Golden Run: A Data-Driven Framework for Optimizing Paper Machine Performance, TAPPICon26

This paper presents the development and implementation of a Golden Run application at a pulp and paper company, aimed at improving production consistency and operational excellence on a paper machine line. The solution is a web-based application built on top of Cognite Data Fusion, using Python and Angular, and integrates data from the PI System and plant quality databases into a unified, contextualized environment.

Production runs are compared with historical runs classified as Golden Runs, enabling the identification of best-performing operational practices. New runs are continuously benchmarked against this reference, allowing near-real-time detection of deviations in key process variables such as quality parameters, refining conditions, machine speed, steam consumption, and others.

The project followed an agile and collaborative approach, with frequent interaction among operators, process engineers, and the development team. Statistical analyses and interactive dashboards supported the identification of performance offenders and guided corrective actions. Additionally, correlation analyses between pulp-area variables and paper machine quality parameters provided new insights into process interactions.

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As a result, the Golden Run application became a daily operational tool for both process engineers and operators, displayed 24/7 in the control room to support data-driven decision-making, reduce process variability, and align production with optimal operational standards.