Real-time In-line Stickies, Dirt, Shives, and Fiber Morphology Measurement, 2016 Pulping, Engineering, Environmental, Recycling, Sustainability (PEERS) Conference Proceedings


This paper presents an in-line sensor built on a machine-learning algorithm to detect and classify contaminants such as stickies and dirt in a pulp stream. Measurement trends from the in-line sensor were validated against existing stickies measurements in multiple paper mills before operating decisions were made based entirely on these real-time measurements. Mills where in-line stickies measurements have been implemented have, on average, been able to make process decisions 8-10 hours faster than mills using laboratory measurements. Another application of this sensor is to measure fiber morphology and shives during the pulping process. This application is essential for monitoring pulp quality and performance of unit operations such as digesters, refiners, and screens. Detection accuracy is not affected by the presence of bubbles, flocculation, and consistency variation in pulp. A trial using the in-line sensor was conducted throughout an old corrugated container plant to generate profiles of stickies and dirt. It was found that in this specific recycling line, primary fine screens were inefficient for stickies removal, while cleaners did not play a positive role in dirt removal. The in-line sensor was found to be a powerful tool for providing real-time feedback of equipment performance in paper recycling plants. 

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Author: Suvajit Das and Laura Chen
Real-time In-line Stickies, Dirt, Shives, and Fiber Morpholo
Real-time In-line Stickies, Dirt, Shives, and Fiber Morphology Measurement, 2016 PEERS
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