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Title: Moisture Estimation in Woodchips Using IIoT Wi-Fi and Machine Learning Techniques
For the pulping process in a pulp & paper plant that uses woodchips as raw material, the moisture content (MC) of the woodchips is a major process disturbance that affects product quality and consumption of energy, water, and chemicals. Existing woodchip MC sensing technologies have not been widely adopted by the industry due to unreliable performance and/or high maintenance requirements that can hardly be met in a manufacturing environment. To address these limitations, we propose a non-destructive, economic, and robust woodchip MC sensing approach utilizing channel state information (CSI) from industrial Internet-of-Things (IIoT) based Wi-Fi. While these IIoT devices are small, low-cost, and rugged to stand for harsh environment, they do have their limitations such as the raw CSI data are often very noisy and sensitive to woodchip packing. Thus, direct application of machine learning (ML) algorithms leads to poor performance. To address this, statistics pattern analysis (SPA) is utilized to extract physically and statistically meaningful features from the raw CSI data, which are sensitive to woodchip MC but not to packing. The SPA features are then used for developing multiclass classification models as well as regression models using various linear and nonlinear ML techniques to provide potential solutions to woodchip MC estimation for the pulp and paper industry.  more » « less
Award ID(s):
1805950
PAR ID:
10346614
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Computer aided chemical engineering
Volume:
9
ISSN:
2543-1331
Page Range / eLocation ID:
1657-1662
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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