces, a sheet stiffness can be computed using the equation K_sheet=F/U.
Once the reaction forces, stresses, and displacements have been determined for the virtual distribution, the data can be calibrated against a set of experimentally collected data, such as measured data corresponding to physical hand sheets created from pulp samples. For instance, relationships for tensile index, tear burst, and the like can be identified. FIG. 11 is a graph showing, for an exemplary context, the relationship between tensile index modeled using a virtual distribution and tensile index measured using physical hand sheets.
In one embodiment, one or more of the following exemplary strength properties are determined for the virtual distribution or portions thereof: tear index (mN-m²/g), burst index (kPa-m²/g), sheet density (kg/m³), air resistance (s/100 mL), tensile index (Nm/g), stretch (%), tensile energy index (J/kg), and tensile stiffness (kNm/g).
In one embodiment, a pulp analyzer provides a graphical user interface (GUI) that presents useful information for a user and/or receives user inputs. FIG. 12 is an exemplary screenshot 1200 according to an embodiment of the invention. The screenshot 1200 includes a representation 1210 of a virtual distribution of fibers created by a pulp analyzer. In the representation 1210, the fibers are color-coded depending on their relative area. In other embodiments, the fibers can be represented differently based on other properties.
FIG. 13 is an exemplary screenshot 1300 according to an embodiment of the invention. The screenshot 1300 includes several graphs representing information determined by a pulp analyzer using a virtual distribution. In particular, the graph 1310 plots flow versus drainage time. The graph 1320 plots freeness versus drainage time. The graph 1330 plots freeness versus time. The graphs 1310, 1320, and 1330 are but examples of the host of information that can be presented to a user of the pulp analyzer. Alternatively or additionally, information concerning the distribution of fibers in the virtual distribution can be presented, such as in the form of contour plots (e.g., plotting width versus length) or graphs (e.g., plotting percent of fibers versus fiber length).
FIG. 14 shows a system 1400 for pulp fiber analysis according to an embodiment of the invention. The system 1400 includes an image capture device 1410 and a pulp analyzer 1420. In the embodiment shown, the image capture device 1410 and the pulp analyzer 1420 communicate over a network 1430 (or multiple networks), which may be a high-speed TCP/IP Ethernet network or other suitable network.
The image capture device 1410 sends image data 1440 depicting pulp fibers over the network 1430. In one embodiment, the image capture device 1410 interfaces with a source of pulp to be analyzed and captures images of the pulp.
The pulp analyzer 1420 receives the image data 1440 from the image capture device 1410. Using the image data 1440, the pulp analyzer 1420 creates a virtual distribution 1450 of the pulp fibers. For instance, the pulp analyzer 1420 may employ approaches described above, or other suitable approaches. The pulp analyzer 1420 determines at least one property 1460, such as a drainage or strength property, by analyzing the virtual distribution 1450. The pulp analyzer 1420 optionally sends data 1470 to the image capture device 1410, which data may be used, for example, to calibrate the image capture device 1410, request data from the image capture device 1410, or direct the image capture device 1410 to perform an operation.
The pulp analyzer 1420 optionally generates control information 1480 that controls, or can be used by a controller to control, one or more components of a paper production system, such as, for example, a refiner, a screen, or a paper machine. For instance, control information 1480 can be sent to a central computer of a mill to control (1) the energy input, plate gaps, and/or feed of a refiner; (2) the rotor speed and/or delta p (i.e., the difference between infeed and outlet pressure) of a screen (to achieve desired fractionation or freeness); or (3) mixtures of different pulp streams to, and feed consistency of, a paper machine.
FIG. 15 shows a system 1500 according to an embodiment of the invention. The system 1500 includes a pulp analyzer 1420 and multiple image capture devices 1410 communicating with the pulp analyzer 1420 over a network 1430 (e.g., a wireline or wireless network).
The image capture devices 1410 are placed at various process points of a mill 1530. In particular, the image capture devices 1410 are appropriately interfaced at those points in order to sample pulp, capture images of the pulp, and send captured images to the pulp analyzer 1420 over the network 1430. In one embodiment, each image capture device 1410 has an IP address, and sends packetized data to the pulp analyzer 1410, including the IP address and image data. In an exemplary implementation, up to ten image capture devices 1410 send image data to the pulp analyzer 1420. In other embodiments, data from multiple image capture devices 1410 is multiplexed to the pulp analyzer 1420.
Using the image data received from each image capture device 1410, the pulp analyzer 1420 creates a respective virtual distribution and determines properties as described above. In one embodiment, the pulp analyzer 1420 employs an event-driven architecture, wherein the pulp analyzer 1420 is responsive to the occurrence of events, such as the receipt of new image data. Because each image capture device 1410 in the system 1500 can have an identity (e.g., a unique IP address), the pulp analyzer 1420 can identify image data received from respective image capture devices 1410. As such, the pulp analyzer 1420 can in parallel create separate virtual distributions for pulp sampled by respective image capture devices 1410. Multithreading may be used to facilitate the necessary separate processing. Alternatively or additionally, parallel processors can be incorporated in a pulp analyzer 1420. It is to be appreciated that multiple pulp analyzers 1420 can be implemented in a particular setting, and can create virtual distributions for a subset of the image capture devices 1410 in that setting. Further, a pulp analyzer 1420 can be remote from, or proximate to, an image capture device 1410. If implemented on a portable computer (e.g., laptop), the pulp analyzer 1420 can be physically moved.
Various benefits can be realized using embodiments herein. For example, by incorporating image capture devices 1410 at multiple process points of a mill, the pulp sampling frequency is greatly increased, enabling both short- and long-term variations in pulp properties to be detected and responded to. In addition, because a greater body of information is available, problems can be pinpointed with greater accuracy, and control decisions can be more readily made, simplifying associated control systems. Moreover, the architecture is less sensitive to malfunctions; if one image capture device 1410 goes offline, many process points remain available for sampling.
In another embodiment, modules of the pulp analyzer 1420 are written in a scripting language to provide an open source architecture. Accordingly, the behavior and/or rules of the pulp analyzer 1420 can be customized to meet the particular needs of a user or implementation context. For instance, the pulp analyzer 1420 can be customized such that tear index is determined for samples from one process point of a mill, while tensile index is determined for samples from another process point. Moreover, scripts can be written to tailor operation of the control system of the mill. Embodiments herein enhance the flexibility of the control system, as well as reduce maintenance costs in a mill.
The following exemplary disc gap controller rule may be implemented in the pulp analyzer 1420. The rule has logic that controls the disc gap depending on, among other things, freeness calculations made by the pulp analyzer 1420. More particularly, in the rule below, Chxx[t] is a time-based array used to store values; t=0 is the most recent time, t=1 is the time one second ago, and so on. Ch 82[0] is the most recent freeness (CSF) value. Ch83 is an internal timer; no control occurs until 90 seconds have elapsed (i.e., Ch83=90). Ch 26 is the consistency. Depending on the consistency and freeness values, freeness can be controlled by making adjustments to one or both disc gaps (aveCZ and aveFZ) by respectively adjusting the distributed control system parameters Ch84 and/or Ch85. In the rule, control is not allowed to occur unless, for example, the gaps are in range, production (e.g., the consist controller) is on, and/or vibration is low. Permit is a flag that, when set (Permit=1), indicates that these conditions (interlocks) are met and that the rule can be run.

-- Step 4) FREENESS CONTROLLER

--

if Permit>0 then

if Ch83[0] < 90 then

Ch83[0] = Ch83[1] + 1

else

if Ch26[0] > 55 then

Ch84[0] = aveCZ + 0.10

Ch85[0] = aveFZ + 0.10

Ch83[0] = 0

else

if Ch26[0] >= 50 then

if Ch82[0] >= 25 then

Ch85[0] =aveFZ + 0.05

Ch83[0] = 0

else

if Ch82[0] <= −25 then

Ch85[0] = aveFZ − 0.05

Ch83[0] = 0

end

end

else

if Ch82[0] >= 25 then

Ch84[0] = aveCZ + 0.10

Ch85[0] = aveFZ + 0.05

Ch83[0] = 0

else

if Ch82[0] >= 5 then

Ch84[0] = aveCZ + 0.04

Ch85[0] aveFZ + 0.02

Ch83[0] = 0

else

end

end

end

end

end

end
FIG. 16 shows a system 1600 according to an embodiment of the invention. The system 1600 is an exemplary implementation in a mill. In practice, the topology of a given implementation may depend on the specific configuration of the mill, the need to measure pulp at certain process points, cost issues, and other relevant factors. In the system 1600, image capture devices 1410 are respectively interfaced with pulp lines at a screen room 1610, a decker 1620, and a refiner 1630 of the mill. An image capture device 1410 also can be interfaced with a sampling reservoir in a laboratory 1640 of the mill. The image capture devices 1410 communicate with a pulp analyzer 1420 over a network 1430. The pulp analyzer 1420 creates virtual distributions and computes associated properties for the respective pulp samples. In one embodiment, properties are provided by the pulp analyzer 1420 about once per minute.
Image capture devices can be implemented in various ways. FIG. 17 is a block diagram of an image capture device 1700 according to an embodiment of the invention. In one embodiment, the image capture device 1700 has a relatively streamlined design to reduce manufacturing costs. For example, the image capture device 1700 may lack a freeness tester. Accordingly, relative to conventional technologies, the image capture device 1700 can be cost-effectively implemented at multiple sampling points in a mill. Moreover, the image capture device 1700 can be cost-effectively installed in both newer and older mills, as the image capture device 1700 may merely need access to one or more networks. Cumbersome supply lines may not be required by the image capture device 1700, which can be installed at or near process points of the mill.
The image capture device 1700 includes a circulation pump 1705, a dilution solenoid valve 1710, a camera interface module 1715, a light 1720, a camera 1725, a sample solenoid valve 1730, a drain solenoid valve 1735, a pump motor drive 1740, a network module 1745, an input/output module 1750, and a power supply 1755. Variations of the configuration of the image capture device 1700 are within the scope of embodiments of the invention. For instance, in an alternative embodiment, one or more of the components of the image capture device 1700 can be external to, and interfaced with, the image capture device 1700.
The image capture device 1700 interfaces with a source of pulp that is to be sampled, such as pulp transported in a supply pipe or hose. The pump 1705, pump motor drive 1740 (e.g., a variable speed drive), dilution solenoid valve 1710, sample solenoid valve 1730, and drain solenoid valve 1735 together may form a fluid subsystem within the image capture device 1700. This fluid subsystem interfaces with the source of pulp and circulates pulp to be sampled so that images thereof can be captured.
In one embodiment, the sampled pulp passes through a glass cell (not shown) about 10 to 30 mm wide and 0.5 to 3 mm between the walls thereof. As the pulp passes through the cell, the light 1720 (e.g., an LED) illuminates the fibers therein, creating a large contrast between the fibers and the associated background. The camera 1725 captures images of the fibers, which appear as darker regions in the captured images. In an exemplary implementation, the camera 1725 is a high-resolution CMOS or CCD camera compatible with the Camera Link connectivity standard for vision applications, which defines a communications interface. The camera 1725 may capture, for instance, between 10 to 100 frames per second. An exemplary image resolution may be 1200 by 1000 pixels or larger (e.g., 10,000 by 10,000 pixels), with a pixel size of 3 to 10 micrometers.
The camera interface module 1715 provides an interface between the camera 1725 and, for example, a network. In one embodiment, the camera interface module 1715 converts captured image data to packetized data, such as TCP/IP data. In one embodiment, the camera interface module 1715 can directly connect to a network. The camera interface module 1715 can be implemented with the iPORT PT1000-CL IP Engine offered by Pleora Technologies Inc. (Kanata, Ontario, Canada) or with other suitable hardware and/or software. Use of a camera interface module 1715 and associated software may allow compatible cameras to be interchangeably incorporated in the image capture device 1700. In particular, if a higher performance camera becomes available, that camera can replace the existing camera in the image capture device 1700.
The network module 1745 enables network communication between the image capture device 1700 and device(s) on the network. In one embodiment, the network module 1745 connects to a Megabit network. Control information, such as information to control the camera 1725 and/or the pump motor drive 1740, can be sent from a pulp analyzer to the network module 1745 over the Megabit network, while image data can be sent by the camera interface module 1715 to the pulp analyzer over a second (e.g., Gigabit) network. In such an embodiment, the network module 1745 and the camera interface module 1715 each may have a respective IP address. In other embodiments, a single network transports both control and image data transmitted between an image capture device and a pulp analyzer. In such an embodiment, fewer network-related modules may be implemented in the image capture device.
The input/output module 1750 may include one or more analog and/or digital input/output modules. The modules can be used to control, for example, the on/off state of the solenoid valves 1710, 1730, 1735; the on/off state of the pump 1705; the on/off state of the light 1720; and level indications from a sample reservoir (e.g., if the image capture device 1700 is operating in a laboratory setup). The modules may also provide an analog signal for the controlled speed of the pump 1705 and an analog signal for the actual speed of the pump 1705, for example. Depending on the particular implementation, some digital switches may be used to select other sample points if sample lines of the image capture device 1700 are multiplexed to multiple different sample points (e.g., inlet and outlet of a screen or a refiner).
FIG. 18 is a perspective view of an image capture device 1800 according to an embodiment of the invention. The image capture device 1800 includes a housing 1810, a sampling module 1820, and an electronics module 1830. In an embodiment, the sampling module 1820 and/or the electronics module 1830 may be hotswapped with respective replacement modules. The image capture device 1800 is an exemplary implementation of the image capture device 1700 of FIG. 17.
The housing 1810 receives the sampling module 1820 and electronics module 1830, which in one embodiment are separate self-contained modules. In the illustrated embodiment, the sampling module 1820 and electronics module 1830 are contained in separate drawers that are receivable by the housing 1810. FIG. 19 is a perspective view of the image capture device 1800 showing the electronics module 1830 partially pulled out of the housing 1810.
FIG. 20 is an exploded view of the housing 1810. As shown, the housing 1810 includes slotted members 2010 to receive the sampling module 1820 and the electronics module 1830. In an exemplary implementation, the housing 1810 is sized to facilitate placement at various process points of a mill. For instance, the housing 1810 may be approximately 19 inches wide, 6 inches tall, and 9 inches deep.
In one embodiment, the sampling module 1820 includes certain fluid-related components of the image capture device 1800, and the electronics module 1830 includes certain electronics-based components of the image capture device 1800. For instance, the sampling module 1820 can include a pump, light, camera, camera interface, and solenoid valves. The electronics module 1830 can include a pump motor drive, input/output modules, power supply, and network modules. It can be appreciated that, by segregating certain fluid-related and electronics components into separate compartments, replacement of modules can more readily and cost effectively occur in the event of a malfunction. Moreover, the need to repair or replace the entire image capture device 1800 is reduced.
FIG. 21 is an exploded view of subcomponents 2100 of the sampling module 1820. The subcomponents 2100 include a camera 2110, a lens 2120, a sample cell 2130, and an LED back light 2140.
FIG. 22 is an exploded view of subcomponents 2200 of the sampling module 1820. The subcomponents 2200 include the camera 2110, the LED back light 2140, solenoid valves 2220, a camera interface module 2250, and a circulation pump 2230.
FIG. 23 is an exploded view of subcomponents 2300 of the electronics module 1830. The subcomponents 2300 include a pump motor drive 2310, components 2320, and a backplane 2330 with a connector. The connector may be, for example, an Advanced Module Format (AMF) connector that is spring-loaded for self-centering as the electronics module 1830 is slid into the chassis of the housing 1810. The components 2320 include input/output modules, a power supply, and network modules.
An image capture device can be used in a laboratory setting. FIG. 24 is an exploded view of a sampling reservoir 2400 that can be used in a laboratory setting to supply pulp to be analyzed to an image capture device. The sampling reservoir includes a sample cylinder 2410, level switches 2420, and valves 2430 that can be interfaced with the image capture device.
The embodiments described above are merely exemplary embodiments, and other embodiments can be practiced that fall within the scope of embodiments of the invention. For instance, three-dimensional techniques may be used in connection with processing of image data, creation of a virtual distribution, and/or analysis of a virtual distribution. In addition, finite difference or other appropriate analytical approaches may be used in lieu of, or in addition to, finite element analytical approaches such as described herein. Further, image capture devices described herein can be outfitted with additional functionality, such as freeness testers, if desired. It can also be appreciated that embodiments herein can be implemented in conjunction with other technologies used to improve processes in a mill setting.
As should also be apparent to one of ordinary skill in the art, the systems shown in the figures are models of what actual systems can be like. As noted, many of the modules and logical structures described are capable of being implemented in software executed by a microprocessor or a similar device or of being implemented in hardware using a variety of components including, for example, ASICs. Terms like “processor” may include or refer to both hardware and/or software. Furthermore, throughout the specification, where capitalized terms are used, such terms are used to conform to common practices and to help correlate the description with the examples and drawings. However, no specific meaning is implied or should be inferred simply due to the use of capitalization. Thus, the claims should not be limited to the specific examples or terminology or to any specific hardware or software implementation or combination of software or hardware.
Various features and advantages of the invention are set forth in the following claims.

Claims

1. A computer-implemented method of determining a property of pulp or paper, the method comprising:

receiving image data depicting a plurality of pulp fibers;

virtually distributing the pulp fibers over a predetermined area to create a virtual distribution of the pulp fibers; and

determining the property using the virtual distribution.

2. The method of claim 1, wherein the property relates to drainage.

3. The method of claim 1, wherein the property is tensile index or tear index.

4. The method of claim 1, wherein the pulp fibers are randomly virtually distributed.

5. The method of claim 1, wherein the predetermined area is two-dimensional.

6. The method of claim 5, wherein the predetermined area is a field of known radius.

7. The method of claim 1, further comprising processing the image data to produce descriptive information for the respective pulp fibers.

8. The method of claim 7, wherein the descriptive information comprises geometric parameters of the respective pulp fibers.

9. The method of claim 7, wherein the descriptive information comprises image representations of the respective pulp fibers.

10. The method of claim 1, further comprising controlling a component of a paper production system based at least in part on the determined property.

11. The method of claim 10, wherein the component is at least one of a refiner, a screen, and a paper machine.

12. The method of claim 1, further comprising storing, in a memory, an image representation of each pulp fiber and determined geometric parameters of that pulp fiber.

13. The method of claim 1, wherein the property is determined using a finite element analysis.

14. The method of claim 1, wherein the property is determined, at least in part, based on holes in the virtual distribution, the holes representing porosity of the virtual distribution.

15. A pulp fiber analyzer configured to receive image data depicting a plurality of pulp fibers, create a virtual distribution of the pulp fibers over a predetermined area, and determine a property of pulp or paper by analyzing the virtual distribution.

16. The pulp fiber analyzer of claim 15, wherein the pulp fiber analyzer comprises a computer configured to communicate over a network.

17. The pulp fiber analyzer of claim 15, wherein the pulp fiber analyzer is further configured to process the image data to produce descriptive information for the respective pulp fibers.

18. The pulp fiber analyzer of claim 15, wherein the pulp fiber analyzer is further configured to control a component of a paper production system based at least in part on the determined property.

19. A system for pulp fiber analysis, the system comprising:

an image capture device configured to send image data depicting a plurality of pulp fibers; and

an analyzer configured to

receive the image data from the image capture device,

create a virtual distribution of the pulp fibers depicted in the received image data, over a predetermined area, and

determine a property of pulp or paper by analyzing the virtual distribution.

20. The system of claim 19, further comprising a plurality of image capture devices configured to send, to the analyzer via a network, image data depicting a plurality of pulp fibers, the image capture devices being located at respective nodes of a paper production line.

21. An image capture device for a pulp fiber analysis system, the image capture device comprising:

a housing;

a first module comprising

at least one valve configured to interface with a source of pulp and admit pulp to be sampled,

a pump configured to circulate the sampled pulp,

a camera configured to produce image data of pulp fibers in the sampled pulp, and

a camera interface configured to interface with the camera and convert the image data to packetized network data, the packetized network data including digital image data; and

a second module comprising

a pump motor drive configured to drive the pump, and

an interface configured to send and receive data.

22. The image capture device of claim 21, wherein the first module is removably mounted in the housing.

23. The image capture device of claim 22, wherein the first module is self-contained and receivable by the housing.

24. The image capture device of claim 21, wherein the first module and the second module are separate self-contained modules.

25. The image capture device of claim 21, wherein the received data includes control data to control at least one function of the image capture device.

26. The image capture device of claim 21, wherein the image capture device has a unique network address in the network.

27. The image capture device of claim 21, wherein the image capture device lacks a freeness tester.

28. The image capture device of claim 21, wherein the camera is a CCD camera.

29. A method of implementing a graphical user interface for a pulp fiber analyzer application, the method comprising:

displaying a plurality of user-selectable elements through which input of a user can be received;

displaying a representation of a virtual distribution of pulp fibers, the virtual distribution being generated over a predetermined area based at least in part on analysis of image data depicting a plurality of pulp fibers; and

displaying a representation of a property of pulp or paper determined using the virtual distribution.

30. The method of claim 29, wherein the representation of the virtual distribution has a plurality of colors indicative of differences among the pulp fibers.

31. The method of claim 29, wherein the property relates to freeness.

32. A computer-readable medium having a plurality of processor-executable instructions for:

receiving image data depicting a plurality of pulp fibers;

determining a property of pulp or paper using the virtual distribution.

33. A system for pulp fiber analysis, the system comprising:

an image capture device configured to send image data depicting a plurality of pulp fibers;

means for virtually distributing the pulp fibers over a predetermined area to create a virtual distribution of pulp fibers; and

means for determining a property of pulp or paper using the virtual distribution.