Application of AI-based approach to control the papermaking process, TAPPI Journal March 2025

Application: This paper will benefit papermakers, process engineers, and mill management professionals through better utilization of available data in predictive process control capabilities. Impacts of the predictive algorithms will benefit production cost, efficiency, and reduction off-spec product.

Author: P.N. Arjunan, P. Venkat, V. Raghavan, and P. Pruszynski

ABSTRACT: This paper explores AI’s role in revolutionizing the pulp and paper industry, and specifically in predicting wet tensile strength (WTS) for specialty-grade papers. Leveraging eLIXA technology, a 90-day study achieved a 15% reduction in chemical dosage and an 80% decrease in wet tensile standard deviation. The real-time dosage prediction led to optimizing the wet strength resin (WSR) consumption and improved process reliability. The self-learning models exhibited adaptability to changing variables, ensuring their robustness. Overall, this study highlights AI’s transformative impact on efficiency, cost savings, and product quality within the dynamic landscape of papermaking. The approach used for wet strength optimization has been used to optimize other aspects of pulp and paper production.