PAPER making! FROM THE PUBLISHERS OF PAPER TECHNOLOGY INTERNATIONAL ® Volume 8, Number 3, 2022
The results suggest that use of small or moderate amounts of PAE with AKD can be a viable option for paper mills facing problems related to the high usage of PAE. “Effect of Different Size Ratio and Filling Method on the Characteristics of Calcium Silicate-filled Paper”, Sheng Xu, Peng Xu, Pu-Qi Zhao & Yu-Ting Du, BioResources , Vol.17(3), 4196-4205 (2022). Process conditions were investigated relative to the use of the fly ash predesilication-alkali lime sintering method to extract calcium silicate, which is a by-product of alumina co-production technology, for use as paper filler. The ratio of different fiber raw materials, filler filling ratio, and filling method were studied, and papermaking experiments were conducted. The results showed that calcium silicate filler at a higher proportion (40%) paper can still maintain high mechanical properties. Under the same filling ratio and filling method of calcium silicate filler, the comprehensive performance of softwood pulp and hardwood pulp papermaking paper was found to be better than that of deinked pulp paper. TESTING “Alternative method for determining basis weight in papermaking by using an interactive soft sensor based on an artificial neural network model”, José L. Rodríguez-Álvarez, Rogelio López-Herrera, Iván E. Villalón-Turrubiates, Jorge L. García-Alcaraz, José R. Díaz-Reza, Jesús L. Arce-Valdez, Osbaldo Aragón-Banderas & Arturo Soto-Cabral, Nordic Pulp & Paper Research Journal , Vol.37(3) (2022). Currently, there are two procedures to determine the basis weight in papermaking processes: the measurements made by the quality control laboratory or the measurements made by the quality control system. This research presents an alternative to estimating basis weight-based artificial neural network (ANN) modeling. The NN architecture was constructed by trial and error, obtaining the best results using two hidden layers with 48 and 12 neurons, respectively, in addition to the input and output layers. Mean absolute error and mean absolute percentage error was used for the loss and metric functions, respectively. Python was used in the training, validation, and testing process. The results indicate that the model can reasonably determine the basis weight given the independent variables analyzed here. The R 2 UHDFKHG E\ WKH PRGHO ZDV ௗ DQG 0$( ZDV ௗJUDPVP 2 . Using the same dataset, the fine tree regression model showed an R 2 of ௗDQGDQ 0$(RI JUDPVP 2 . Additionally, a dataset not included in the building process was used to validate the method’s performance. The results showed that ANN- based modeling has a higher predictive capability than the regression tree model. Therefore, this model was embedded in a graphic user interface that was developed in Python. “Characterization of Paper Surfaces by Friction Profilometry”, Byoung Geun Moon, Na Young Park, Young Chan Ko & Hyoung Jin Kim, BioResources , Vol.17(4) 6067- 6078, (2022). Friction profilometry is a powerful technique that is suitable for the surface characterization of paper products. In this technique, a stylus-type contact method that resembles papermaking processes is used for evaluating the quality attributes of products. The surface characterization requires both surface roughness and friction measurements. At present, however, few reports have been available regarding characterization of the friction by the surface profilometric method. The objective of this study was to provide guiding principles of a stylus-type contact surface profilometry for determining the friction properties of paper. Another objective was to introduce the concept of the mean absolute deviation (MAD) from the average coefficient of friction as a new friction parameter.
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Technical Abstracts
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