Optimizing Refining Energy and Freeness: An AI-Driven Approach to Efficient Papermaking, Advanced Coating Symposium (TAPPICon26)

Refining is a critical operation in papermaking that determines fiber bonding potential, drainage behavior and final sheet properties. At the same time, it is one of the most energy-intensive unit operations in a paper mill. Achieving a stable target freeness while minimizing specific energy consumption remains a persistent operational challenge due to variations in furnish quality, stock consistency, and refiner operating conditions. Conventional refining control strategies are largely heuristic, relying on operator experience and fixed load setpoints, often leading to energy inefficiencies and inconsistent pulp quality.

In many mills, refining-related data is distributed across PLC/DCS systems, online analyzers, laboratory databases, and historians without structural linkage. While individual parameters can be visualized, the lack of time-synchronized integration limits traceability between refining inputs, fiber response, and downstream quality KPIs. This fragmentation restricts root cause analysis and constrains advanced optimization efforts.

This study presents an AI-driven refining optimization framework developed using Haber’s Mt. Fuji platform. The system integrates structured data like, real-time refining process parameters, freeness and fiber response data, and unstructured data from plant SOPs/ manuals, images, log books and P&IDs to build predictive models that correlate refining energy input with freeness development. Using these models, agentic AI dynamically adjusts refiner load and dilution water to maintain freeness within a defined target band while minimizing specific refining energy. The system operates in a closed-loop autonomous mode and continuously improves its predictions by learning from live mill data.

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