Analysis of the Behavior of Mottling in Coated Board using Neural Networks

Quality monitoring of paperboard depends on the measurement of several properties. Part of these properties have online devices to do measurements while another part can only be measured in the laboratory, an activity that sometimes require more time than a production of one entire jumbo roll or generate waste until fix the production. The advantage to use mathematical modeling as the neural networks is the ability to 'predict' online the product final properties through the machine's information such as speed, flow of pulp, coating weight and the quality of fiber as degree of refining and whiteness. One of the properties used for assessing the quality of paperboard is the mottling that describes a marbled appearance on the paperboard surface. Mottling is determined using the method STFI™ Mottling who is characterized by a coefficient of variation of reflectance or standard deviation - defined by the methodology of the equipment. This property when out of parameters affects the quality of the final printed package, giving unsightly appearance. The focus of this study is to determine parameters by mathematical modeling that influence the mottling in order to provide conditions for machine's operators to perform the process, reducing the variation of this property and keep the values inside the specified limits. The model was developed from historical data of 6 months of paperboard machine operation. The results indicated that mottling is mainly influenced by the temperature of the dryer after coating process. Application—Statement: A further understanding of the mechanisms that cause mottling would help to optimize the paperboard quality.