Prediction of paperboard properties with virtual on-line, Solutions!, Online Exclusives, July 2003

Prediction of Paperboard Properties with Virtual On-Line Analyzers

By Osvaldo Vieira, Klabin S.A.; Enrique L. Lima, Professor, COPPE/UFRJ, Brazil; Ivo Neitzel, Professor, Universidade Estadual de Maringá, Brazil; Mário Gerhard, Director, Pavilion Technologies Inc.

Under a program to reduce production costs and increase quality, Telêmaco Borba’s Klabin S.A. mill installed a system using mathematical models to predict, on-line and in real-time, several properties of the products. The installed virtual on-line analyzers (VOA) run every minute, providing values of several paperboard properties. In addition to an increase in quality, the system has boosted operator confidence, allowing them to operate closer to the process constraints and the minimum product specification. Making product closer to the specification reduces the amount of fiber give-away. It is anticipated that this reduction in fiber consumption will result in savings of over US$800,000 at current market pulp prices.

Mathematical models can be used to represent phenomena that occur within a paper machine. Those cause and effect relationships between input variables (stock flows, headbox pressures, basis weight and others) and the output sheet properties (MD and CD bending stiffness and consequently the geometric average, latic acid and peroxide absorption, edge wicking index, and ply-bond) are often not well known by engineers and scientists, making the use of traditional first principles modeling techniques impractical.

The mathematical models utilized in this system were developed using a gain constrained neural network empirical technique, as described in reference [1]. Neural networks are well suited to define complex, non-linear cause and effect relationships between input and output variables, eliminating the need for first principles equations. The operating data of a process, in this case a paper machine, is used to build these models. Accurate paper machine sheet property predictions developed from neural networks are used as soft sensors, replacements or enhancements for lab testing or feedback to closed control and optimization systems.

A simplified schema of a neural network is shown in Figure 1. The nodes represent transfer functions, which are highly interconnected. The degree of interconnectivity is defined by weights, shown as P in the figure. In order to build models, the data history series is presented sequentially to the network. For each set of input data at a certain instant, the outputs calculated by the neural net are compared to the real values (the lab analysis of the sheet properties), and an algorithm adjusts the weights of the network in order to minimize the difference between the real values and the calculated ones. The neural network is therefore a mathematical structure that adapts to the data. This process of adaptation is called training the neural net. After training, the relationships between inputs and outputs of the process are established.

Figure 1. Neural Network Structure

Theoretically, the construction of models using neural networks is simple, and the technique is well known. However in practice, this must be done carefully. The fundamental requirement is that the historical data is of good quality. As there are always shutdowns, instrument failures and other problems, the data must be carefully cleaned. Such cleaning needs the use of several techniques as well as common sense and knowledge of the process operation. In this application, several variables were moderately filtered while others were mathematically created inputs for the models.

Input variables that have a linear correlation coefficient higher than 0.8 present serious problems for the training of neural networks because they carry almost the same information; the algorithm can’t distinguish appropriately the difference of the influence of each variable, producing wrong process gains. The gain constrained training technique solves this problem, but must be used in conjunction with knowledge of the process. The resulting model is of very high quality as it is based on all of the information available about the process, both data and the engineer’s knowledge.

Any model that is to be used on-line is carefully analyzed for precision, coverage of full operational region of the process and the correct gains, among other features. For each model, validation is partially verified through the use of additional data that was not used for training.

Once an adequate model is obtained, it is useful for two primary purposes on a paper machine: virtual on-line analyzers (VOA) or control. To achieve this, specific software was employed, that was developed for ease of use at a reasonable cost. Specifically for VOA’s, it is important that the software has the additional functionality such as validation of real-time input data, alarming in case of abnormalities and calculations using the inferred values.

Finally, no model is perfect. Even if many input variables are used, the possibility always exists that disturbances not accounted for in the model will alter the paperboard properties. Typical examples are changes in the stock quality or slow drifts in the instruments that collect data for the model. These types of disturbances can be compensated for by a key feature of the virtual analyzers – they can be calibrated on-line during normal operation. Laboratory analysis can be used, regularly or at random, and a correction factor can be generated that compensates for the effects of the unmeasured disturbances, as presented in Figure 2 and detailed in reference [2].

Figure 2. The output of the virtual analyzer is calibrated on-line.

For this on-line application, once the historical process data from the paperboard machine was formatted and preprocessed, model development was performed off-line. The models were then transferred to an on-line computer that communicates with the mill’s distributed control system (DCS). A specific software module collects variable values, which are input to the on-line model every minute. The models generate a set of predicted values and the results are written back to the DCS for display to the operators, according to Figure 3.

Figure 3. The VOA software reads the input variables, performs the
calculations and writes the inferred results back in the DCS.

Process description and data used
Paper machine # 7 at Telemaco Borba uses three different pulps: unbleachead for the bottom and middle layers, and bleached at the top layer. The machine has three stock chests and three white water tanks with their associated fan pumps. At each of these tanks the chemicals are added: sizing agent, retention agents and starch, among others. The bottom and middle pulps are launched in the same fourdrinier through a single headbox. The top layer has its own specific headbox and uses another fourdrinier. The two wet sheets are joined at the presses and raw starch is used to glue them. The machine has seven groups of drying cylinders, after which a scanning head (frame # 3) measures basis weight, caliper and moisture. Two coaters are used, and after each one there is an additional scanning head. The machine has conventional controls for basis weight and moisture.

Due to the complexity of the machine, more than 150 variables were collected, at a 1-minute frequency. This represents either snapshots or a 1-minute average of the variables, without any other treatment such as filtering or averaging. Additionally, laboratory data was used from samples collected at the end of each reel. Eight months of data, corresponding to 6700 reels where used for the construction of the models.

Figure 4 shows a select group of variables over a five-day period. The paperboard properties and other analysis appear as steps, as the data system records the values of the variables and treats them as constant until the next analysis. For building the models, only the values of the variables at the moment of the changes were used. These variables are referred to/identified with the suffix “pontual” (instantaneous) in the graphics and tables that follow.

Figure 4. Some process variables and paperboard properties used in modeling.

The data shown has already been cleaned, taking out periods of shutdowns, sheet breaks, and problems with instrumentation and laboratory errors.

Bending stiffness models
The bending stiffness models were initially constructed using a large number of input variables, which were later reduced by applying the engineer’s knowledge of the paper machine and from analysis of results obtained from preliminary models. All the models are of the quasi-static type; they take into account the specific time delay of each input variable to the output. The properties of the sheet are referred to at instant zero while the input variables are delayed (the time delay, in minutes, is indicated in the figures and tables with a negative sign in brackets, after the name of the variable).

Stiffness is mainly related to caliper and basis weight of the sheet. However, other variables also influence stiffness, such as moisture, degree of pulp refining, jet to wire ratio, sizing agent, and retention agents. Basis weight and caliper have different influences over MD and CD stiffness. The detection of this difference as well as the influence of some of the less important variables was only possible from empirical modeling. This represents additional benefit – increased process knowledge for the plant engineers and operators.

Figure 5 shows some of the variables that have greater influence on MD and CD stiffness, ranked by the normalized sensitivity. Figure 6 includes the response curves of these properties (in the y-axis) to the input variables (in the x-axis, scaled from 0 to 100% of each variable range). Some non-linearities of the process can be seen by the curvature of the lines in this figure.

Figure 5. Bending stiffness input variables ranked by sensitivities.

Figure 6. Response curves of bending stiffness to some input variables.

Figure 7 shows the actual value (lab analysis) of the CD stiffness on the x-axis and the value calculated by the model on the y-axis for all paperboard grades produced by machine MP7. The standard deviation of the residue (difference between predicted and actual) is 1.9% of the range of CD stiffness. The distribution of this residue is shown in Figure 8 (the average of the error is almost zero). Using a timeline for the x-axis, the actual and the predicted values are plotted in Figure 9, during a period when several grades of paperboard were produced.

Figure 7. CD bending stiffness real and predicted values.

Figure 8. Residue distribution for all kinds of paperboards.

Figure 9. Laboratory and prediction of CD bending stiffness of various reels.

Both the MD and CD bending stiffness models are of high quality, this being due mainly to the strong influence of the basis weight and caliper over these properties.

Peroxide and lactic acid absorption index models
These two properties are similar in nature, primarily influenced by the chemical additives injected in the stock flow, particularly the sizing agent and starch. However, other variables such as moisture and density also influence the properties.

Figure 10 shows that, for lactic acid, the influence of sizing agent is positive, reducing absorption. On the other side, moisture and retention agents negatively affect the property. The high non-linearity of the absorption as a function of the glue content can also be seen.

Figure 10. Response of lactic acid to some input variables.

In Figure 11, the actual peroxide absorption (lab measurement) and the values calculated by the model are plotted for a selected period of time. The standard deviation of the residue is 4.1% of the range of the property. It also can be seen that the laboratory analysis of this property presents high variability.

Figure 11. Laboratory and prediction of peroxide absorption for various reels.

Ply-bond model
This property represents the energy necessary to separate the plies of the paperboard. The operational objective is that it must not be less than a certain minimum specification value.

Contrary to other properties, plybond depends on many input variables. The prediction results are reasonable, but more reliable models are still under development. The standard deviation of the residue is 6.3% of the range of the property (Figure 12).

Figure 12. Ply-bond real and predicted values.

On-line monitoring of the properties
Using the virtual on-line analyzers, the MP7 operators can now monitor in real-time (every 1 minute) the key properties of the paperboard being manufactured. This helps in two main quality aspects:

a) It is possible to take corrective actions on the properties and see the results during the production of the reel. Figure 13 shows a period of time in which five reels of the same paperboard grade were produced resulting in small variations in stiffness. The variable in steps is the CD bending stiffness as measured in the lab, while StiffCD_VOA is the result of the prediction. Each row represents one minute. It can be seen that stiffness can vary significantly during the production of a reel; the main cause is due to moisture variability. Based on the 1-minute information, operators can act so as to compensate for the most significant trends in the properties, stabilizing the values and avoiding violations of the specification limits.

Figure 13. CD bending stiffness monitoring at every minute.

b) It is possible to reach the specification for a new grade faster than previously. Figure 14 shows a situation before installation of the VOA’s, when there was a transition from a 190 g/m2 coated grade to a 266 g/m2 uncoated board. As can be seen, almost two reels were out of specification, with the target finally achieved at the third reel. The specification during a transition is now reached at the beginning of the first reel, because of the continuous monitoring using the VOA’s.

Figure 14. Grade transition before the use of VOAs.

Economic results
Using the virtual on-line analyzers, the improvement of the paperboard quality is significant, expressed as the result of less variability.

However, the most significant measurable economic benefits come from the possibility of producing paperboard closer to the limits of the specifications, reducing give away. Previously, with information only after the end of the reel, operators relied only on their experience and a too broad margin of safety was used. With the VOA’s as instantaneous and continuous guides, the operators are confident in reducing the operating margins.

In a review after an eight-month period of production, the mill staff was able to operate the process closer to minimum quality specifications. Bending stiffness was formerly maintained at a level 1.5% above the minimum specifications; the current typical operation is 0.7% above the minimum specification. This reduction in fiber give away, reflected in reduced basis weight has resulted in a savings of more than US$800,000/year based on current market pulp prices.

Next steps
Each quality property of paperboard depends on several process variables. Some can be manipulated; others are a consequence of factors that are beyond the operator’s actions (the type of furnish, etc.). Such disturbances are many and they act in different ways in the properties.

The control of MP7 machine is therefore a multivariable problem, which is difficult for the operators to manage. Klabin plans on implementing a non-linear model-based predictive controller to directly control the properties referred to in this paper. Better moisture control is also in the scope of the planned project.

In addition, there is the possibility of minimizing the overall cost of chemicals. For instance, sizing agent has a positive effect on the peroxide absorption but other additives have a negative effect. There are trade-offs that can be considered in the use of these multiple chemicals – a classic economic optimization problem. Off-line optimization has already been tested and Klabin plans to implement on-line optimization using technology that is already available in the market [4].

1. Training Feed-forward Neural Networks with Gain Constraints; E. Hartman; Neural Computation, 1999.
2. Analisadores Virtuais; M. Gerhard; Revista Intech Brazil, Feb/02.
3. Prediction and Control of Paper Machine Parameters Using Adaptive Technologies in Process Modeling; C.A. Schweiger, J. B. Rudd; TAPPI 1994 Process Control Symposium
4. Neural Network Based Model Predictive Control; S. Piché, J. Keeler, G. Martin, G. Boe, D. Johnson, M. Gerules; Neural Information Processing Systems (NIPS)’99 Conference.

About the authors:
Osvaldo Vieira is process engineer at Klabin S.A., Brazil, and can be reached at osvaldov@klabinpr.com.br; Enrique L. Lima is a professor at COPPE/UFRJ, Brazil, and can be reched at enrique@peq.coppe.ufrj.br; Ivo Neitzel, is a professor at Universidade Estadual de Maringá, Brazil, and can be reached at ivo@deq.uem.br; and Mário Gerhard is director, Pavilion Technologies, Inc., Brazil, and can be reached at mgerhard@pav.com.

For more information on Pavilion’s products in the pulp and paper industry,contact David Garcia at dgarcia@pav.com or 1 512 438-1525.