Method and apparatus for estimating an optimal dosage of bleaching agent to be used in a process for producing pulp

Abstract

A method and an apparatus for estimating an optimal dosage of bleaching agent to be used in a process for producing pulp of a required brightness value involve a set of wood chip properties characterizing the wood chips as estimated by a measurement system. Corresponding wood chip properties data are fed at the inputs of a predictive model (10) including a neural network (12), as well as an initial dosage value of the bleaching agent. The predictive model (10) generates a predicted brightness value of pulp to produce from the inspected wood chips, to estimate the optimal bleaching agent dosage for which the predicted brightness value substantially reaches the required brightness value. A method and system for controlling the bleaching of pulp are respectively based on the same estimation method and apparatus.

Description

FIELD OF THE INVENTION

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.

BACKGROUND OF INVENTION

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).

SUMMARY OF INVENTION

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.

BRIEF DESCRIPTION OF THE DRAWINGS

The methods, apparatus and system according to the present invention will be described in detail with reference to the accompanying drawings in which:

FIG. 1 is a graph showing relative importance index of independent variables according to PLS analysis;

FIG. 2 is a graph showing coefficient of correlation for dependent variables by PLS analysis;

FIG. 3 is a graph representing observed and predicted values for ISO brightness; and

FIG. 4 is a block diagram of a bleaching agent control system according to a first embodiment of the invention, which includes an estimation apparatus based on a neural network-based predictive model;

FIG. 5 is a block diagram of 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.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

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.

TABLE 1
Test number	Spruce %	Balsam fir %	Jack pine %	Birch %
1	0	0.2	0.4	0.4
2	1	0	0	0
3	0	1	0	0
4	0.6	0	0	0.4
5	0	0.6	0.4	0
6	0.6	0	0.4	0
7	0	0.6	0	0.4
8	0.2	0	0.4	0.4
Following are repetitions for experimental error determination
9	1	0	0	0
10	0	1	0	0
Following are additional tests
11	0	0	1	0
12	0	0	0	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.

	TABLE 2
	Large chips	Small chips
	(4-8 mm)	0-4 mm
INITIAL CHIPS
Spruce at low density	Test no. 1	Test no. 2
Balsam fir at low density	Test no. 3	Test no. 4
Spruce at high density	Tests no. 5 and 6	Test no. 7
Balsam fir at high density	Test no. 8	Tests no. 9 and 10
Aged chips (dryness of 75%)
Spruce at low density	Test no. 11	Tests no. 12 and 13
Balsam fir at low density	Tests no. 14 and 15	Test no. 16
Spruce at high density	Test no. 17	Test no. 18
Balsam fir at high density	Test no. 19	Test no. 20

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.

	TABLE 3
	Parameters
	P1	P2	P3	P5
Temperature, ° C.	70	70	70	70
Retention time, min	180	180	180	180
Consistency, %	12	12	12	12
Sodium silicate, %	3.00	3.00	3.00	3.00
Magnesium sulfate, %	0.05	0.05	0.05	0.05
Total Alcali Ratio	2.00	1.20	0.90	0.80
Sodium hydroxyde, %	1.66	2.06	2.36	3.66
Hydrogen peroxide, %	1.00	2.00	3.00	5.00

where: $\begin{array}{cc}\mathrm{Total}\mathrm{Alcali}\mathrm{Ratio}=\frac{\%\mathrm{Hydrogen}\mathrm{Peroxide}}{\%\mathrm{OH}-\mathrm{given}\mathrm{by}\mathrm{sodium}\mathrm{silicate}\mathrm{and}\mathrm{sodium}\mathrm{hydroxide}}& \left(3\right)\end{array}$
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. FIG. 1 presents the independent variables that have been chosen according their relative importance index. As expected, the parameters, which have most impact and are correlated to dependent variables, are the concentration of sodium hydroxide (NaOH) and the concentration of hydrogen peroxide (PEROA). After that, the variables CO_moy (chip size), MDH (average of H), MMLC (average of luminance), MDS (average of S), MDL (average of L) and MSURFM (average of the surface moisture) also contribute to a lesser extent to the bleached pulp properties response.

The correlation coefficients for each dependent variable are presented in FIG. 2. The value R2 shows the correlation for the dependent variables. It is an indication of how well the model can fit the experimental data. The value Q2 shows the correlation of the interpolated responses, i.e. predictions not part of the experimental data. The graph shows that the model is adequate to predict ISO brightness [ISOB] (coefficient of correlation of 0.88), color coordinates L*[LB] (coefficient of correlation of 0.92) and a*[AB] (coefficient of correlation of 0.90), and residual peroxide [PEROR] (coefficient of correlation of 0.83). As for the color coordinate b*[BB], the coefficient of correlation is only 0.60. We also note that chemical properties such as MEXT (extractives) and AGR (fatty and resinic acids) are difficult to correlate (coefficients of correlation of 0.33 and 0.26 are respectively obtained).

FIG. 3 presents observed and predicted values for the ISO brightness. These results show that the model is able to predict adequate values for this optical property. Brightness ranging from 43.79% to 80.2% was measured on the bleached pulps.

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 FIG. 4. It is to be understood that any appropriate modeling technique such as neural network, PLS, Model Predictive Controller (MPC), regression, state space matrix, FRI, fuzzy logic, genetic algorithm, or a combination thereof can be used to obtain a predictive model for the purpose of the present invention. Some of those known predictive modeling techniques are discussed by Quian, X. et al. in “Mechanistic Model for Predicting Pulp Properties from Refiner Operating Conditions” TAPPI Journal, 78 (4) (1994); by Qian, Y. et al. in “Fuzzy Logic Modeling and Optimization of a Wood Chip Refiner” TAPPI Journal, 77 (2) (1995), and by Qian, Y. et al. in “Modeling a Wood-Chip Refiner Using Artificial Neural Networks, TAPPI Journal, 78 (6): 167-174 (1995). The predictive model generally designated at 10 and as readily implemented in a data processing device such as a computer (not shown) provided on the bleaching agent dosage estimating apparatus and bleaching control system represented in FIG. 4, preferably includes a neural network 12 that was previously trained according go to the experimentally obtained data on wood chip properties and on dosage of said bleaching agent as described above, i.e. over the nine (9) remaining database columns consisting of eight (8) inputs identified by PLS method as shown in FIG. 1, and one output, namely pulp brightness as shown in FIG. 3. Such known neural network and associated training approach are discussed by Laperriere L. et al. in Modeling and simulation of pulp and paper quality. After a few unsuccessful training trials, it was noticed that the input NaOH is always a ratio of the input H2O2, so it was eliminated from the training set. Out of the available (506) training lines, a selected number (96) were removed (about 20%) and injected back to the trained network for validation. Different sets of the removed 20% were tested and gave similar results. The final configuration was a 7-5-1 neural network (7 inputs, 5 hidden neurons and 1 output) as designated at 12 in FIG. 4. Training was stopped after an average absolute mean error of 5% was reached between the neural network prediction and the training output brightness value for each of the 506 lines. The value of 5% was chosen by taking two factors into consideration: 1) reliability of the output measurements: the experimental error related to the brightness value is about 3%, i.e. ±0.5 brightness points in the experimental span of 43.79 to 80.2 measured brightness; and 2) reliability of the input measurements: calibration errors may encourage an increase of the training error. The training results of the final network, in terms of the connection weights between each of its constituting neurons, were imported into the neural network 12 of model 10, in the form of a computer program that can be implemented in a microcomputer by any person skilled in the art using well-known programming tools. Such program is able to simulate brightness prediction based on the seven (7) chosen inputs, namely reflectance-related properties of wood chips that are Luminance, M, H, S, L and chip size from measurement system 14 as part of the bleaching agent dosage estimating apparatus, and bleaching agent dosage (peroxide charge) value used by the bleaching unit 16 as part of the bleaching control system, to add a corresponding volume of bleaching agent solution into the pulp made of refined wood chips to produce bleached pulp. Optionally, unmodeled disturbances may also be applied to the neural network at input 17

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.

TABLE 4
Variable	Min. experimental value	Max. experimental value
Luminance	13.23	57.93
Moisture	19.54	70.34
Size	16.49	21.39
H	19.29	224.44
S	82.08	192.89
L	9.17	66.85
H2O2	0.00	5.00

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 FIG. 1, the sensitivity of output properties with respect to some of the chosen inputs is shown. Because this result was obtained from a PLS model and the use a neural network model is contemplated, another set of simulations was run to verify if the sensitivity of the sole pulp brightness to each independent variable would be similar. Table 5 shows a series of tests where each variable was given sinusoidal swings of its value over its total experimental span as shown in table 4, while maintaining other variables at their central values, to show brightness sensitivity to the independent variables.

TABLE 5
Variable	Min. brightness	Max. brightness	% change in brightness
Luminance	63.55	68.02	12.3
Moisture	64.52	67.01	6.8
Size	64.60	67.48	7.9
H	63.81	67.71	10.7
S	63.99	67.69	10.1
L	65.18	66.52	3.6
H2O2	49.57	70.83	58.4

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.

TABLE 6
		Peroxyde
	Peroxyde	charge (%)
Brightness	charge (%)	best
set point	average quality	theoretical	Peroxyde charge (%) best
(%)	chips	chips	experimental chips
55	0.77	0.0	0.15
60	1.41	0.0	0.76
65	2.22	0.35	1.48
70	4.12	1.21	2.92
71	4.99	1.48	3.54
75	Unachievable	4.96	Unachievable
	(max 71%)		(max 72%)

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 FIG. 5. Such continuous pulp production process includes, between raw wood chips supplying step, where the wood chips properties are measured, and bleaching step at least one step consisting of refining chips, and generally a plurality of other processing or handling steps each being characterized by a specific processing time, involving equipment such as chests and towers for performing process functions such as storage, mixing and transfer on various pulp matter such as accepted pulp, unrefined reject, refined reject, screened reject, etc. In order to adequately estimating and controlling the bleaching dosage for such continuous pulp production process, the estimated wood chips properties may be used by the predictive model according to the invention only if the time delay between wood properties estimation and bleaching steps as induced by the intermediate processing steps is considered by the bleaching agent estimation method. For so doing, the bleaching agent estimation apparatus and bleaching control system shown in FIG. 5 is provided with a time delay module 30 receiving all wood chip properties data, namely luminance, moisture content”, H, S, L and size data, to apply thereto a time delay value, which can be either a fixed value in the case of a simple process involving few intermediate processing steps, or a calculated value in the case of a more complex process, using input/output process parameters including pulp consistency, pulp weight/mass flow rate, chest/tower volume and filling levels, etc. For so doing, basic dynamic calculation or a more advanced modeling technique such as neural networks, fuzzy logic, genetic algorithms, or a combination thereof along with active mass balance data may be used by any person skilled in the computer programming for the purpose of implementing the desired time delay. Moreover, the processing chest/towers used in the process between chip properties estimation and bleaching operation induce an attenuation of the actual wood chip properties, and therefore, the estimated wood chip properties data must be filtered accordingly, preferably using an attenuation filter 32 receiving the delayed chip properties data from time delay 30, and feeding the resulting chip property data to inputs 18 of the predictive model 10. Here again, basic dynamic calculation or a more advanced modeling technique may be used by any person skilled in the computer programming for the purpose of implementing the desired attenuation filtering. In a case where the bleaching unit discharge is located on the main pulp line containing accepted pulp and treated reject pulp, some difference in properties will be observed between accepted pulp and treated reject pulp, which difference will be influenced by the rejected pulp treating rate as well as the delay induced by each chest involved. Such pulp properties difference may be also compensated in a similar manner as described above, using either dynamic calculation or an advanced modeling technique.

Referring again to FIG. 5, it can be seen that the model 10, in addition to receiving the required brightness (set point) at input 34 thereof in a same manner as explained above with reference to the embodiment shown in FIG. 4, receives further error data at input 40, in order to further improve bleaching dosage estimation, as will now be explained in detail. Since the optimal dosage estimation is based on a prediction by the model 10 of the resulting, final brightness of the bleached pulp, an estimation of the resulting, actual pulp brightness as obtained either with an online measurement sensor (not shown) or following an off-line analyzing procedure on a pulp sample (Paptac standard), is made according to a time delay following the predicted brightness value generation by the predictive model 10. Conveniently, such measurement may be made at the outlet of bleaching chest/tower, or be made at the scanning station of the paper machine by implanting an appropriate model considering active mass balance data and respective chip properties data associated with the various pulp matters used to produce the paper. A time delay 36 is provided for delaying the predicted brightness value according to the time delay, which may be either a fixed value or a calculated value obtained through dynamic calculation or advanced modeling in a similar manner as explained above. A comparator 38 is provided for comparing the delayed predicted brightness value with the resulting brightness value to generate further error data that is fed back to input 40 of predictive model 10, which can further optimize the bleaching agent dosage value to minimize further error data. Upon receiving further optimized bleaching dosage data generated by the model 10, the bleaching unit 28 is caused to discharge a corresponding amount of bleaching agent solution, and the applied dosage measurement is fed back to the input 20 of model 10 in a same manner as explained before with respect to the embodiment shown in FIG. 4. The bleaching agent addition control function may be conveniently performed through a model implemented in the data processing device, generating one or more mass flow set points for the bleaching agent so as to better regulate the process.

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.

Claims

1. 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, said method comprising 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 (10) 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.

2. The method according to claim 1, wherein said set of wood chips properties further includes wood chip size.

3. The method according to claim 1, wherein said set of wood chips properties further includes moisture.

4. The method according to claim 1, wherein said predictive model estimate the optimal bleaching agent dosage by performing the steps of:

a) comparing said brightness predicted value with said required brightness value to generate error data;

b) optimizing said bleaching agent dosage value to minimize said error data; and

c) repeatedly generating predicted brightness value and performing said steps a) and b) with the optimized bleaching agent dosage value until said predicted brightness value substantially reaches said required brightness value, to estimate said optimal bleaching agent dosage.

5. The method according to claim 1, wherein said predictive model includes a neural network (12) previously trained according to experimentally obtained data on said wood chip properties and on dosage of said bleaching agent.

6. 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 method of claim 1, 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, said control method comprising the step of:

a) adding bleaching agent to said refined wood chips according to said optimal bleaching agent dosage to produce said pulp.

7. The method according to claim 6, wherein said pulp production process is continuous, said set of wood chip properties data are filtered according to an attenuation of said estimated wood chip properties caused by said at least one processing step.

8. The method of claim 6, further comprising the step of:

b) repeating said step i) to generate wood chip properties data corresponding to a new estimation of the set of wood chip properties characterizing said wood chips;

c) repeating said step iii) with said optimal bleaching dosage to provide a new estimation of the optimal bleaching dosage; and

d) adding bleaching agent to said refined wood chips according to said new estimation of bleaching agent dosage to produce said pulp.

9. 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 method of claim 1, 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, said 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.

10. 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, said apparatus comprising:

means (14) 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;

said apparatus being characterized by further comprising:

data processor means implementing a predictive model (10) 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.

11. The apparatus according to claim 10, wherein said predictive model (10) includes:

a) means (24) for comparing said brightness predicted value with said required brightness value to generate error data; and

b) means (26) for optimizing said bleaching agent dosage value to minimize said error data.

12. The apparatus according to claim 10, wherein said predictive model (10) includes a neural network previously trained according to experimentally obtained data on said wood chip properties and on dosage of said bleaching agent.

13. 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 apparatus according to claim 10, said pulp production process including at least one processing steps including a step of refining said wood chips to produce refined wood chips, said control system comprising means (16) for adding bleaching agent to said refined wood chips according to said optimal bleaching agent dosage to produce said pulp.

14. 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 apparatus according to claim 10, said pulp production process including at least one processing steps including a step of refining said wood chips to produce refined wood chips, said control system comprising:

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 (10);

means for time delaying said predicted brightness value according to said time delay;

means (38) for comparing said delayed predicted brightness value with said resulting brightness value to generate further error data;

said predictive model (10) further optimizing said bleaching agent dosage value to minimize said further error data; and

means (16) for adding bleaching agent to said refined wood chips according to said further optimized bleaching agent dosage to produce said pulp.

15. The system according to claim 14, wherein said pulp production process is continuous, said system further comprising means for filtering said set of wood chip properties data according to an attenuation of said estimated wood chip properties caused by said at least one processing step.