By Matt Taylor
Iggesund Paperboard (www.iggesund.com) in Workington, Cumbria, U.K., has five main-line refiners that each require 15 megawatts to operate, so saving energy is essential to reduce our operating costs. However, while we needed a solution to optimize our energy use, we also had to make certain that the refiners produce the required pulp for our final paperboard products.
Consequently, the model we recently implemented to predict paper stiffness is based on an artificial neural network (ANN) with a feed-forward, single-layer perception (FFSLP) structure. We chose this model over a multilinear regression (MLR) model because the process needs were nonlinear, and the ANN is more accurate at making predictions over a wide range of grades. In addition, using just one model for all paper grades is convenient and makes the system easy to use for refinery operators.
We also coupled the ANN with a multivariate data analysis (MVDA) approach for online monitoring because it allows for more adaptability to operating conditions, such as paper grade changes. The resulting nonlinear model accurately predicts the machine directional (MD) variation of the cross direction (CD) bending stiffness.
Our facility in Workington employs approximately 380 people and has a capacity of 200,000 tonnes of Incada per year. Incada is a multilayered folding box board (FBB) that is manufactured from fiber of known and traceable origin. It's produced at a site where pulp production is integrated with the paperboard mill (Figure 1).
Next, we designed a predictive model with National Instruments' (www.ni.com) LabVIEW software and ANN technology, combined with advanced adaptive control algorithms for closed-loop adaptive refiner control. The solution incorporates an offline tool that enables simulation of refiner loads for different pulp types. This gives operators and development engineers the opportunity to run different refining configurations and compare the predicted final paperboard quality measurements. By simulating the effects of changing parameters with LabVIEW, we saved time and money by avoiding expensive full-scale trials.
In addition, LabVIEW's data-logging and supervisory control (DSC) module makes it easy to run several models in parallel and provides straightforward online performance monitoring. Using the DSC module, we bounded the model within the normal operating conditions for the pulp mill. After variables move outside of these conditions, an alarm activates to notify an operator that a problem has occurred, and the system communicates which variable is at fault, as well as the best action to resolve it.
Modeling Leads to Online Monitoring
At present, Iggesund Paperboard is successfully using this new model in its online operations. Using a score plot, the system notifies machine operators when variables deviate from specifications. The score plot displays the principle component for each of the variables and updates them in one-minute intervals. For example, the scores for two latent vectors are plotted against each other to form a two-dimensional monitoring chart (Figure 2). The 2-D score graphs are then used to show the relationship between the two latent vectors and are displayed along with a 95% to 99% Hotelling's ellipse, which defines the normal operating region for the process. This allows operators to easily identify outliers.
All similar data points are clustered together in the 2-D score graph, with groupings of points representing an operating point for the process. These graphs may show one group of points to indicate a single operating point, or more than one cluster of points to indicate more than one operating point. If the data points remain within the Hotelling's ellipse, this indicates that the operating point is within the normal region. Any abnormal shift in the process variables—whether the basic correlation between variables remains intact or not—is clearly indicated in the 2-D score graph. With the help of a loadings graph, users can then determine the root cause of the problem and isolate the fault.
Consequently, the model we built with the DSC module gives our board machine operators real-time process information and clearly indicates when deviation from the required pulp quality setpoint has occurred. The pulp mill operators can select the desired setpoint and refiner energy to achieve optimal results.
As a result, Iggesund Paperboard has achieved significant benefits from the LabVIEW DSC modeling solution. Once the model was implemented, the plant experienced a 1% increase in throughput, which is a significant amount of energy savings for a large plant. In fact, cost savings from the energy reduction are estimated at £720,000 (USD1,117,755) per year.
Another benefit is better quality control and less repulping of board that isn't suitable for sale. Production losses due to re-pulping are £12,000 (USD18,630) per hour. We estimate that the model will improve board machine uptime by 30 hours per year for an annual cost savings of about £360,000 (USD558,877).