PAPERmaking! FROM THE PUBLISHERS OF PAPER TECHNOLOGY Volume 7, Number 2, 2021
be determined by the user. Dusting is said to be a result of poor surface strength which can be caused by poor bonding of particles on paper web or poor adhesion of surface size. The purpose of this thesis was to find out whether the results of dust measurements have a correlation with surface strength, tensile strength, filler content, surface size amount, porosity and surface roughness. The filler content of paper affects the dusting of paper. In addition to filler amount, the particle shape, size and chemical behaviour are factors that affect dusting. Distribution of fillers and fines in paper network is a factor affecting dusting and those can be affected on paper machine forming section. Dusting of paper can be reduced with surface sizing. WASTE TREATMENT “ Neighborhood component analysis for modeling papermaking wastewater treatment processes ” , Yuchen Zhang, Jie Yang, Mingzhi Huang & Hongbin Liu, Bioprocess and Biosystems Engineering , online (2021). It is of great importance to obtain accurate effluent quality indices in time for pulping and papermaking wastewater treatment processes. However, considering the complex characteristics of industrial wastewater treatment systems, conventional modeling methods such as partial least squares (PLS) and artificial neural networks (ANN) cannot achieve satisfactory prediction accuracy. As a supervised metric learning method, neighborhood component analysis (NCA) is able to significantly improve the prediction performance by training an appropriate model in metric space using the distance between samples for papermaking wastewater treatment processes. The results on two data sets show that NCA has a higher prediction accuracy compared with PLS and ANN. Specifically, NCA has the highest determination coefficient (R2) and the lowest root mean square error in a benchmark simulation data set. On the other hand, the results on the data from an industrial wastewater process indicate that NCA has better modeling accuracy and its R2 increases by 32.80% and 29.08% compared with PLS and ANN, respectively. NCA provides a feasible way to realize online monitoring and automatic control in wastewater treatment processes. “ Application of aluminum sulfate in the treatment of papermaking white water ”, X Ming, Q. Li & W. Jiang, BioResources ; Raleigh Vol.16 (1), pp.1382-1393. Physical chemical methods were used to treat papermaking white water used to produce plant fiber mulch sheet that contained fine fibers and inorganic fillers as suspended solids. The ordinary chemical oxygen demand (CODCr) was obviously reduced after the papermaking white water was treated by the flocculant. By comparing three different coagulants (aluminum sulfate (Al2(SO4)3), poly-aluminum chloride (PAC), and poly(diallyldimethylammonium chloride) (PDADMAC)) and flocculant (poly-acrylamide copolymer (PAM)) to process papermaking white water, it was found that Al2(SO4)3 had the best coagulation effect and the lowest cost. The best flocculation conditions were 2,733 mg/L of Al2(SO4)3 and 4.52 mg/L of PAM to treat the papermaking white water. Under the best flocculation conditions, the CODCr was less than 300 mg/L. The goal of closed recycling and zero discharge of white water in the production process of plant fiber mulch sheet was realized. “ Quality-related monitoring of papermaking wastewater treatment processes using dynamic multiblock partial least squares ”, Jie Yang, Yuchen Zhang, Lei Zhou, Fengshan Zhang, Yi Jing, Mingzhi Huang & Hongbin Liu, Journal of Bioresources and Bioproducts , online 7 April 2021. Environmental problems have attracted much attention in recent years, especially for papermaking wastewater discharge. To reduce the loss of effluence discharge violation, quality-related multivariate statistical methods have been successfully applied to achieve a robust wastewater treatment system. In this work, a
Technical Abstracts
Page 9 of 11
Made with FlippingBook Online newsletter maker