This research paper examines water-treatment limitations in pulp and paper mills, highlighting how water closure affects quality and productivity, and explores new technologies needed to meet rising sustainability and regulatory demands.

This research paper highlights AI's ability to optimize Wet Tensile Strength through real-time dosage prediction, achieving major reductions in chemicals and variability while improving stability, scalability, and overall production efficiency across pulp, paper, and packaging systems.

This research paper examines how AI and deep learning unlock value from rapidly growing unstructured mill data, improving product quality and productivity by optimizing wet-end chemistry, reducing deposition issues, and significantly enhancing FPR and FPAR across grades.

This research paper examines how AI and deep learning unlock value from rapidly growing unstructured mill data, improving product quality and productivity by optimizing wet-end chemistry, reducing deposition issues, and significantly enhancing FPR and FPAR across grades.

This research paper demonstrates how AI and automated filtrate turbidity measurement can accurately predict charge demand and optimize coagulant dosage, improving chemical efficiency, process stability, and product quality in pulp and paper systems with high anionic trash.

This research paper highlights AI/ML as transformative tools for mill ETPs, offering predictive maintenance, advanced control, and resource savings, validated through a case study achieving superior water quality with reduced chemical and energy inputs.

This research paper examines how AI and deep learning unlock value from rapidly growing unstructured mill data, improving product quality and productivity by optimizing wet-end chemistry, reducing deposition issues, and significantly enhancing FPR and FPAR across grades.

This research paper showcases how eLIXA® leverages AI/ML to optimize retention, drainage, and chemical dosing in molded fiber mills, delivering improved drainage, reduced fluoro-chemical use, higher productivity, and substantial energy and resource savings.

This research paper examines energy consumption as a critical industry bottleneck, showing how eLIXA® deploys AI/ML to enhance retention and drainage, lower steam and power use, and boost overall sustainability and productivity in papermaking.

This research paper highlights how AI and smart automation shift pulp and paper plants from reactive to proactive control, optimizing chemical dosing, improving OEE, reducing manual intervention, and delivering measurable efficiency gains across key wet-end and effluent processes.

This research paper examines how AI and deep learning unlock value from rapidly growing unstructured mill data, improving product quality and productivity by optimizing wet-end chemistry, reducing deposition issues, and significantly enhancing FPR and FPAR across grades.