WO2005042832A1 - Procede et dispositif permettant l'estimation du dosage optimal d'agent de blanchiment pour un procede de production de pate a papier - Google Patents

Procede et dispositif permettant l'estimation du dosage optimal d'agent de blanchiment pour un procede de production de pate a papier Download PDF

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Publication number
WO2005042832A1
WO2005042832A1 PCT/CA2004/001888 CA2004001888W WO2005042832A1 WO 2005042832 A1 WO2005042832 A1 WO 2005042832A1 CA 2004001888 W CA2004001888 W CA 2004001888W WO 2005042832 A1 WO2005042832 A1 WO 2005042832A1
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Prior art keywords
bleaching agent
pulp
brightness value
wood chips
dosage
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PCT/CA2004/001888
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English (en)
Inventor
Mokhtar Benaoudia
Luc Laperriere
Pierre Bedard
Céline LEDUC
Claude Daneault
Laurier Morissette
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Centre De Recherche Industrielle Du Quebec
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Priority to CA002543781A priority Critical patent/CA2543781A1/fr
Priority to US10/577,434 priority patent/US20070158040A1/en
Publication of WO2005042832A1 publication Critical patent/WO2005042832A1/fr

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Classifications

    • D—TEXTILES; PAPER
    • D21—PAPER-MAKING; PRODUCTION OF CELLULOSE
    • D21C—PRODUCTION OF CELLULOSE BY REMOVING NON-CELLULOSE SUBSTANCES FROM CELLULOSE-CONTAINING MATERIALS; REGENERATION OF PULPING LIQUORS; APPARATUS THEREFOR
    • D21C9/00—After-treatment of cellulose pulp, e.g. of wood pulp, or cotton linters ; Treatment of dilute or dewatered pulp or process improvement taking place after obtaining the raw cellulosic material and not provided for elsewhere
    • D21C9/10—Bleaching ; Apparatus therefor
    • D21C9/1026—Other features in bleaching processes
    • D21C9/1052—Controlling the process
    • D—TEXTILES; PAPER
    • D21—PAPER-MAKING; PRODUCTION OF CELLULOSE
    • D21C—PRODUCTION OF CELLULOSE BY REMOVING NON-CELLULOSE SUBSTANCES FROM CELLULOSE-CONTAINING MATERIALS; REGENERATION OF PULPING LIQUORS; APPARATUS THEREFOR
    • D21C9/00—After-treatment of cellulose pulp, e.g. of wood pulp, or cotton linters ; Treatment of dilute or dewatered pulp or process improvement taking place after obtaining the raw cellulosic material and not provided for elsewhere
    • D21C9/10—Bleaching ; Apparatus therefor
    • D21C9/16—Bleaching ; Apparatus therefor with per compounds
    • D21C9/163—Bleaching ; Apparatus therefor with per compounds with peroxides

Definitions

  • the present invention relates to the field of pulp and paper process automation, and more particularly 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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:
  • 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:
  • 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
  • the pulp was pretreated with 0,2% of DTPA.
  • the pre-treatment 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.
  • 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.
  • 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 mecanic Industrielle du Quebec (Ste-foy, Canada), for measuring a number of optical properties as well as moisture content.
  • a wood chip optical inspection apparatus known as CMS-100 chip management system commercially available from the present assignee, Centre de mecanic Industrielle du Quebec (Ste-foy, Canada
  • Such wood chip inspection apparatus is described in U.S. Patent no. 6,175,092 B1 issued on January 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, 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.
  • 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 ScanChipTM system supplied by Iggesund Tools Inc. (Oldsmar, FL, USA).
  • 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 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.
  • Fig. 1 presents the independent variables that have been chosen according their relative importance index.
  • 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).
  • 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).
  • ISO brightness [ISOB] coefficient of correlation of 0,88
  • L* [LB] coefficient of correlation of 0,92
  • a* [AB] coefficient of correlation of 0,90
  • residual peroxide [PEROR] coefficient of correlation of 0,83.
  • the coefficient of correlation is only 0,60.
  • 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.
  • 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.
  • MPC Model Predictive Controller
  • 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.
  • 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.
  • 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.
  • 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 1 O, 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.
  • unmodeled disturbances may also be applied to the neural network at input 17
  • 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.
  • 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.
  • the neural network 12 generates at output 22 thereof, a predicted brightness value for pulp to produce from the inspected wood chips.
  • the brightness predicted value is compared with the required brightness value to generate error data, as indicated at node 24.
  • the error data is used by an optimization module 26, which optimizes the bleaching agent dosage value to minimize the error data.
  • 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.
  • 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.
  • 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.
  • peroxide has a predominant effect.
  • 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.
  • table 6 representing the effect of chip quality on peroxide charges to achieve different brightness set points.
  • a bleaching agent control system 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the bleaching unit 28 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.
  • 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.

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Abstract

L'invention concerne un procédé et un dispositif permettant l'estimation du dosage d'agent de blanchiment pour un procédé de production de pâte à papier de blancheur requise. On travaille sur une série de propriétés de copeaux de bois caractérisant ces copeaux selon l'estimation réalisée par un système de mesure. Les données de propriétés correspondantes sont ensuite injectées aux entrées d'un modèle de prévision (10) y compris un réseau neuronal (12), et on injecte aussi une valeur de dosage initiale de l'agent de blanchiment. Le modèle de prévision (10) donne une valeur de blancheur prévue pour la pâte à produire à partir des copeaux de bois inspectés, permettant l'estimation du dosage d'agent de blanchiment optimal auquel la valeur de blancheur prévue atteint sensiblement la valeur de blancheur requise. On décrit un procédé et un système permettant de contrôler le blanchiment de la pâte, reposant respectivement sur le procédé et le dispositif d'estimation considérés.
PCT/CA2004/001888 2003-10-28 2004-10-28 Procede et dispositif permettant l'estimation du dosage optimal d'agent de blanchiment pour un procede de production de pate a papier WO2005042832A1 (fr)

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CA002543781A CA2543781A1 (fr) 2003-10-28 2004-10-28 Procede et dispositif permettant l'estimation du dosage optimal d'agent de blanchiment pour un procede de production de pate a papier
US10/577,434 US20070158040A1 (en) 2003-10-28 2004-10-28 Method and apparatus for estimating an optimal dosage of bleaching agent to be used in a process for producing pulp

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CA002447098A CA2447098A1 (fr) 2003-10-28 2003-10-28 Methode d'estimation du dosage optimal d'agent de blanchiment a melanger a des copeaux de bois
CA2,447,098 2003-10-28

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WO2008134885A1 (fr) * 2007-05-04 2008-11-13 Centre De Recherche Industrielle Du Quebec Système et méthode d'optimisation du raffinage d'un matériau lignocellulosique granulaire
EP2158356A1 (fr) * 2007-05-04 2010-03-03 Centre De Recherche Industrielle Du Quebec Système et méthode d'optimisation du raffinage d'un matériau lignocellulosique granulaire
EP2158356A4 (fr) * 2007-05-04 2013-07-31 Quebec Centre Rech Ind Système et méthode d'optimisation du raffinage d'un matériau lignocellulosique granulaire

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