ML Algorithm Synthesizing Domain Knowledge for Fungal Spores Concentration Prediction

TL;DR

This paper presents a machine learning approach using domain knowledge and time-series data to predict fungal spore concentrations in pulp and paper manufacturing, enabling real-time quality control.

Contribution

It introduces a Ridge Regression-based model that integrates domain knowledge for accurate, real-time fungal spore concentration prediction, improving industry efficiency.

Findings

Achieved an MSE of 2.90 with Ridge Regression

Demonstrated potential for real-time quality control

Improved prediction accuracy over traditional methods

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.