Methods and systems for controlling paper quality by adjusting fiber filter parameters

Abstract

Disclosed is a system that adjusts the operational parameters of a fiber filter apparatus to keep measured properties indicative of paper quality, such as the paper's porosity, within desired value ranges. A feedback loop may be set up in which input properties are read, such as the paper's porosity and the pollution content of the filtrate leaving the fiber filter. These inputs go into a predictive model that determines how to adjust the fiber filter's operational parameters in order to move the measured paper quality properties into their desired value ranges while accommodating other constraints on the fiber filter's operation. The model looks at positive and negative correlations between the operational parameters and the measured inputs. Having processed the inputs and predicted an outcome, the predictive model directs controllers to change the operational parameters of the fiber filter. The input properties are again read, and the feedback loop is repeated.

Description

CROSS-REFERENCE TO RELATED APPLICATION

[0001] The present application claims the benefit of United States Provisional Patent Applications Serial No. 60/290,199, filed on May 11, 2001, No. 60/291,683, filed on May 17, 2001, and No. 60/293,806, filed on May 25, 2001.

TECHNICAL FIELD

[0002] The present invention relates generally to paper production, and, more particularly, to systems and methods of controlling the quality of paper produced in an automated paper manufacturing process.

BACKGROUND OF THE INVENTION

[0003] Paper making is an extremely complicated process, but in its essentials a thermomechanical pulp (TMP) system takes wood, pulps it into wood fibers, and creates a slurry of the fibers and water. A paper machine pours the slurry over a mesh screen (called a “wire”). The fibers build up into a mat on the wire. When excess water is removed, the mat becomes paper.

[0004] Throughout the paper-making process, filters are used to control the amount and size of fibers in the slurry. For example, the excess water that passes through the wire contains a significant amount of unused fibers. To increase the efficiency of the process, and to reduce pollution, these fibers are retrieved and recycled. A waste fiber filter apparatus (called a “saveall”) retrieves the fibers. The saveall has a filter mesh through which water containing fibers flows. Fibers collect on the mesh, just as they do on the paper machine's wire. The fibers are harvested off the mesh and then join the slurry flowing from the TMP system to the paper machine. In a typical saveall, the mesh is in the form of one or more filter disks that rotate while the water passes through them. The speed of the rotation affects both the efficiency of fiber recycling and the cleanliness of the water filtrate leaving the saveall.

[0005] The quality of paper produced by the paper machine is determined in large part by the characteristics of the slurry in its input stream. Traditionally, the fiber filters in the saveall and in the TMP system were adjusted only to optimize efficiency at harvesting fibers and to reduce pollution. Other aspects of the slurry-making process were adjusted to control paper quality.

SUMMARY OF THE INVENTION

[0006] The above problems and shortcomings, and others, are addressed by the present invention, which can be understood by referring to the specification, drawings, and claims. The present invention adjusts the operation of a fiber filter in order to regulate the consistency of paper as it is being produced, all the while satisfying the original goals of efficiently harvesting fibers and reducing pollution. The invention adjusts the fiber filter's operational parameters to keep measured properties indicative of paper quality, such as the paper's porosity, within desired value ranges. The adjustments are made subject to other constraints on the fiber filter's operation, such as its efficiency in harvesting fibers and its control of the level of pollution in the filtrate leaving the fiber filter.

[0007] In some embodiments, a feedback loop is set up in which input properties are read, such as the paper's porosity and the pollution content of the filtrate. These inputs go into a predictive model that determines how to adjust the fiber filter's operational parameters in order to move the measured paper quality properties into their desired value ranges while accommodating the other constraints on the fiber filter's operation. The model looks at positive and negative correlations between the fiber filter's operational parameters and the measured inputs. Associated with the operational parameters may be maximum rates at which the parameters should be changed, weighting factors saying which parameters are preferentially changed, and the like. Having processed the inputs and predicted an outcome, the predictive model directs controllers to change the operational parameters of the fiber filter. The input properties are again read, and the feedback loop is repeated.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] While the appended claims set forth the features of the present invention with particularity, the invention, together with its objects and advantages, may be best understood from the following detailed description taken in conjunction with the accompanying drawings of which:

[0009] FIG. 1 is a simplified schematic of the basic elements involved in producing paper from wood pulp;

[0010] FIG. 2 adds to FIG. 1 sensors and controllers of some embodiments of the present invention to show how logical components of the invention interact;

[0011] FIG. 3 is a flowchart of an exemplary embodiment of the invention as it operates to control paper quality; and

[0012] FIGS. 4a and 4b are a flowchart of an exemplary procedure for using information about the operating characteristics of a paper mill to create predictive models usable by the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0013] Turning to the drawings, wherein like reference numerals refer to like elements, the invention is illustrated as being implemented in a suitable industrial environment. The following description is based on embodiments of the invention and should not be taken as limiting the invention with regard to alternative embodiments that are not explicitly described herein.

[0014] In the description that follows, the invention is described with reference to acts and symbolic representations of operations that are performed by one or more computers, unless indicated otherwise. As such, it will be understood that such acts and operations, which are at times referred to as being computer-executed, include the manipulation by the processing unit of the computer of electrical signals representing data in a structured form. This manipulation transforms the data or maintains them at locations in the memory system of the computer, which reconfigures or otherwise alters the operation of the computer in a manner well understood by those skilled in the art. The data structures where data are maintained are physical locations of the memory that have particular properties defined by the format of the data. However, while the invention is being described in the foregoing context, it is not meant to be limiting as those of skill in the art will appreciate that various of the acts and operations described hereinafter may also be implemented in hardware.

I. The Saveall's Operation in a Paper Mill

[0015] In the simplified schematic of FIG. 1, the TMP system 100 produces a slurry of wood fibers in water. The process of taking wood, pulping it into fibers, refining the fibers, and controlling the parameters of the resulting slurry is well known in the art and is incorporated in box 100. The slurry is piped (flow 102) to the mixed stock tank 104. From the mixed stock tank, the slurry is sent (106) to the paper machine 108. The slurry falls onto a constantly moving mesh screen conveyor, or “wire,” of the paper machine and there forms a mat. Excess water with some fibers are removed from the mat, and the mat becomes paper. The excess water and fibers fall (flow 110) into the paper machine wire pit 112 and are pumped (114) to the white water collection tank 116. From there, the water and fibers are recycled back (118) into the TMP system.

[0016] This simplified process is enhanced through better control of the fibers that pass from the paper machine wire pit 112 into the white water collection tank 116. A waste fiber filter apparatus called a “saveall” 120 is introduced to capture and recycle these fibers, thus increasing the efficiency of the paper-making process while decreasing the amount of pollution created. Some of the output from the white water collection tank (flow 122) is diverted to the saveall. The saveall has one or more rotating mesh screens through which the water containing the fibers flows. Fibers collect on the mesh screens, just as they do on the wire of the paper machine 108. The fibers are harvested off the mesh screens and are put into a flow of water (124) going to the mixed stock tank 104.

[0017] Very small fibers (called “fines”) could pass through the saveall 120's mesh screens and not be captured. To prevent this, a slurry of “sweetener,” water containing larger fibers, is diverted from the TMP system 100's output and added to the saveall's input stream (flow 126). The large fibers of the sweetener build up on the saveall's mesh screens and increase its efficiency at harvesting the fines. A proportioning valve 128 controls the ratio of sweetener to water from the white water collection tank 116 in the input stream to the saveall.

[0018] After the saveall 120 removes many of the fibers from its input stream, the filtrate water and remaining fibers are sent to a seal tank 132. This flow 130 is called the “drop leg.” The seal tank contains two sections. The first section is for “clear” filtrate that comes from the part of the saveall where the mat is more developed. This clear filtrate is recycled back to the TMP system 100 (flow 134) or, when the seal tank overflows, is sent (flow 136) to a sewer 138. The second section of the seal tank is for “cloudy” filtrate that comes from the part of the saveall where the mat is less developed. This filtrate is clouded by fibers that escaped the saveall. It is piped (140) into the white water collection tank 116 and eventually returns to the TMP system.

[0019] The quality of paper produced by the paper machine 108 is determined in large part by the characteristics of its input stream 106. Traditionally, components of the TMP system 100 were adjusted to control the quality of the paper, while the saveall 120 was only adjusted to optimize its efficiency in harvesting fibers and in reducing pollution. An embodiment of the present invention adjusts the operation of the saveall to produce paper of consistent quality, while satisfying the saveall's original goals of efficiently harvesting fibers and reducing pollution.

II. An Exemplary Embodiment: Adjusting Saveall Parameters to Control Paper Quality

[0020] FIG. 2 adds to FIG. 1 a few sensors and controllers while removing some of FIG. 1's detail for clarity's sake. The discussion accompanying FIG. 2 shows how logical components of an exemplary embodiment of the invention interact and presents the methods of the invention at a high level. Later, the text accompanying FIGS. 3 and 4 delves into details of an implementation of the invention. A computing device 200 receives from sensors measurements indicative of the quality of the paper being produced by the paper machine 108. For clarity's sake, the computing device 200 is depicted as a personal computer in FIG. 2, but its functions could be implemented on any control technology, including servers, multiprocessor systems, microprocessor-based systems, minicomputers, mainframe computers, and distributed computing environments that include any of the above systems or devices. In FIG. 2, one standard measurement of quality is shown: the porosity 202 of the paper. Properties other than porosity, singly or in combination, indicative of the quality of the paper may be measured and used in other embodiments of the invention. A predictive model running on the computing device compares the measured property with a desired value range set for that property. In order to keep the property within its desired value range, the model predicts the effect on the property of adjusting one or more operational parameters of the saveall 120. Then, the model directs controllers to adjust the saveall's operational parameters in accordance with its predictions. In FIG. 2, the saveall parameters adjustable by the predictive model include the rotation rate 204 of the saveall's drum filters and the ratio 206 of sweetener (flow 126) to white water (flow 122) in the input stream to the saveall.

[0021] At the same time that the predictive model is keeping paper porosity 202 within its desired value range, the model may attempt to satisfy other constraints on the operation of the saveall 120. It does this by controlling other properties to keep their measured values within their own desired value ranges. For example, the consistency 208 of the drop leg shows the saveall's efficiency at harvesting fibers. The predictive model may attempt to keep the measured drop leg consistency below a desired value. The model may be responsive to other constraints and may be able to adjust other operational parameters than those shown in FIG. 2, but these two controlled properties (paper porosity and drop leg consistency) and two adjustable parameters (rotation rate 204 and sweetener ratio 206) serve for illustrative purposes. The correlations between the controlled properties and adjustable parameters have been determined experimentally and are shown in the following table. 1 TABLE 1 Controlled Property: Adjustable Parameter: Porosity Drop Leg Consistency Sweetener Ratio − − Rotation Rate − +

[0022] Here, a “−” means that as the adjustable parameter increases, the controlled property decreases. A “+” means that the adjustable parameter and the controlled property increase and decrease together. For example, Table 1 shows that the higher the saveall filter rotation rate, the higher the drop leg consistency (more fibers in the filtrate), which means the lower the efficiency of the saveall's fiber harvesting. Conversely, the higher the sweetener ratio (within the normal operating range), the greater the saveall's efficiency. These correlations are used by the predictive model to determine how to adjust the saveall's operational parameters to keep the controlled properties within their desired value ranges. Section III discusses how the predictive model is developed from the correlations.

[0023] The flowchart of FIG. 3 shows how an embodiment of the invention reads measurements of controlled properties and then adjusts the saveall 120's operational parameters to keep the controlled properties within their desired value ranges. In step 300, a desired range is set for each controlled property. The “range” may take many possible forms. It may be a single value representing the optimum value for the controlled property. It may be a more complex structure such as a single preferred value (called a “soft target”) surrounded by a range of less preferred, but still acceptable, values. Here, a range of acceptable values is set for the paper porosity 202 while a maximum allowable value is set for the drop leg consistency 208. The desired value ranges for controlled properties generally depend upon the operating characteristics of each particular paper mill and are generally derived from experiments run at the mill. These ranges can change over time, such as when the mill makes a different type of paper. Techniques for setting these ranges are well known in the art, and the disclosed control concerns keeping the controlled properties within their desired value ranges rather than determining what those ranges should be.

[0024] As an additional feature of embodiments of the invention, step 300 allows for the setting of optimal values for the operational parameters. In a -manner similar to moving the controlled properties toward their desired value ranges, the operational parameters are driven toward their optimal values. The table below shows two exemplary methods for specifying optimal values. (Note that these methods are also used to specify desired value ranges for controlled properties in exemplary embodiments of the invention.) 2 TABLE 2 Adjustable Parameter LP Coefficient QP Coefficient Target Sweetener Ratio 0.0 100.0 0.07 Rotation Rate 75 0.0 0.0

[0025] In the LP (Linear Product) method, a cost coefficient is assigned to the parameter. If the cost is positive, then optimization tends to maximize the value of the parameter. Negative costs lead to a minimization of the parameter's value. Table 2's optimization maximizes the saveall 120's rotation rate 204. The QP (Quadratic Product) method tends to reduce the square of the difference between the value of the parameter and a set “target” value. Table 2 optimizes the sweetener ratio 206 in this manner. The effects of these two methods can be combined by giving a non-zero coefficient to each.

[0026] In step 302, input values are collected. This collection includes the values of the controlled properties, but may also include values of a much more diverse set of properties. Any property of the paper mill useful in predicting the result of changing the saveall 120's operational parameters (see discussion of step 306, below) may become the subject of a measurement. For example, besides the exemplary controlled properties of paper porosity 202 and drop leg consistency 208, motor load, chip moisture, temperature, pH level, and flow rate at various points in the paper mill can provide information useful in step 306. Considerations of cost and practicality determine which property measurements are utilized in a particular paper mill.

[0027] To collect many of these inputs, sensors measure the properties and provide the values. For some properties, however, measurements are not always immediately available. For example, in an embodiment of the invention measurements of paper porosity 202 are only available after a technician runs a sample through a laboratory process. The technician takes a sample only once an hour or so, while the feedback loop in FIG. 3 is set to run once every 30 seconds. When an actual measurement of an input property is not available in step 302, the predictive model takes the values it can measure and uses them to model a value for the property it cannot measure. When an actual measurement becomes available in a later instance of step 302, the modeled value is reset to the measured value.

[0028] The values of the controlled properties, whether measured or modeled, are compared in step 304 to their desired value ranges, set in step 300. Whenever controlled properties are outside of their desired ranges, the predictive model develops a proposed course of action in step 306. The model predicts how adjusting the saveall 120's operational parameters (in this example, rotation rate 204 and sweetener ratio 206) will change the values of the controlled properties. Note that the predictive model balances changes to all of the controlled properties, not simply the ones beyond their desired ranges. The predictive model attempts to move the wayward properties back into line with their desired value ranges without moving other controlled properties beyond their desired ranges. If a desired value range includes a soft target, then the predictive model, for example, attempts to move the controlled property closer to the soft target even if its current value is within the range of acceptability.

[0029] In embodiments of the invention, the predictive model takes into account how far a controlled property has deviated from its desired value range. The greater the deviation, the harder the predictive model works to eliminate it. Also, the predictive model need not treat all controlled properties equally. In the following table, weights are assigned to show how important it is to return each controlled property to its desired value range. 3 TABLE 3 Controlled Property Minimum Weight Maximum Weight Paper Porosity 2.0 2.0 Drop Leg Consistency 1.3 1.3

[0030] The minimum and maximum weights indicate the importance of correcting deviations when a controlled property's value drops below or rises above its desired value range, respectively. In this case, it is more important to keep paper porosity 202 within its desired range than it is to control drop leg consistency 208, and deviations above and below the ranges are treated equally.

[0031] In some embodiments, the predictive model takes into account other considerations when developing its proposed course of action. The following table illustrates two of these. 4 TABLE 4 Controlled Property Noise Filter Rotation Factor Paper Porosity 1.0 0.0 Drop Leg Consistency 1.0 0.0

[0032] A noise filter may be assigned to each controlled property to prevent the predictive model from trying to address minor variations. The predictive model multiplies a change in the value of a controlled property by its noise filter and reacts as if the result were the actual change. Thus, a noise factor of 1.0 means that the predictive model does no filtering while a value of 0.0 means infinite filtering (that is, the predictive model never responds to changes in this controlled property).

[0033] For an embodiment of the invention, the predictive model does not wait until a controlled property's value is beyond its desired range. Instead, the predictive model monitors trends in the value, uses previous changes in the adjustable parameters to predict where the controlled property's value is going, and works proactively to keep the value within its range. The rotation factor of Table 4 shows the extent to which the predictive model forecasts a measured change of a controlled property's value into the future. A value of 1.0 means that the entire change is forecast. A value of 0.0 means that the model does not use changes in the controlled property's value to forecast changes. In either case, the predictive model uses changes in the adjustable parameters to forecast changes in the controlled property's value.

[0034] Also during steps 304 and 306, the predictive model develops a proposed course of action for optimizing the operational parameters. Optimization is similar to controlling the controlled parameters with this difference: optimizing is of secondary concern and is only performed when it does not move the controlled properties beyond their desired value ranges.

[0035] Often, the predictive model's proposed course of action is not implemented unchanged. In step 308, various considerations are applied to limit or modify the proposal. Maximum and minimum allowable values are assigned to each adjustable parameter, such as a maximum rotation rate. Other considerations are illustrated by the following table. 5 TABLE 5 Adjustable Parameter Step High Step Low Move Suppression Sweetener Ratio 0.01 0.01 3000 Rotation Rate 0.2 0.2 100

[0036] Step High and Step Low are the maximum changes allowed to an adjustable parameter when increasing or decreasing it, respectively. Move suppression determines how fast the predictive model may adjust a parameter to keep the controlled properties within their desired value ranges.

[0037] In step 310, the predictive model's course of action, as modified by step 308, is acted upon by sending commands to controllers that change the saveall 120's operational parameters. This feedback loop is repeated by returning to step 302 and collecting new input values. The repetition rate of the loop is set, usually at about twice a minute, so that deviations are detected and addressed quickly.

III. Developing the Predictive Model

[0038] Section II shows how the predictive model uses sensor inputs, weights, noise filters, rotation factors, move suppression values, etc., to predict how to adjust the saveall 120's operational parameters to keep controlled properties in their desired value ranges while, optionally, optimizing the settings of the operational parameters. This section describes how the predictive model is created, that is, how those weights, filters, factors, etc., are determined and how correlations among them are set. Table 1 and its accompanying discussion hinted at the answer: these values depend strongly on the operating characteristics of a particular paper mill and are determined experimentally. As mentioned above, even the inputs available to the model and the parameters under its control vary from plant to plant so that Table 1 does not apply as is to every situation.

[0039] The flowchart of FIGS. 4a and 4b is an example of a method used to experimentally determine the model's factors. In step 400, the operator of the paper plant ranks the controlled properties according to the importance of keeping each one in its desired value range. This ranking is based on experience with paper making and on the characteristics of a particular plant. In step 402, weights are assigned that reflect the ranking. (These weights are discussed with reference to Table 3.) In steps 404 and 406, the predictive model is run with these weights and its behavior is compared against what the plant operator wants. The weights are refined until the operator is satisfied with the operation of the system under the predictive model. Similarly, in step 408 the plant operator decides which of the saveall 120's operational parameters should be changed faster and which slower. Move suppression values (Table 5) are assigned in step 410 to reflect the operator's decisions, tested in step 412, and refined in step 414. In this iterative manner, the plant operator uses the material at his disposal, that is, the operational parameters put under the predictive model's control, and refines the model's responses. This experimental process is also used to develop the predictive model's ability to model a value when it does not have a current measurement for an input. (See discussion of step 302 of FIG. 3.)

IV. Embodiments Using Fiber Filters Other Than the Saveall

[0040] The present invention is described in Section II by means of an exemplary embodiment that adjusts the operation of the saveall fiber filter 120 in order to regulate paper quality. The invention is not restricted to operating with the saveall, however. As mentioned earlier, the paper-making process uses fiber filters other than the saveall, such as in the TMP system 100. According to the teachings of the present invention, paper quality can be regulated by adjusting the operation of these other fiber filters, either alone or in combination with the saveall. The control techniques and methods for creating predictive models follow the examples given above. As emphasized in Section III, details of each embodiment depend upon the operating characteristics of a particular paper-making plant, including which sensors and controllers are available, and parameter settings are experimentally determined.

[0041] In view of the many possible embodiments to which the principles of this invention may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of invention. For example, the predictive model may be split into several distinct applications which run on separate computing devices. Modeling of input values may be performed by a process distinct from the one that controls the operational parameters. Therefore, the invention as described herein contemplates all such embodiments as may come within the scope of the following claims and equivalents thereof.

Claims

1. A method for controlling a property indicative of a quality of paper by adjusting an operational parameter of a fiber filter apparatus, the method comprising:

measuring a value of the property indicative of a quality of paper;

predicting how adjusting the operational parameter of the fiber filter apparatus will affect the value of the property indicative of a quality of paper; and

adjusting, based on the predicting, the operational parameter of the fiber filter apparatus to bring or to keep the value of the property indicative of a quality of paper within a desired value range.

2. The method of claim 1 wherein the fiber filter apparatus recycles fibers received from a paper machine back to the paper machine.

3. The method of claim 1 wherein the fiber filter apparatus is part of a thermomechanical pulp system.

4. The method of claim 1 wherein the desired value range of the property indicative of a quality of paper is a single value.

5. The method of claim 1 wherein the property indicative of a quality of paper relates to a porosity of the paper.

6. The method of claim 1 wherein the operational parameter of the fiber filter apparatus relates to a rotation rate of a filter in the fiber filter apparatus.

7. The method of claim 1 wherein the operational parameter of the fiber filter apparatus relates to a ratio of sweetener added to an input to the fiber filter apparatus.

8. The method of claim 1 further comprising:

choosing a maximum adjustment rate for the operational parameter;

wherein the operational parameter is adjusted no faster than the maximum adjustment rate.

9. The method of claim 1 further comprising:

choosing a desired value range of a constraint property, the constraint property distinct from the property indicative of a quality of paper;

measuring the value of the constraint property;

predicting how adjusting the operational parameter of the fiber filter apparatus will affect the value of the constraint property; and

adjusting, based on the predicting, the operational parameter of the fiber filter apparatus to bring or to keep the value of the constraint property within the desired value range of the constraint property.

10. The method of claim 9 wherein the constraint property relates to a drop leg consistency and the desired value range of the constraint property is specified by a maximum desired consistency.

11. The method of claim 9 wherein the constraint property relates to a rotation rate of a filter in the fiber filter apparatus and the desired value range of the constraint property is a minimum desired rotation rate.

12. The method of claim 1 further comprising:

predicting how adjusting a second operational parameter of the fiber filter apparatus will affect the value of the property indicative of a quality of paper; and

adjusting, based on the predicting, the second operational parameter of the fiber filter apparatus to bring or to keep the value of the property indicative of a quality of paper within the desired value range.

13. The method of claim 1 further comprising:

measuring the value of an input property distinct from the property indicative of a quality of paper;

wherein the predicting is based in part on the value of the input property.

14. The method of claim 13 wherein the input property is in the set: motor load, chip moisture, temperature, pH level, flow rate.

15. A computer-readable medium having instructions for performing a method for controlling a property indicative of a quality of paper by adjusting an operational parameter of a fiber filter apparatus, the method comprising:

measuring a value of the property indicative of a quality of paper;

predicting how adjusting the operational parameter of the fiber filter apparatus will affect the value of the property indicative of a quality of paper; and

adjusting, based on the predicting, the operational parameter of the fiber filter apparatus to bring or to keep the value of the property indicative of a quality of paper within a desired value range.

16. A system for controlling a property indicative of a quality of paper, the system comprising:

a fiber filter apparatus;

a controller that adjusts an operational parameter of the fiber filter apparatus;

a sensor that measures the value of the property indicative of a quality of paper;

a computing device; and

a predictive control application running on the computing device that reads measurements produced by the sensor, predicts how adjusting the operational parameter of the fiber filter apparatus will affect the value of the property indicative of a quality of paper, and directs the controller to adjust the operational parameter of the fiber filter apparatus to bring or to keep the value of the property indicative of a quality of paper within a desired value range.

17. The system of claim 16 wherein the fiber filter apparatus recycles fibers received from a paper machine back to the paper machine.

18. The system of claim 16 wherein the fiber filter apparatus is part of a thermomechanical pulp system.

19. The system of claim 16 wherein the desired value range of the property indicative of a quality of paper is a single value.

20. The system of claim 16 wherein the property indicative of a quality of paper relates to a porosity of the paper.

21. The system of claim 16 wherein the operational parameter of the fiber filter apparatus relates to a rotation rate of a filter in the fiber filter apparatus.

22. The system of claim 16 wherein the operational parameter of the fiber filter apparatus relates to a ratio of sweetener added to an input to the fiber filter apparatus.

23. The system of claim 16 wherein the predictive control application directs the controller to adjust the operational parameter of the fiber filter apparatus no faster than a desired maximum adjustment rate.

24. The system of claim 16 further comprising:

a second sensor that measures the value of a constraint property, the constraint property distinct from the property indicative of a quality of paper;

wherein the predictive control application reads measurements produced by the second sensor, predicts how adjusting the operational parameter of the fiber filter apparatus will affect the value of the constraint property, and directs the controller to adjust the operational parameter of the fiber filter apparatus to bring or to keep the value of the constraint property within a desired value range.

25. The system of claim 24 wherein the constraint property relates to a drop leg consistency and the desired value range of the constraint property is specified by a maximum desired consistency.

26. The system of claim 24 wherein the constraint property relates to a rotation rate of a filter in the fiber filter apparatus and the desired value range of the constraint property is a minimum desired rotation rate.

27. The system of claim 16 further comprising:

a second controller that varies a second operational parameter of the fiber filter apparatus;

wherein the predictive control application predicts how adjusting the second operational parameter of the fiber filter apparatus will affect the value of the property indicative of a quality of paper and directs the second controller to adjust the second operational parameter of the fiber filter apparatus to bring or to keep the value of the property indicative of a quality of paper within a desired value range.

28. The system of claim 16 further comprising:

a second sensor that measures the value of an input property distinct from the property indicative of a quality of paper;

wherein the predictive control application reads measurements produced by the second sensor and bases its prediction in part on the value of the input property.

29. The system of claim 28 wherein the input property is in the set: motor load, chip moisture, temperature, pH level, flow rate.

30. A computer-readable medium containing instructions for providing a system for controlling a property indicative of a quality of paper, the system comprising:

a fiber filter apparatus;

a controller that adjusts an operational parameter of the fiber filter apparatus;

a sensor that measures the value of the property indicative of a quality of paper;

a computing device; and

a predictive control application running on the computing device that reads measurements produced by the sensor, predicts how adjusting the operational parameter of the fiber filter apparatus will affect the value of the property indicative of a quality of paper, and directs the controller to adjust the operational parameter of the fiber filter apparatus to bring or to keep the value of the property indicative of a quality of paper within a desired value range.