Supporting machine operators in paper production using machine learning based state estimation and user assistance system

The paper production industry has witnessed significant advancements in automation, yet human expertise remains crucial for process control due to its complexity.This study proposes a knowledge and data-based assistance system to support machine operators in optimizing the production process.The system employs state/situation recognition through data analysis, utilizing machine learning algorithms and Nelson rules on sensor data to estimate or to detect the process state.By integrating error codes from process control systems, the system generates suggestions for potential solutions.Remarkably, over 50 % of the cases reveal that the top-ranked suggestion proves to be the correct solution.Additionally, the proposed system facilitates the detection of long-term tendencies that often go unnoticed, enhancing the operator's ability to identify and address such trends.This research contributes to the ongoing efforts in automating paper production while leveraging the expertise of human operators in ensuring efficient and error-free processes.