A Mathematical Model for Paper Break Prediction Based on Machine Learning Technology

To effectively predict paper breakage events in pulp and paper production lines and allow for better equipment debugging time, this study focuses on the development of a predictive model that can provide early warnings to production staff. By analyzing the existing fault data from a papermaking enterprise, relevant production and operational parameters were extracted and preprocessed to identify key characteristic features from the raw production data. These characteristic parameters formed the foundation for building a predictive mathematical model capable of forecasting paper breakage incidents. The model's design takes into account the unique operational characteristics of the papermaking process, including various machine settings and environmental factors that contribute to paper breakage. To validate its effectiveness, the model was applied in an actual production setting, and its performance was rigorously tested. The results revealed that the model could provide a reliable alarm approximately 11 minutes and 30 seconds before a paper breakage event occurred, giving operators sufficient time to adjust equipment settings and prevent the breakage from escalating. This early warning system not only helps reduce the immediate economic losses associated with paper breakages but also contributes to overall operational efficiency by minimizing downtime and optimizing maintenance schedules. The success of this predictive model demonstrates its potential application in other similar production environments, providing significant value in improving the stability and productivity of manufacturing systems.