Patent Application
20070158040
The present invention relates to the field of pulp and paper process automation, and more particularity to methods for estimating and controlling optimal dosage of bleaching agent to be used in a process for producing pulp of a required brightness value from wood chips.
Thermomechanical pulp properties and quality are influenced by two types of variables: feed material (chips) and process (refiner). Over the years, many researchers have underscored the impact of the stability of the refiner operation for the production of constant pulp quality, as mentioned by Strand, B. C. in “The Effect of Refiner Variation on Pulp Quality”, International Mechanical Pulping Conference, Proceedings, 125-130 (1995). However, variations of the process itself are mainly related to variations in the raw material feeding the system as, mentioned by Wood, J. A. in “Chip Quality Effects in Mechanical Pulping—a Selected Review”, 1996 TAPPI Pulping Conference, Proceedings, 491-497 (1996). In particular, pulp brightness is considered as an important quality requirement, as discussed by Dence, C. W. et al. in “Pulp Bleaching—Principles and Practice”, TAPPI Press, 457-490 (1996).
A main object of the methods, apparatus and system according to the invention is to estimate the optimal dosage of bleaching agent for the purpose of control thereof in a pulp production process, by modeling the relationship between the quality of the chips feeding the process with an important pulp and paper resulting property, namely pulp brightness. In particular, the model is used to evaluate the minimum charge of peroxide required to reach certain level of pulp brightness according to possible chips properties fluctuations, in order to minimize the cost and environmental impact of the bleaching operation.
According to the above mentioned object, from a broad aspect of the invention, there is provided a method for estimating an optimal dosage of bleaching agent to be used in a process for producing pulp of a required brightness value from wood chips. The method comprises the step of: i) estimating a set of wood chip properties characterizing said wood chips to generate corresponding wood chip properties data, said set including reflectance-related properties; said method being characterized by further comprising the steps of: ii) providing an initial dosage value of said bleaching agent; and iii) feeding said wood chip properties data and said bleaching agent dosage value at corresponding inputs of a predictive model for generating predicted brightness value of pulp to produce from said wood chips, to estimate the optimal bleaching agent dosage for which said predicted brightness value substantially reaches said required brightness value.
According to the same object, from another aspect of the invention, there is provided a method of controlling the bleaching of pulp in a pulp production process on the basis of the optimal bleaching agent dosage estimated according to the above mentioned estimation method, said pulp production process including, between said steps i) and iii), at least one processing step including a step of refining said wood chips to produce refined wood chips. The control method comprises the step of: a) adding bleaching agent to said refined wood chips according to said optimal bleaching agent dosage to produce said pulp.
According to the same object, from another aspect of the invention, there is provided a method of controlling the bleaching of pulp in a pulp production process on the basis of the optimal bleaching agent dosage estimated according to the above mentioned estimation method, said pulp production process including, between said steps i) and iii), at least one processing steps including a step of refining said wood chips to produce refined wood chips. The control method comprising the step of: a) estimating a resulting brightness value of the pulp according to a time delay following said predicted brightness value generation; b) comparing said predicted brightness value with said resulting brightness value to generate further error data; c) further optimizing said bleaching agent dosage value to minimize said further error data; and d) adding bleaching agent to said refined wood chips according to said further optimized bleaching agent dosage to produce said pulp.
According to the same object, from another aspect of the invention there is provided an apparatus for estimating an optimal dosage of bleaching agent to be used in a process for producing pulp of a required brightness value from wood chips. The apparatus comprises means for estimating a set of wood chip properties characterizing said wood chips to generate corresponding wood chip properties data, said set including reflectance-related properties. The apparatus is characterized by further comprising: data processor means implementing a predictive model receiving at corresponding inputs thereof said wood chip properties data and an initial bleaching agent dosage value for generating predicted brightness value of pulp to produce from said wood chips, to estimate the optimal bleaching agent dosage for which said predicted brightness value substantially reaches said required brightness value.
According to the same object, from another aspect of the invention there is provided a system of controlling the bleaching of pulp in a pulp production process on the basis of the optimal bleaching agent dosage estimated by the above mentioned apparatus, said pulp production process including at least one processing steps including a step of refining said wood chips to produce refined wood chips. The control system comprises means for adding bleaching agent to said refined wood chips according to said optimal bleaching agent dosage to produce said pulp.
According to the same object, from another aspect of the invention there is provided a system for controlling the bleaching of pulp in a pulp production process on the basis of the optimal bleaching agent dosage estimated by the above mentioned apparatus, said pulp production process including at least one processing steps including a step of refining said wood chips to produce refined wood chips. The control system comprises means for estimating a resulting brightness value of the pulp according to a time delay following said predicted brightness value generation by said predictive model; means for time delaying said predicted brightness value according to said time delay; means for comparing said delayed predicted brightness value with said resulting brightness value to generate further error data; said predictive model further optimizing said bleaching agent dosage value to minimize said further error data; and means for adding bleaching agent to said refined wood chips according to said further optimized bleaching agent dosage to produce said pulp.
The methods, apparatus and system according to the present invention will be described in detail with reference to the accompanying drawings in which:
The methods for estimating an optimal dosage of bleaching agent of the present invention being based on the estimation of properties of wood chips that must have significant effect on the bleaching characteristics of the pulp made therefrom, an experimental protocol used to qualify wood chip properties to be preferably used in modeling will be presented first. In order to define the parameters used for the model, two sets of experiments corresponding to two different blocks were performed. In the first block, a potential mix of four species, black spruce, balsam fir, jack pine and white birch, was studied. The last two species were chosen because they represent a potential source of new resources. The trees have been selected, cut, barked and chipped in order to obtain standard chips with known and controlled age. In fall, outdoor stacks of each species of chips were prepared. During the following 12 months, six samples were selected in order to conduct the experimental plan for chips aging as described in table 1.
In each sample, the experiments for 100% black spruce and 100% balsam fir were repeated twice in order to evaluate the experimental error and two additional tests for 100% jack pine and 100% birch (12 runs in each sample). The six samples allow to evaluate the evolution of the quality, i.e. degradation, of the chips in time. This degradation is highly dependent on storage temperature. The first four samples were evaluated at an interval of three weeks. After that, there has been a longer waiting time. It was noticed that the winter degradation of each stack was extremely slow.
The second block of experiments was used to investigate the effects of other important variables regarding pulp quality. This second block of experiments has been conducted with four variables: species (black spruce, balsam fir), density (high, low), initial dryness of the chips (fresh, dry), and thickness of the chips (0-4 mm, 4-8 mm). Table 2 describes the experiments for chips aging that were conducted in this second block.
For the purpose of the experiment, the estimation and control method according to the invention was applied to a batch pulp production process. Refining was conducted on a pilot unit Metso CD-300. Each sample was washed and refined in two stages. The first one was conducted at a temperature of 128° C. and the second one at atmospheric pressure. For each experiment, pulps with a freeness ranging from 200 to 150 mL were selected for further peroxide bleaching, which fundamental principles are briefly described next.
It is generally accepted that the active mechanism in chromophore elimination with hydrogen peroxide as bleaching agent involves the perhydroxyl ion OOH−. As taught by Sundholm, J. in “Papermaking Science and Technology—Mechanical Pulping”, Finnish Pulp and Paper Research Institute, 313-345 (1999), hydrogen peroxide bleaching is therefore performed in alkaline systems to produce the active ion according to the following equation:
H2O2+OH−→OOH−+H2O (1)
The formation of the perhydroxyl anion can be enhanced by increasing the pH or by increasing the temperature. Hydrogen peroxide readily decomposes under bleaching conditions according to the following equation:
2H2O2→O2+H2O (2)
Sodium silicate and magnesium silicate are normally added to the bleach liquor to stabilize peroxide. Transition metals ions like iron, manganese and copper catalyze peroxide decomposition. In order to prevent
That, before bleaching with peroxide, the pulp was pretreated with 0.2% of DTPA. The pretreatment of the pulp was done at 60° C., 15 minutes and 3% of consistency.
Different concentrations of hydrogen peroxide varying from 1 to 5% (O.D. basis) were tested for bleaching the different pulp. Table 3 describes the experimental conditions used for the peroxide bleaching of the pre-treated pulps.
where:
Bleaching was conducted at 70° C., 180 minutes and 12% of consistency. The bleaching liquor was composed of 3.00% of sodium silicate, 0.05% of magnesium sulfate, hydrogen peroxide and sodium hydroxide. After the bleaching step, the pulp was diluted at 1% of consistency and neutralized with sodium metabisulfite at pH 5.5. A volume of the bleaching liquor was kept to measure the residual peroxide by an iodometric dosage. Optical properties such as ISO brightness and color coordinates (L*, a*, b*) have been measured according to Paptac standard.
Chips of the eighty four (84) runs in block 1 and twenty (20) runs in block 2 were systematically analyzed using a wood chip optical inspection apparatus known as CMS-100 chip management system commercially available from the present assignee, Centre de Recherche Industrielle du Quebec (Ste-foy, Canada), for measuring a number of optical properties as well as moisture content. Such wood chip inspection apparatus is described in U.S. Pat. No. 6,175,092 B1 issued on Jan. 16, 2001 to the present assignee. Such multi-sensor system includes main and optional auxiliary sensors able to characterize wood chips online. The main sensors include artificial vision sensor (an RGB color camera) and near infrared sensor to measure chip brightness and moisture content. Auxiliary sensors such as a distance sensor and an air conditions sensor to measure air temperature and relative humidity may be advantageously used. They provide information that extends measurements of the main sensor to stabilize the system (for example, variations of the camera measuring distance will influence the chip brightness measurement). The system will work on frozen and non-frozen wood chips, and it used for predicting bleach charges or dosage based on chip quality for use as a bleach control method or system. The correlation between some chip properties and its possible application in bleach control is discussed by Ding, F. et al. in “Economizing the Bleaching Agent Consumption by Controlling Wood Chip Brightness”, Control System 2002, Proceedings, June 3-5, Stockholm, Sweden, 205-209 (2002). The most relevant wood chips properties measurements for the purpose of the present invention are described next.
A first measurement relates to chip luminance, wherein the brightness of black is defined as zero and the brightness of white as 150. The RGB colour camera of the system is calibrated by a color checker made of black and white paperboard. The wood chip color is between white and black, so its brightness is between 0 and 150. A second measurement relates to chip average moisture content. The system includes a near infrared sensor such as model NDC 55 supplied by Korins Co. Ltd. (Korea), that is used to measure surface moisture content of wood chips, without any non-contact therewith. A method for estimating surface moisture of wood chips that can be used for the purpose of the estimation method of the present invention is disclosed by Ding, F. et al. in “Wood Chip Physical Quality Definition and Measurement”, IMPC Proceedings, June 2-5, Québec, Canada, 367-373 (2003). A phenomenological model may also be used to calculate the average moisture content from surface moisture content, as described by Ding, F. et al. in “Economizing the Bleaching Agent Consumption by Controlling Wood Chip Brightness”, Control System 2002, Proceedings, June 3-5, Stockholm, Sweden, 205-209 (2002). Other measurements may be obtained from various further sensors, generating a large amount of data categorized in many different variables. According to the present preferred embodiment, a number of four (4) other measurements are considered, namely the image “H”, “S” and “L” parameters, as well as a chip average size estimation, which may be obtained using an imaging-based, chip size classifier such as the ScanChip™ system supplied by Iggesund Tools Inc. (Oldsmar, Fla., USA). Alternatively, a sampling-based size estimation method according to a known standard such as William size classifying protocol may be used to provide chip size data. Other color imaging standard measurements such as “R G B” or “LAB” may be also used to characterize reflectance-related characteristics of wood chips.
The database resulting from the various experiments gives rise to three (3) types of variables: chip properties coming from the measuring system, operational parameters of the TMP and bleach processes, and pulp quality characteristics. Overall, the database used contained a large number (n=178) of variables distributed over a corresponding number of columns. Because all (104) runs for both blocks produced pulps which were bleached at four (4) different peroxide charges, the database also contains four times (416) runs distributed over a corresponding number of lines. In order to capture possible system measurements errors, the database contained many repeated measurements for the same chips, leading a final database containing a still greater number (506) of data lines. In the following sections, the techniques that are preferably used to screen the columns of data to a reasonable amount of most relevant variables and to use the lines for neural network training will be explained. Both techniques are done with the objective of obtaining a good enough pulp brightness model that could be used in a brightness control strategy.
The data screening to perform the selection of the independent variables which have an effect on the dependent variables that have been measured is preferably done using known PLS (Projection on a Latent Structure) modeling.
The correlation coefficients for each dependent variable are presented in
A neural network-based predictive model that can be used to carry out the method according to the invention will now be described in reference to
In operation, according to the set of wood chip properties characterizing the wood chips as estimated by the measurement system, corresponding wood chip properties data are fed at respective inputs 18 of neural network 12, as well as an initial dosage value of the bleaching agent (peroxide) at further input 20. Although input 20 is preferably used to receive bleaching agent dosage as actually fed to pulp as typically calculated from a flow meter measurement at bleaching unit outlet, knowing agent concentration and pulp weight, the initial dosage may be set to a predetermined value for the purpose of initializing the prediction model. In turn, the neural network 12 generates at output 22 thereof, a predicted brightness value for pulp to produce from the inspected wood chips. Then, the brightness predicted value is compared with the required brightness value to generate error data, as indicated at node 24. In turn, the error data is used by an optimization module 26, which optimizes the bleaching agent dosage value to minimize the error data. Finally, the above prediction, comparison and optimization steps are repeated with the optimized bleaching agent dosage value as fed back to the network 12 at input 25 thereof, until the brightness predicted value substantially reaches the required brightness value, to estimate the optimal bleaching agent dosage. In other words, the peroxide charge is tuned to minimize the error, while maintaining constant chip properties, and an optimization loops is performed in model 10 for several iterations before it reaches the peroxide charge that meets the required brightness value or set point according to the neural network model prediction. When this optimal value has been found, it can be sent back to the actual process through control switch 27 and control input 28 of bleaching unit 26 for corrective action on a control valve (not shown) provided on bleaching unit 26. Such control strategy assumes that the time taken for the optimization to take place is less than the frequency at which brightness set points will be modified, which is a reasonable assumption.
Because the brightness prediction is based upon variables that are algorithmic transformations of camera signals, a first simulation was designed in which the neural network model was used in conjunction with an optimizer that would find the best combination of measurement system input variables that would give the best achievable brightness. Simulation results are shown in table 4.
There are two main observations from this result. First, for optimal brightness all independent variables are either at the minimum or maximum values or their respective span. This means that the hyper surface for which a minimum was found slants towards an intersection of the constraint hyper planes corresponding to the maximum or minimum values of each independent variable. This also means there is a well-defined combination for maximum brightness (the optimal combination was consistently reproduced for many different simulation trials). Second, we see that five (5) of the six (6) system measurements give the best pulp brightness when they are at their higher values, except for the “H” parameter (lowest value).
Turning back to
It turns out that peroxide has a predominant effect. In fact, the peroxide charge fixes the brightness level and changes in the chip properties simply add small variations around the level attained. Every system measurement variable, when bumped independently within its full span, contributes to small percentage of change around the brightness level dictated by the peroxide charge.
In order to illustrate brightness control feature, a first set of simulation results is shown in table 6, representing the effect of chip quality on peroxide charges to achieve different brightness set points.
All measurement system parameters (chip properties) were maintained at their average value and brightness set point was bumped from 55 to 75 by increments of (five) 5 points. For these “average” chips, one can see that a 38% increase in peroxide (from 2.22 to 4.12%) is required to increase the brightness level from 65 to 70 points (13.7%), and that a further 17.6% increase (from 4.12 to 5%) is required to gain only 1 brightness point (from 70 to the maximum achievable 71) Doing the same thing with the theoretical best possible chips as per table 4, one can note that no peroxide is required until a brightness set point close to 65 is desired. Also, a 71 brightness is achievable with only 1.48% peroxide. Finally, further gains in brightness points from 71 to 75 are only obtained at a high peroxide cost (from 1.48 to 4.96%). Because one cannot assume that chips with such properties actually exist, the chip properties contained in the above-mentioned database were also used, which returned the best brightness value at 72.46. In this case, the better chip properties still reduce peroxide consumption for the same brightness level, but to a lesser extent.
A bleaching agent control system according to another embodiment of the invention, which is particularly adapted for controlling a bleaching operation as part of a continuous pulp production process will now be described with reference to
Referring again to
When using the methods, apparatus and system according to the invention, the same brightness set point can be achieved at lower bleaching agent charges when the chip quality increases. The method may be useful to assist chip management in the mill, or in the context of internal model control (IMC) or model predictive control (MPC) strategies. It is to be understood that dosage of other bleaching agents such as hydrosulfites may also be performed with the method of the invention.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2,447,098 | Oct 2003 | CA | national |
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/CA04/01888 | 10/28/2004 | WO | 4/27/2006 |
