2023/09/23 by Md Asif Bin Syed, Azmine Toushik Wasi, Syed, Md Asif Bin +3
Chemistry · Engineering · #Algorithm #Artificial intelligence #Computer science #Data mining #Domain knowledge #Engineering #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine learning #Metric (unit) #Mineral Processing and Grinding #Operations management #Pulp (tooth) #Spectroscopy and Chemometric Analyses #Usability
paper · pdf · doi:10.48550/arxiv.2309.13402
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.