Application of advanced data treatment to predict paper properties

Papermaking is an industrial process that is becoming more competitive nowadays. In this process there are numerous techniques and measurements to indicate paper quality. To increase competitiveness a good control of paper quality is needed through paper properties predictions from different process measurements. However, complex physico-chemical processes take place during papermaking, and thus, paper property predictions are not easy to obtain, especially in the wet-end area. In the wet end flocculation takes place, which will determine the floc properties during the formation of the sheet, and therefore, it will influence retention, drainage and formation. These strongly affect the runnability of the machine and the properties of the final product, and thus, using wet-end measurements for the predictions implies advanced data treatment. Artificial neural networks have been used in this article to predict newsprint paper properties from wet-end parameters. Results show that formation and strength properties can be robustly predicted from pulp properties at the headbox, flocculation parameters and machine speed.