ML Algorithm Synthesizing Domain Knowledge for Fungal Spores Concentration Prediction

• URL: http://arxiv.org/abs/2309.13402v1
• Date: Sat, 23 Sep 2023 15:27:14 GMT
• Title: ML Algorithm Synthesizing Domain Knowledge for Fungal Spores Concentration Prediction
• Authors: Md Asif Bin Syed, Azmine Toushik Wasi and Imtiaz Ahmed
• Abstract summary: Fungal spore concentration is a crucial metric that affects paper usability. Current testing methods are labor-intensive with delayed results, hindering real-time control strategies. This paper showcases a promising method for real-time fungal spore concentration prediction, enabling stringent quality control measures in the pulp-and-paper industry.
• Score: 3.4836961035265217
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The pulp and paper manufacturing industry requires precise quality control to ensure pure, contaminant-free end products suitable for various applications. Fungal spore concentration is a crucial metric that affects paper usability, and current testing methods are labor-intensive with delayed results, hindering real-time control strategies. To address this, a machine learning algorithm utilizing time-series data and domain knowledge was proposed. The optimal model employed Ridge Regression achieving an MSE of 2.90 on training and validation data. This approach could lead to significant improvements in efficiency and sustainability by providing real-time predictions for fungal spore concentrations. This paper showcases a promising method for real-time fungal spore concentration prediction, enabling stringent quality control measures in the pulp-and-paper industry.

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