Simulation Aids Pulp and Paper Production

Keeping Operating Points Normal

Figure 2: This 2-D score plot shows the relationship between two latent vectors and displays them in a Hotelling's ellipse. Points inside the ellipse show that their operating points are within the normal operating region, while any abnormal shift in the process variables also is indicated in the 2-D score graph. With the help of a loadings graph, Iggesund's operators can determine the root cause of a problem and isolate its fault.

However, one problem with conventional process control is variables are monitored individually, which indicates the state of only the individual variables and ignores the interaction of variables. So normal process monitoring gives the operators warning alarms when only that single process is out of specification.

On the other hand, multivariate techniques take into account the internal interactions among variables. Therefore, we can model the process on detecting univariance with individual variables. This can then be compared with the univariance for combined variables to identify the true process performance, which can then be used to identify when the process is unstable.
In the first stage of this project, we're finding the primary contributing chemicals that drive the pH in our pulp process. We find these chemicals through previous experiments and observing the effects of the different chemicals using multi-linear regression (MLR). A predictive control method using an ANN can then be trained and developed using the same data used for the MLR model.

In general, an ANN is an information processing paradigm inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. Like people, ANNs learn by example. They're typically configured for specific applications, such as pattern recognition or data classification, through a learning process. Also, an ANN is a nonlinear model, so it can predict the nonlinear process much better. An indication of how well it matches the actual output response is given by the root mean square error (RMSE), which improved from 0.13 to 0.045, providing far more accurate results.

Because of these capabilities, we used LabVIEW to develop a pH model that demonstrates that it's possible to model a nonlinear complex process. It confirms the pH meter readings and provides a basic online diagnostic tool for operators. Because the model is only taking in the key variables that affect pH, if there is a change in the incoming flows of one of these, then it's highlighted on the model. This change can be observed much sooner than normal due to process dwell time. The lower and upper control limits are set at three standard deviations, below and above the mean, respectively, so any difference between the actual and predicted pH values displayed on the screen indicates either a drift with the online pH meter or a problem with the incoming chemicals. All the chemical flows are displayed on the GUI under the pH trend. This means that the operators have an instant reading of all the chemical flows.

This model aided in monitoring and optimizing chemicals to control the pH more smoothly by displaying real-time information. This increased process stability is due to improvement of the control of a process-critical variable.