Data-Driven Key Parameter Prediction of Dryer Section in the Papermaking Process

To improve the energy efficiency of the dryer section in the papermaking process, it is essential and significant to accurately predict the key parameters of the dryer section. Compared with conventional mechanism-based prediction models, data-driven methods effectively establish the parameter prediction models. This work selects the feature variables from the production data, and then adopts four kinds of data-driven prediction models for comparative analysis. All the prediction models are performed on two testing cases with high and low correlation degrees respectively. The testing results provide useful experience and insights for the operators and managers in the pulp and paper industries.