Research and Application of Raw Paper Quality Prediction Model for Cardboard Papermaking Process
Jiwei Qian, Zhenglei He, Yi Man, Jigeng Li, Mengna Hong · 2025
The papermaking industry plays a crucial role in China&s;s economy and social development. With the increasing demand for cardboard driven by industries like e-commerce and logistics, ensuring the quality of raw paper has become essential. However, the current offline manual detection methods suffer from limitations such as long feedback cycles and unstable results. This study proposes the use of data-driven soft sensors to predict key paper properties and optimize the papermaking process. Four different data-driven methods were employed to establish soft sensor models, which showed high accuracy in predicting folding endurance, bursting strength, smoothness, and transverse ring compressive strength. These soft sensors facilitated real-time monitoring and process adjustments, leading to potential reductions in product costs, energy consumption, and greenhouse gas emissions. The optimized results demonstrate a possible total reduction of up to 17.3% in cost, energy consumption, and GHG emissions. The developed soft sensors offer significant contributions to the papermaking industry.