Paper plants have hundreds of PID control loops that can cause process variation and contribute to quality issues, if not maintained in a healthy condition.
At the same time, while Skjern Paper has extensive quality assurance systems, the team does not have many real-time ways to measure paper quality, making real-time quality control difficult. Operators would check quality samples for a whole reel of paper at the end of a production run, which involved a delayed lab analysis and the inability to adjust production earlier in the process.
Proficy CSense at Skjern Paper helps reduce waste and control variationWith a goal of avoiding or reducing 5% of quality rejects, especially when switching between different products, Møller took advantage of free consulting with a Velotic AI and ML expert to jump start the analytics project.
In six hours of consulting, Skjern Paper captured insights from the initial project. The team used Proficy CSense to:
- Analyze: Used available data to discover causes of quality variation and rejects
- Monitor: Monitored the health of PID control loops to reduce process variation
- Predict: Created a predictive model from available data to predict product quality in real time, enabling real-time quality control to reduce quality rejects
Proficy CSense capabilities helps Skjern Paper reduce quality rejects, especially when switching between different products.Quality Prediction and Analysis: Paper Burst Strength
The team built a model in CSense around a quality parameter related to the Mullen burst strength of the paper. The Mullen Burst Test is an industry standard to measure the paper’s physical strength and fiber bond. Skjern Paper has 20 data points inputting to the CSense model, which predicts the burst strength parameter.
Burst strength is a sensitive measurement, according to Møller, and a hard one to start with – however, even with that challenge, the team saw early predictive success.
“We built a rather fine model and compared data from the model with actual quality data later,” Møller explains. “It is not completely aligned but showing good results. I’m quite pleased with it.”
By examining data and applying the capabilities of CSense, Møller was able to discover possible causes of errors in the production line. When producing paper, manufacturers add dewatering chemicals. Møller discovered in the production process that they are producing scrap due to adding too much chemical to the pulp mix.
“The model provides instant feedback on the differences in the level of the chemical,” Møller says. “It was the error in the production. When we had problems with dewatering, we would add more chemical, but in this case, we can add less. "
"We had thought that more chemical was better in the production, but we have new insight with CSense. Now, we can reduce the amount of chemical used and reduce scrap – which decreases our costs. It is a great capability that gave us benefit straight away."