Abstract:
A method and apparatus for using a neural network to control a paper or paper board production machine. By using spectrum measuring devices, the characteristics of the starting materials for the paper and board production and/or their intermediate or final products are registered and the values fed to a neural network. The network provides statements concerning the paper and board quality, from which signals for the feedback and/or feedforward control of the production process may be derived.

Description:
This application in the U.S. national stage application of International Application No. PCT/OE96/00476 having an international filing date of Mar. 19, 1996. 
    
    
     FIELD OF THE INVENTION 
     The invention relates to a method and a device for the process management of a paper machine for the production of paper and/or board, using at least one measuring device for registering physical characteristic values and at least one regulating or controlling device for the operating means used in the paper machine. 
     BACKGROUND OF THE INVENTION 
     In the earlier international Patent Application WO 95/08019 A1, which is not a prior publication, a device is proposed for operating an installation specifically for the production of deinked pulp. The installation includes at least one waste paper preparation means, downstream of which a paper machine or at least one dewatering machine is connected. In this case, measuring devices for registering spectral and/or physical characteristic values of the waste paper suspension are already used. Furthermore, regulating or controlling devices are used there for the operating means of the waste paper preparation means. There is also at least one state analyser, designed in the form of one or more parallel neural networks, for the waste paper suspension. The analyzer, by means of the characteristic values of the measuring device, supplies controlled variables for process management to the regulating or controlling devices of the operating means for the waste paper preparation means. 
     In the case of the device described above for the production of deinked pulp, using as great a proportion as possible of waste paper, there is in particular the problem that the quality of waste paper introduced into the installation fluctuates severely. For example, there can be, in the respective mixture of waste paper, sharply variable proportions of, for example, coloured illustrated paper, gray newsprint, white paper, contaminated paper, old books, for example with adhesive residues, such as telephone directories, cartons, packages, coated papers and contaminations of all types. The device previously described in the earlier Patent Application solves these problems in a satisfactory manner for the waste paper preparation means. 
     EP-A-0,137,696 discloses a method and an associated device for registering the water content of a paper web during production, in which, via an optical infrared measurement, use is made of the fact that water has an absorption band at 1.94 μm. To this end, a measuring channel and a reference channel having a different wavelength than the water absorption band is used. In addition, U.S. Pat. No. 5,282,131 discloses a system for regulating a pulp washing plant, in which a neuron network is employed to verify predictable process parameters. In the case of both of these documents, therefore, only some aspects of paper production are addressed. 
     OBJECTS AND SUMMARY OF THE INVENTION 
     An object of the present invention is to use the measurement principle on which the above described device is based directly in a paper machine. The object is achieved, according to the invention, in that, using the measuring device, spectral characteristic values are registered at different wavelengths on the operating materials of the paper machine. The operating materials are the starting material directly before the flow box on the paper machine and/or the intermediate or final product. The signals from the measuring device for the spectral characteristic values are evaluated by at least one neural network and statements about the product quality are derived therefrom. The statements are, in particular, the quality parameters of the paper or of the board. Signal variables are derived from the statements about the product quality. The signal variables may be used, on the one hand, for feedback control in the so-called stock preparation upstream of the paper machine, and on the other hand, for the feed forward control of the paper machine itself. 
     In the case of feedback control, the parameters of the stock preparation are adjusted to produce the stock quality necessary for an intended paper or board quality. In the case of feedforward control, on the other hand, the operating means of the paper machine are controlled so that an intended paper or board quality is achieved with a given stock quality. 
     The result of the invention is to provide, for the first time, the possibility of an on-line measurement using the spectrometer in a paper machine. By suitable evaluation, with determination of the quality values of the paper or the board, the quality-influencing parameters in the stock preparation for the paper machine can also be influenced. The delay times which were produced with the previously normal laboratory measurement are thus dispensed with. 
     In the associated arrangement, spectroscopes or spectrometers are used, in particular for picking up spectral distributions or overall spectra. The neural networks are used within the context of the invention, specifically for the evaluation of spectral characteristic values. Either the diffuse backscatter intensity or the diffuse transmitted intensity of selected spectral ranges is used as input variables for the neural networks. Further parameters of the stock suspension or of the paper or board, for example, consistency, moisture, grammage and the like, may also advantageously be used as input variables for the neural networks. Output variables from the neural networks are mechanical quality parameters of the paper or board produced, such as in particular, the so-called CMT value, the breaking length, the burst pressure, and other factors which are significant for the practical suitability of the paper. The neural networks can be trained using quality parameters measured off-line in the laboratory. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The foregoing and other objects and advantages of the present invention will be better understood with reference to the following detailed description of several embodiments thereof, which is illustrated by way of example, in the accompanying drawings, wherein: 
     FIG. 1 schematically illustrates an embodiment of the invention in the context of a paper producing machine; 
     FIGS. 2 a  and  3  schematically illustrate two partial neural networks for use in FIG.  1  and FIG. 2 b  illustrates the use of certain preferred wavelengths as inputs to the neural networks; and 
     FIGS. 4 to  7  show alternative embodiments for utilizing the data provided by spectrometers. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the figures, identical or identically acting parts have corresponding reference symbols. 
     In FIG. 1 an installation for producing paper is designated by  1 , which essentially comprises the so-called stock entry  2 , the actual paper machine with flow box  3 , a downstream wire mesh conveyor belt  4  for the transport of the wet paper webs, further roller tracks such as  5  and  6  for drying and winding up a paper web  110  and a size press  9 . In particular, the size press  9  can be arranged between drying devices  7  and  8 . Installations of this type for producing paper are known and in use in practice in a wide range of technical configurations. 
     In the installation according to FIG. 1 there is a first spectrometer  10  in the region of the stock inlet  2 . The measuring area  11  of the spectrometer  10  is directed onto the stock suspension that is used for the starting material for the paper production. A second spectrometer  10 ′ has its measuring area  11 ′ directed onto the paper web  110  upstream of the size press  9 . Furthermore, a third spectrometer  10 ″ is arranged with its measuring area  11 ″ downstream of the size press  9 , the measuring are  11 ″ may be directed onto the sized paper before the adjacent wind-up device, which is not shown in detail. 
     In FIG. 2 a  a three-layer neural network in designated by  20 , and, for example, comprises input neurons EN 1  to EN 7  and further neurons ZN 1  to ZN 5  and an associated output neuron AN 1 . Using the neural network  20 , the spectrum from the first spectrometer  10  is evaluated. The backscatter intensities I 1  to I n  from preferred wavelengths λ i  of the schematic diagram in FIG. 2 b  are used as inputs for the neural network  20 . Statements about the quality of the paper to be produced may be obtained from the wavelength intensities I i  with i=1, . . . , n. The statements of the paper quality of output AN 1  can be used, on the one hand, for the feedback control in the stock preparation and, on the other hand, for feedforward control in the paper machine  1  itself. 
     Shown in FIG. 3 is another three-layer neural network  30 , which has input neurons EN 8  to EN 12  and further neurons ZN 6  to ZN 9  and an output neuron AN 2 . Using this second neural network  30 , data from the spectrometers  10 ′ and  10 ″ are evaluated in a similar manner to that in FIG. 2 a.  It is also advisable to use the moisture  31  of the paper web  110  as a measured variable as a further input variable. Hence, statements about the product quality of the finished paper or board may be obtained on output AN 2 . Furthermore, mechanical parameters, such as the so-called CMT factor, the breaking length, and the burst pressure, can also be obtained. 
     It is also possible to provide, for the two further spectrometers  10 ′ and  10 ″, one dedicated partial neural network each. The neural networks according to FIGS. 2 and 3 can also be combined, the reliability of the derivation of the measured variables being improved by their interlinking. 
     Alternative possibilities for use of spectrometers in the context of paper and board production are set forth in FIGS. 4 to  7 . For each of FIGS. 4 to  7  there is a unit  50  for stock preparation, a so-called central stock area  70 , a paper machine  100 , which corresponds to the paper machine  1  according to FIG. 1, and a neural network  200 , which is assigned to the paper machine  100  and corresponds to the neural network  20  of FIG. 2 a.  The units  50 ,  70 ,  100  and  200  are integrated into a functional loop. 
     In FIG. 4 the spectrometer  10  according to FIG. 1 is assigned to the unit  50  for stock preparation. There, it is possible, for example, to register pulp or different waste paper materials, which is indicated by means of the parallel arrows. Via the central stock area  70 , suitable output material passes to the paper machine  100 . In addition to the signals from the spectrometer, the machine parameters and actuating variables and the data about the required paper quality are fed to the neural network  200 . Following off-line training via laboratory measurements on finished paper, the required mixing parameters and actuating variables for the output stock are given to the central stock area  70  using the neural network  200 . 
     In FIG. 5, the measurement using the spectrometer  10  takes place at the stock inlet for the paper machine  100 , that is to say downstream of the central stock area  70 . With a construction of the neural network  200  which is in principle identical, the result here is the possibility of generating actuating signals, such as actuating variables and parameters for the stock preparation  50 , on the one hand, and actuating variables for the paper machine  100 , on the other hand. 
     In the case of the arrangement according to FIG. 6, the spectrometer  10  is used on the finished paper or board emergent within the paper machine  100 . In accordance with FIG. 6, using the neural network, it is likewise possible to obtain actuating variables for the paper machine  100 , as well as actuating signals for the stock preparation  50  or the central stock area  70 . 
     In the case of the alternatives shown in FIGS. 4 to  6 , the neural network  200  is in each case directly assigned to the paper machine  100 , the required mechanical paper quality data being predetermined as important characteristic variables. In FIG. 7, an example is specified which specifically relates to the stock preparation  50 , that is to say the unit  50  of FIGS. 4 to  6 . In addition to the unit  50  for the stock preparation, having known individual elements, such as a so-called pulper, a refiner or the like, there is here, moreover, a stock pulping unit  55 . The stock pulping unit  55  essentially prepares waste paper and mixes it in specific proportions with a suitable pulp suspension. 
     In FIG. 7 the unit  50  for the stock preparation is assigned a neural network  220 , to which the required fibre qualities are input as characteristic variables. Using the spectrometer  10 , in this case measurements are made on the pulped stock before the actual stock preparation  50 , and the measured intensity signals are entered into the neural network  220 . Following suitable training of the neural network  220 , suitable actuating signals for the stock preparation  50  may be obtained using the measured values. 
     In the individual examples, it was shown that as a result of on-line measurement using one or more spectrometers and evaluation using one or more neural networks, as well as determination of the quality values of the paper or board to be produced, the quality determining parameters of the stock preparation and of the paper machine can be influenced on-line. In contrast to previous approaches utilizing discontinuous methods using laboratory measurements, the present invention avoids delay times.