Applying Modern Data Analytics to Optimize Key Process Phases and Monitor Quality Parameters, TAPPICon25

This paper explores how modern data analytics technologies enable the identification and analysis of critical sequences, durations, and outcomes in key process phases, such as paper grade changes. By analyzing these transitions, inefficiencies can be pinpointed and addressed, streamlining grade changes and enhancing performance.

Through case studies, we demonstrate how advanced data analytics and automated soft sensor creation develop dynamic operational windows for critical measurements. These tools provide actionable insights, enabling mills to optimize parameters, reduce delays, and adapt to changing demands with precision.

A key outcome is the real-time identification of unnecessary delays during grade changes. Addressing these inefficiencies establishes optimal transition pathways, reducing changeover times, minimizing waste, and improving efficiency. Complementing this, automated soft sensor models predict critical process parameters, such as dry and wet tensile strength, with deviations of less than 5% from laboratory values, ensuring consistent product quality.

TAPPI conference proceedings and presentations, technical papers, and publication articles provide technical and management data and solutions on topics covering the Pulp, Paper, Tissue, Corrugated Packaging, Flexible Packaging, Nanotechnology and Converting Industries.

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