Company
North American Board Producer
Challenge: Inefficient Chemistry Dosage Scheme for Wet-Strength Chemistry
A North American board producer wanted to reduce raw material consumption while optimizing wet tensile quality. The mill employed an inefficient dosage scheme for wet-strength chemistry. The dynamically changing nature of continuous manufacturing and the periodic reel-to-reel quality measurement environment of tissue-making presented operators with a challenge for manual optimization.
Solution: Fine-Tuning Chemistry Dosage With AI
The board producer implemented Solenis OPTIX™ Applied Intelligence, powered by ProcessMiner. A machine-learning, predictive analytics platform with autonomous control capabilities, OPTIX delivers a virtual measure of wet tensile quality in real time and uses machine learning capabilities to remain robust and accurate in the face of changing process conditions. Using artificial intelligence (AI) to make data-driven process adjustments, the wet strength chemistry dosage is finely tuned to drive wet tensile quality to the target.
Results: Achieving Immediate Cost Savings
Over a two-month period of using OPTIX’s autonomous control, the mill realized a 14% reduction in wet strength chemistry usage across all grades. The autonomous control algorithms adjusted the wet strength chemistry dosage up or down to ensure target adherence of the wet tensile quality parameter. This unprecedented, AI-driven autonomous control optimized wet tensile quality by reducing variation by 15%, all while avoiding off-quality production. This improvement in target adherence allowed the mill to then lower wet tensile targets to drive further optimization.
Disclaimer: This case study highlights the strategic partnership between ProcessMiner and Solenis in pulp and paper manufacturing. ProcessMiner’s AI-driven solutions also extend beyond the pulp and paper industry, offering optimization across multiple industries.