In today’s pulp and paper industry, increasing profitability simply by increasing output isn’t very effective—there’s no guarantee that the extra product you push out will ever be purchased. Reducing variability in your process, however, is a reliable way to increase your mill’s profitability. It can also lead to cost savings thanks to reductions in energy and chemical usage, off-spec tonnage, and even maintenance downtime.
What Causes Process Variability?
So, what causes variability in the process? There are a lot of different things that can lead to it, including raw material variation, sensor drift, control loop tuning, grade changes, and human factors.
Implementing advanced process control strategies can significantly minimize this variability. Producers are implementing these strategies into pulp and paper mills all around the world. Keep reading to learn how advanced process control strategies can be implemented, and benefit your mill.
Advanced Process Control
Advanced process control, or APC, provides solutions for pulp and paper mills in the way of reducing process variability. How does it do this? By applying a combination of predictive controls and pulp-tracking techniques. Pulp tracking monitors the performance of various key pulp properties throughout the process. For example, level of moisture content in wood chips, pulp conductivity in brown stock washers, chemical dosage in the first stage of bleaching, etc.
These things are tracked all the way up to the location of interest. Once these variables are transformed, a soft sensor model is used in the appropriate APC module for online prediction of controlled variables. For example, if the off-spec pulp is produced for ten minutes at the digester, the system can predict when this slug of pulp will reach the various bleaching stages and adjusts bleaching parameters accordingly.
It also serves as a diagnostic and visualization tool for the operators. If off-spec pulp or increased variability is observed at an intermediate stage in bleaching, the operator can drill down and determine the possible sources of this variability. In the case of grade changes, the tracking function helps you in accurately predicting when the new grade would arrive at a given location along the fibre line.
Applying APC in Your Pulp Mill
Pulping, brown stock washing and bleaching are the main fibre line areas that can benefit from reduced variability. When it comes to brown stock washing, the main objective is to achieve the removal of impurities in the pulp with a minimal amount of fresh water consumption. Sub par performance at this stage can significantly affect the stability of operation, as well as lead to increased operating costs—because of higher energy requirements and higher bleach use.
In the recovery area of your pulp mill, APC techniques can also be applied. Modules have been developed for evaporation, causticizing, and the lime kiln as well. In fact, a British Columbia pulp mill was looking to reduce their fuel consumption, while also improving the quality of the lime they were producing. Through the Lime module, this mill was able to achieve a 5% reduction in energy consumption and more consistent residual carbonate in the re-burnt lime.
In recent research, experts have found that there are essentially three important factors when it comes to success using APC. First, you need good quality historical data, and good quality live data. This will, of course, depend on the age of your underlying distributed control system or DCS. It can be hard to extract the data you need from older DSCs, as well as to interface the optimization system with it.
Second, you’ll need good sensors and accurate measurements. It’s important to note that some measurements are critical to have, while others are just good to have. If your measurement system is not accurate, the APC won’t deliver the results you expect.
Third, you have to be open to change—not only changing the way you operate your process but also open to deliberately putting your process to the test. This is what will provide the information you need to build the process model, and understand how all of your process variables interact with one another. Being willing to explore other avenues and other ways of doing things is crucial to success.
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