US20080073050A1

US20080073050A1 - Method and apparatus for controlling at least one quality feature of a running material web

Info

Publication number: US20080073050A1
Application number: US11/845,984
Authority: US
Inventors: Rudolf Muench
Original assignee: Voith Patent GmbH
Current assignee: Voith Patent GmbH
Priority date: 2006-09-26
Filing date: 2007-08-28
Publication date: 2008-03-27
Also published as: EP1905894A1; DE102006045786A1

Abstract

A method and apparatus for controlling at least one quality feature of a running material web, in particular a fibrous web, during its production, including a measurement system, an electronic control and/or an evaluation unit. The measurement system measures the quality feature on a running material web repeatedly over the entire width of the material web. The electronic control and/or evaluation unit determines the variability of the quality feature through a variance component analysis, which is conducted on the basis of the profile measure values of a measured value set including several consecutive CD profiles in the machine direction, the set having been recorded during a selectable previous time window. The result of this variance component analysis is also drawn on to control the quality feature.

Description

BACKGROUND OF THE INVENTION

1. Field of the Invention
This invention relates to a method and an apparatus for controlling at least one quality feature of a running material web, in particular a fibrous web, during its production. The fibrous web can be in particular a paper web or a paperboard web.
2. Description of the Related Art
Quality controls on a paper web are based on two-dimensionally recorded quality data (profile measured values). The data are recorded by way of a quality measurement system (QMS) using scanners, associated with traversing measurement systems or by simultaneous recording of quality data at numerous measurement points in the transverse direction of the web.
The data received are first processed into a suitable form for controlling features of the web in the machine direction (MD) or in the cross direction (CD).
In the case of the control methods and apparatuses known to date, the MD control is used to control the mean quality over the web width through intervention in the settings in the region of the paper machine, including the calender or coater, or in the region of the wet end, such as in the region of the pulp feed to the paper machine. Hence for MD control, the current mean paper quality is calculated at each control moment from the previous measured values, namely through formation of the mean value of all measured values in the direction transverse to the web. This calculation of the current mean paper quality from the previous measured values can be further improved somewhat through an additional use of filterings, extrapolations, estimate functions or the like. The data thus created for the MD control are referred to as the MD profile.
The CD control is used to control the deviations of paper quality in the CD direction of the paper web. The data thus created for the CD control are referred to as the CD profile. In the prior art the variability of the paper quality can be determined from the MD or CD profile only incompletely or slowly. Through the calculation of the MD and CD profiles using the formation of mean values, the original data quantity is reduced so severely as to lose information for statistical statements.
For example, if a traversing cross profile measurement includes 200 profile data values for the web width and if it is also assumed, for example, that the duration of a profile measurement amounts to 20 seconds and MD profile values are determined computationally through interpolation techniques every 5 seconds from the measured data of the traversing measurement system, then this means that only 20 MD profile values are determined from five traversings of the measurement instrument in 100 seconds. This number of MD profile values is not sufficient for meaningful statistical statements.
What is needed in the papermaking art is a method for computing more exacting statistics in a shorter period of time.

SUMMARY OF THE INVENTION

The present invention creates an improved method and an improved apparatus with which the previously mentioned problems are eliminated. In particular, a more informative and faster assessment of the variability of the web quality results.
A faster and more exact determination of the variability enables control systems to be optimally adjusted in a shorter time when a statistical characteristic of the profile measured values changes. According to one embodiment of the present invention this object is accomplished by a method for controlling at least one quality feature of a running material web, in particular a fibrous web, during its production, in which, by use of a measurement system the quality feature in question is measured on a running material web, repeatedly over the entire width, in order to receive profile measured values in the longitudinal direction (MD) and transverse direction (CD) of the web. This is accomplished by way of an electronic control and/or evaluation unit. The variability of the quality feature is determined through a variance component analysis, which is conducted on the basis of the profile measured values of a measured value set including several consecutive CD profiles in the machine direction. The measured value set having been recorded during a selectable previous time window, and the result of this variance component analysis is used to control the quality feature.
The individual measured data received from the material web in a previous time window are simultaneously assessed and information losses are prevented accordingly. Additionally, the system assesses the variability of the material web far more informatively. For example, the MD variability in a respective previous time window is now assessed on the basis of a very high number of individual measured values instead of on the basis of only a few values as hitherto. If the statistical properties of the material web change, then the correspondingly faster and more precise knowledge of the variability of the web quality permits a faster change of the corresponding parameters of the control systems and filterings. Faults of the production process due to overly severe control interventions are reduced to a minimum. Such faults can arise when, because of random or non-controllable faults, the control responds severely nevertheless and thus carries out unnecessary process interventions. Such faults are practically ruled out according to the present invention.
A preferred practical embodiment of the present invention is a method were the variability of the quality feature is divided by way of the variance component analysis of profile measured values into a CD variability component in the cross direction (CD), an MD variability component in the machine direction (MD) and a residual variability component. Further, confidence values for the CD profile and/or the MD profile are calculated from determined variance values, and the variability components or confidence intervals are also drawn on to control the quality feature.
It is also an advantage, in particular for the individual profile measured values of a respective measured value set, which is to be subjected to a variance component analysis, to be arranged by way of the control and/or analysis unit in a matrix in whose lines or columns there are CD profile measured values respectively and in whose columns or lines there are MD profile measured values respectively.
It is advantageous for the profile measured values contained in a respective measured value set, which is to be subjected to variance component analysis, to be variously weighted in respect of their contribution to the variability of the quality feature to be determined, at least partially, according to how far back they lie in time compared to the actual moment of determining the variability. In this case, profile measured values lying relatively far back are preferably weighted to a lesser degree.
Expediently the variance component analysis is repeated when a selectable number of new profile measured values is measured by way of the measurement system. The result of a respective variance component analysis can be used advantageously, for example, in order to change the behavior of at least one control system provided to control the quality feature. It is also an advantage, in particular, for at least one control parameter and/or at least one filter parameter of at least one control system, provided to control the quality feature, to be variably adjustable according to the result of a respective variance analysis.
The variance component analysis is based on at least one of the following algorithms: TAPPI TIP 1101, TAPPI T585, Exact Variance Analysis DAHLIN. The Exact Variance Analysis is described below in more detail. These algorithms are individually described in the following publications:

- Dahlin, E. B., “Computational Methods in a Dedicated Computer System for Measurement and Control on Paper Machines”, Tappi Journal, 53, No. 6: 1100-1105, 1970;
- Tappi T585 om-93, “Cross-machine grammage profile measurement (gravimetric method)”, 1993;
- Tappi TIP1101-01, “Calculation and partitioning of variance using paper machine scanning sensor measurements”, issued 1996, corrected 1997.

In the case of some algorithms for the variance component analysis, the residual variability components are interpreted and referred to as short-term deviations. Otherwise the algorithm in question can correspond, at least essentially, to one of the previously mentioned algorithms.
Also used in the present invention are recursive forms of the variance component analysis. In the case of a recursive algorithm, newly recorded measured data are linked to the last event of the variance component analysis such that an updated variance component analysis is obtained as the result.
The CD variability component and the MD variability component can each be divided, through wavelength analysis, into a controllable fraction and a non-controllable fraction respectively.
A relatively large residual variability component, compared to the current CD and MD variability components or controllable CD and MD fractions, is used by way of the control and/or analysis unit. At least one control system is provided to control the quality feature relatively more slowly and/or relatively more weakly on the production process.
It is also advantageous, in particular for a relatively large residual variability component, compared to the current CD and MD variability components or controllable CD and MD fractions, to be taken into account such that the measured data are filtered relatively more intensively prior to calculation of the respective set-point variable for at least one control system that is provided to control the quality features.
Another embodiment of the present inventive method is characterized in that confidence intervals for the CD profile and/or the MD profile are calculated by way of the control and/or analysis device from determined variance values. In the light of these confidence intervals, a distinction is drawn between a respective profile deviation based on a really existing cause and a deviation based at least essentially only on a residual variability component.
At least one control system provided to control the quality feature is activated, preferably by way of the control and/or analysis device, to perform an intervention in the production process, if it was established, in the light of at least one confidence interval, that the deviation in question is based on a really existing cause.
Advantageously, at least one control system provided to control the quality feature is then actuated by way of the control and/or analysis device to intervene relatively more weakly, or not at all, in the production process, if it was established, in the light of at least one confidence interval, that the deviation in question is based at least essentially only on a residual variability component.

EXAMPLES

- K_w: effective control gain
- K₀: control parameter
- VarRES: residual variance
- VarCD: CD variance
- VarCD_R: controllable fraction of the CD variance

According to the invention it is thus possible to use the following equations in order to determine an effective control gain:
$\begin{matrix} a) & K_{w} = K_{0} \cdot \frac{1}{Var Res} \\ b) & K_{w} = K_{0} \cdot \frac{1}{\sqrt{Var Res}} \\ c) & K_{w} = K_{0} \cdot \frac{Var {CD}_{R}}{Var RES} \\ d) & K_{w} = K_{0} \cdot \sqrt{\frac{Var CD}{Var RES}} \end{matrix}$
Variations can also be used through the addition of constants, multiplication with other parameters, functions of greater complexity and/or allowance for limits etc.
It is also an advantage, in particular for the calculated variability components and/or confidence intervals, to be drawn on by way of the control and/or analysis device in assessing the quality of a process model, which serves to control the quality and is saved in the control and/or analysis device as software.
Any deviation of the default values of the process model from the recorded profile measured values is determined by way of the control and/or analysis device. Any determined deviation on a relatively high variability of the quality features and/or a relatively low confidence is weighted relatively more lowly and/or is filtered relatively more intensively prior to further use.
The calculated variability components and/or confidence intervals can also be taken into account when there is a change of the process model which serves the quality control and is saved in the control and/or analysis device as software. In this case account is also given to the calculated variability components and/or confidence intervals, preferably in the form of adaptation parameters, which change the behavior of the model.
It is also an advantage for the process model to be corrected by way of the control and analysis device, more slowly relative to the process measured via the profile measured values in the event of a relatively higher variability of the quality feature and/or a relatively lower confidence.
The quality features of the material web, in particular a paper web or a paperboard web, to be controlled can be, for example, the gsm substance, the moisture content and/or the web thickness, etc.
The object stated above is also accomplished according to the present invention by an apparatus for controlling at least one quality feature of a running material web, in particular a fibrous web, during its production, having a measurement system for the repeated measurement of the respective quality feature on a running material web over the entire width of the material web, and an electronic control and/or evaluation unit for determining the variability of the quality feature through a variance component analysis. The analysis is carried out on the basis of the individual profile measured values of a measured value set including several consecutive CD profiles in the machine direction, the set having been recorded during a selectable previous time window, whereby the electronic control and/or analysis unit is designed such that the control of the quality feature intervenes in the process more or less intensively according to the result of the variance component analysis.
The electronic control and/or analysis unit is preferably designed such that the variability of the quality feature by way of the variance component analysis is divided into a CD variability component in the cross direction (CD), an MD variability component in the machine direction (MD) and a residual variability component and/or confidence values for the CD profile and/or the MD profile. The confidence values are calculated from determined variance values, as such the control of the quality feature takes place according to the variability components or confidence intervals.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned and other features and advantages of this invention, and the manner of attaining them, will become more apparent and the invention will be better understood by reference to the following description of an embodiment of the invention taken in conjunction with the accompanying drawing, wherein:

FIG. 1 is a schematical representation of an embodiment of a method of the present invention for controlling quality features of a running material web.

Corresponding reference characters indicate corresponding parts throughout the several views. The exemplification set out herein illustrates one embodiment of the invention, in one form, and such exemplification is not to be construed as limiting the scope of the invention in any manner.

DETAILED DESCRIPTION OF THE INVENTION

The present invention will be described in more detail in the following text using an exemplary embodiment and with reference to the drawing.
The single FIGURE of the drawing shows a simplified schematic representation of an apparatus for controlling at least one quality feature of a running material web, in particular a fibrous web, during its production. This apparatus is assigned to a plant having a wet end and a paper machine (or coater, or calender) for producing a paper web or paperboard web. Hence in this case the apparatus is used, for example, to control at least one quality feature of a paper web or paperboard web.
The control apparatus includes a measurement system 10 for the repeat measurement of the respective quality feature on a running material web or paper web over the entire width of the material web. As is evident from FIG. 1, measurement system 10 is typically arranged at the end of a paper machine 12 (or coater, or calender).
In addition the control apparatus includes an electronic control and/or evaluation unit 14 for determining the variability of the quality feature through a variance component analysis on the basis of the recorded profile measured values, of a measured value set including several consecutive CD profiles in the machine direction. The set having been recorded during a selectable previous time window.
The electronic control and/or analysis unit 14 is designed such that the control of the quality feature takes place according to the result of the variance component analysis.
The variability of the quality feature can be divided by way of the variance component analysis into a CD variability component in the cross direction (CD), an MD variability component in the machine direction (MD) and a residual variability component. Alternatively, or in addition, it is possible for confidence intervals for the CD profile and/or the MD profile to be calculated from determined variance values. The calculated variability components or confidence intervals are drawn on to control the quality feature.
The individual CD and MD profile measured values of a respective measured value set, which is to be subjected to a variance component analysis, can be arranged by way of the control and/or analysis unit in a matrix in whose lines or columns there are CD profile measured values respectively and in whose columns or lines there are MD profile measured values respectively. The profile measured values, contained in a respective measured value set, which is to be subjected to variance component analysis, can be variously weighted in respect of their contribution to the variability of the quality feature to be determined, at least partially according to how far back they lie in time as compared to the actual moment of determining the variability. In this case, profile measured values lying relatively far back can be weighted in particular relatively more lowly.
The variance component analysis can be repeated when a selectable number of new profile measured values are measured by way of measurement system 10. The result of a respective variance component analysis can be drawn on in order to change accordingly the behavior of at least one control system provided to control the quality feature. For example, at least one control parameter and/or at least one filter parameter of at least one control system, provided to control the quality feature, can be variably adjustable according to the result of a respective variance component analysis.
The variance component analysis can be based, for example, on at least one of the following algorithms: TAPPI TIP 1101, TAPPI T585, DAHLIN Exact Variance Analysis (see the literature cited above). The Exact Variance Analysis is also described below in more detail.
The division into a CD variability component, an MD variability component and a residual variability component must be sufficiently exact in order to be able to draw the correct conclusions for the control in question. An underestimated variable in the assessment of a respective paper quality feature is the residual profile, also referred to simply as residual. The residual fraction of the web deviation is defined in that it cannot be assigned to either the machine direction (MD) or the cross direction (CD). Hence it characterizes random quality faults. The residual fraction is perceived by the paper producer as “live” cross profiles. The cross profile is poor and changes constantly; the peaks in the cross profile are not fixed but sometimes become inverted.
Often the paper producer concludes that the cross profile is poor, which is misleading. The fact is this type of fault cannot be corrected even by an improved cross profile control, for example with more actuating elements.
The purpose of the variance component analysis is to put the relationships into perspective and to clearly distinguish which fraction of the displayed profile faults has its origin in stable cross profile deviations (CD), which fraction comes from temporary production faults (MD), and how large the random fraction of the deviations is.
The variability of the paper web arises therefore from the superimposition of three causes:

- a temporally stable CD profile deviation
- an MD profile deviation which affects the entire web width uniformly
- a residual deviation (RES) which arises randomly in place and time

It has been shown that these three causes of quality deviations can be considered as statistically independent of each other, which is an important precondition for the variance component analysis.
The basic idea behind the Exact Variance Analysis, for example, is to find an algorithm which correctly determines the amplitudes of the three mentioned causes from the measured values of the paper web. The algorithm for this Exact Variance Analysis is presented below. It meets the following requirements:

- The total variance (TOTAL, TOT) is calculated in accordance with the general rules of statistics from the variance of all individual profile measured values in the MD and CD direction (mean square deviation from the mean value).
- The amplitudes of the MD, CD and RES deviations are correctly determined.
- The sum of the variances of MD, CD and RES result in the total variance (TOT) in the expected value (TOT).

The relations in question for the Exact Variance Analysis are presented in the following:
Equation letters:
X: matrix with measured data (each gap is a cross profile)
M, N: number of measured data in the MD and CD direction
i, k counter for the summation
Mean MD profile and CD profile:
${MD}_{k} = \frac{1}{N} \cdot \sum_{i = 1}^{N} x_{i, k}$ $CDi = \frac{1}{M} \cdot \sum_{k = 1}^{M} x_{i, k}$
Mean value of all data values:
$m (X) = \frac{1}{M \cdot N} \cdot \sum_{k = 1}^{M} \sum_{i = 1}^{N} x_{i, k}$
Intermediate variables for calculating the CD and MD variance:
$Var ResMD = \frac{1}{(N - 1) \cdot M} \cdot \sum_{k = 1}^{M} \sum_{i = 1}^{N} {(x_{i, k} - {CD}_{i})}^{2}$ $Var ResCD = \frac{1}{(M - 1) \cdot N} \cdot \sum_{k = 1}^{M} \sum_{i = 1}^{N} {(x_{i, k} - {MD}_{k})}^{2}$
Variance fractions (result of the variance analysis):
$Var TOT = \frac{1}{N \cdot M - 1} \cdot \sum_{k = 1}^{M} \sum_{i = 1}^{N} {(x_{i, k} - m (x))}^{2}$ $Var RES \frac{1}{(M - 1) \cdot (N - 1)} \cdot \sum_{k = 1}^{M} \sum_{i = 1}^{N} {(x_{i, k} - {CD}_{i} - {MD}_{k} + m (x))}^{2}$ $Var MD = \frac{N}{(N - 1) \cdot M} \cdot [\sum_{k = 1}^{M} {({MD}_{k} - m (x))}^{2}] - \frac{1}{N} \cdot Var ResMD Var CD = \frac{M}{(M - 1) \cdot N} \cdot [\sum_{i = 1}^{M} {({CD}_{i} - m (x))}^{2}] - \frac{1}{M} \cdot Var ResCD$
Knowledge of the variance scatter can be used to specify confidence intervals of the variance analysis. The smaller the random sample, the greater the statistical uncertainty of the analysis and the larger the confidence intervals.
The specification of confidence intervals helps to relativize the value of the statistical statements.
For a control system it can be expedient to calculate the variance fractions on the basis of a relatively small number of profiles. In conjunction with the confidence intervals it is thus possible to distinguish random profile fluctuations from controllable fluctuations with a real cause.
It has been shown that the variability of the paper web can be assessed far more informatively if all the individual measured data of the paper web, in a previous time window, are assessed simultaneously. For the example mentioned at the beginning it is possible to assess the MD variability of the paper web in the last 100 seconds on the basis of 500 individual measured values instead of typically 20 MD values as was commonly used hitherto. The fast and precise knowledge of the variability permits a fast change of the corresponding parameters of the controls and filterings if the statistical properties of the paper web change. Faults of the production process due to overly severe control interventions are reduced to a minimum. Such faults can arise when, because of random or non-controllable faults, the control responds severely nevertheless and thus carries out unnecessary process interventions.
The profile measured values contained in a respective measured value set, which is to be subjected to variance component analysis, can be variously weighted, in respect of their contribution to the variability of the quality feature to be determined at least partially according to how far back they lie in time compared to the actual moment of determining the variability. In this case, profile measured values lying relatively far back can be weighted to a lesser degree.
The variance component analysis can be repeated when a selectable number of new profile measured values was measured by way of the measurement system.
The result of a respective variance component analysis can be drawn on in order to change accordingly the behavior of at least one control system provided to control the quality feature. For example, at least one control parameter and/or at least one filter parameter of at least one control system provided to control the quality feature can be variably adjustable according to the result of a respective variance analysis.
The variance component analysis can be based not only on the Exact Variance Analysis just described, but also on at least one of the following algorithms: TAPPI TIP 1101, TAPPI T585, DAHLIN Exact Variance Analysis.
With the algorithm used as a basis for the variance component analysis it is possible to calculate short-term deviations, which can also be roughly interpreted as residual components.
The CD variability component and the MD variability component can each be divided into a controllable fraction and a non-controllable fraction.
A relatively large residual variability component, compared to the current CD and MD variability components or controllable CD and MD fractions, can be used by way of the control and/or analysis unit 14 in particular such that at least one control system provided to control the quality feature acts relatively more slowly and/or relatively more weakly on the production process.
In principle it is also possible for a relatively large residual variability component, compared to the current CD and MD variability components or controllable CD and MD fractions, to be taken into account, such that the measured data are filtered relatively more intensively prior to calculation of the respective set-point variable for at least one control system provided to control the quality features.
As already mentioned it is possible for confidence intervals for the CD profile and/or the MD profile to be calculated by way of the control and/or analysis device 14 from determined variance values and, in the light of these confidence intervals, a distinction can be drawn between a respective profile deviation based on an existing cause and a deviation based, at least essentially, only on a residual variability component. At least one control system provided to control the quality feature is then activated by way of the control and/or analysis device 14 to perform an intervention in the production process if it was established, in the light of at least one confidence interval, that the deviation in question is based on a really existing cause.
It is also conceivable, in particular for at least one control system provided to control the quality feature to be actuated by way of control and/or analysis device 14 to intervene relatively more weakly or not at all in the production process, if it was established, in the light of at least one confidence interval, that the deviation in question is based, at least essentially, only on a residual variability component.
The calculated variability components and/or confidence intervals can also be taken into account in the assessment of the quality of a process model which serves the quality control and is saved in control and/or analysis device 14 as software. Any deviation of the default values of the process model from the recorded profile measured values is determined by way of control and/or analysis device 14 and any determined deviation on a relatively high variability of the quality features and/or a relatively low confidence is weighted relatively more lowly and/or is filtered relatively more intensively prior to further use.
The calculated variability components and/or confidence intervals can also be taken into account, in particular when there is a change of the process model, which serves the quality control, and is saved in control and/or analysis device 14 as data and/or software. The calculated variability components and/or confidence intervals can also be taken into account, for example, through adaptation parameters.
It is also conceivable in particular for the process model to be corrected by way of control and analysis device 14 relatively more slowly to the process measured in particular by way of the profile measured values in the event of a relatively higher variability of the quality feature and/or a relatively lower confidence.
While this invention has been described with respect to at least one embodiment, the present invention can be further modified within the spirit and scope of this disclosure. This application is therefore intended to cover any variations, uses, or adaptations of the invention using its general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains and which fall within the limits of the appended claims.


List of reference numerals

10	measurement system
12	paper machine
14	electrical control and/or analysis unit

Claims

1. A method for controlling at least one quality feature of a running fibrous web during its production, the method comprising the steps of:

repeatedly measuring a quality feature in the running fibrous web over an entire width of the fibrous web by way of a measurement system;

determining a variability of the quality feature through a variance component analysis by way of an electronic unit, said determining step including the step of conducting said variance component analysis on a basis of a plurality of profile measured values of a measured value set, said measured value set including a plurality of consecutive cross direction (CD) profiles in a machine direction, said measured value set having been recorded during a selectable previous time window; and

controlling the at least one quality feature dependant on said variance component analysis.

2. The method of claim 1, wherein said conducting step further comprising the step of dividing said variability of the quality feature by way of said variance component analysis into a CD variability component in the cross direction, into a machine direction (MD) variability component in the machine direction and into at least one of residual variability components and confidence intervals for at least one of said CD profile and a MD profile calculated from determined variance values, said controlling step controlling the at least one quality feature being dependent upon at least one of said variability components and said confidence intervals.

3. The method of claim 2, wherein individual measured values of said measured value set are arranged by said electronic unit in a matrix having said CD profile measured values in one of rows and columns of said matrix and said MD profile measured values in one of said rows and said columns.

4. The method of claim 3, wherein said profile measured values in a respective one of said measured value set are variously weighted respective to their contribution to the variability of the quality feature at least partially according to how far back they lie in time as compared to a moment when said determining step is carried out.

5. The method of claim 4, wherein said profile measured values that are farther back in time from said moment are weighted relatively more lowly than those said profile measure values that are less far back in time from said moment.

6. The method of claim 4, wherein said conducting step is performed in a recursive manner.

7. The method of claim 4, wherein said variance component analysis is repeated when a selectable number of new profile measured values are measured by way of said measurement system.

8. The method of claim 4, wherein at least one result of said variance component analysis is drawn on to change a behavior of at least one control system to control the quality feature.

9. The method of claim 8, wherein at least one of a control parameter and a filter parameter of said at least one control system is variably adjusted dependant on said at least one result of said variance component analysis.

10. The method of claim 4, wherein said variance component analysis is based on at least one algorithm, said at least one algorithm including one of a Technical Association of the Pulp and Paper Industry (TAPPI) Technical Information Paper (TIP) 1101, TAPPI T585 and DAHLIN exact variance analysis.

11. The method of claim 10, further comprising the step of calculating short-term deviations with said at least one algorithm used as a basis for said variance component analysis.

12. The method of claim 4, wherein said CD variability component and said MD variability component are each divided into a controllable fraction and a non-controllable fraction.

13. The method of claim 8, further comprising the step of determining if said residual variability component is relatively large compared to one of a current CD variability component, a current MD variability component, a controllable CD fraction and a MD controllable fraction and if said residual variability component is relatively large then using said control system to control the quality feature at least one of slower and more weakly during the production than if said residual variability component was not relatively large.

14. The method of claim 8, further comprising the step of determining if said residual variability component is relatively large compared to one of a current CD variability component, a current MD variability component, a controllable CD fraction and a MD controllable fraction and if said residual variability component is relatively large then intensively filtering said residual variability component prior to a calculation of a respective set-point variable used by said control system to control the quality feature.

15. The method of claim 2, wherein said confidence intervals for one of said CD profile and said MD profile are calculated from determined variance values, using said confidence intervals a distinction is drawn between a profile deviation based on an existing cause and a deviation based on substantially only said residual variability component.

16. The method of claim 15, further comprising the step of intervening in the production of the fibrous web by a control system if in the light of at least one of said confidence intervals a deviation is based on an existing cause.

17. The method of claim 16, further comprising the step of intervening in the production of the fibrous web by said control system one of relatively weakly and not at all if in the light of at least one of said confidence intervals a deviation is based substantially only on said residual variability component.

18. The method of claim 2, wherein at least one of said variability components and said confidence intervals are drawn on for assessing a quality of a process model saved in said electronic unit.

19. The method of claim 18, wherein any deviations of default values of said process model from recorded profile measured values is determined by way of said electronic unit, any of said deviations that are determined to be at least one of a relatively high variability of the quality feature and a relatively low confidence is at least one of weighted relatively more lowly and is filtered relatively more intensively prior to further use by said electronic unit than if said deviations are determined to not be one of a relatively high variability of the quality feature and a relatively low confidence.

20. The method of claim 18, wherein at least one of said calculated variability components and said confidence intervals are used as input to change said process model.

21. The method of claim 20, wherein at least one of said calculated variability components and said confidence intervals are used as input to change said process model by way of adaptation parameters.

22. The method of claim 20, wherein said process model is corrected by way of the electronic unit more slowly if there is at least one of a relatively higher variability of the quality feature and a relatively lower confidence of said profile measured values.

23. The method of claim 2, wherein at least one of a MD variance and a CD variance is not explicitly determined in said variance component analysis.

24. The method of claim 1, wherein the quality feature is at least one of moisture, thickness, gsm substance and filler material content of the fibrous web.

25. An apparatus for controlling at least one quality feature of a running fibrous web during its production, the apparatus comprising:

a measurement system for repeated measurement of the at least one quality feature over an entire width of the fibrous web; and

one of an electronic control and an evaluation unit for determining a variability of the at least one quality feature by way of a variance component analysis dependent on a plurality of profile measured values of a measured value set, said measured value set including several consecutive cross direction (CD) profiles in a machine direction (MD), said measured value set having been recorded during a selectable previous time window, one of said electronic control and said evaluation unit controlling the quality feature intervenes in the production of the fibrous web at an intensity determined by said variance component analysis.

26. The apparatus of claim 25, wherein one of said electronic control and said evaluation unit controls the variability of the quality feature by way of said variance component analysis that includes a CD variability component in said CD, and a MD variability component in said MD and at least one of a residual variability component and confidence intervals for at least one of said CD profile and a MD profile calculated from determined variance values such that the control of the quality feature is dependent upon at least one of said variability components and said confidence intervals.

27. The apparatus of claim 26, wherein at least one of a MD variance and a CD variance is not explicitly determined in said variance component analysis.

28. The method of claim 25, wherein the quality feature is at least one of moisture, thickness, gsm substance and filler material content of the fibrous web.