Correlating Aggregates Structure with PEL Characteristics Using an Experimental Design Methodology. (2015)

• Record Type: Journal Article
• Authors: Rasteiro, Maria G.; Pinheiro, Ineide; Ferreira, Paulo J.; Garcia, Fernando A.; Wandrey, Christine; Ahmadloo, Hamideh; Hunkeler, David
• Is Part Of: Procedia engineering. Volume 102(2015)
• Page Start: 1697
• Page End: 1706
• Publication Date: 2015
• Subjects: Flocculation -- Polyelectrolytes -- LDS -- Scattering exponent -- Partial Least Squares Regression.; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2015.01.305
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Aggregation of particles is fundamental for improving the performance of many solid/liquid processes. Aggregation can be induced by different means, one of the most common being based on the addition of polymeric additives, namely polyelectrolytes (PEL). In this work we have studied the flocculation of precipitated calcium carbonate (PCC), used as filler in papermaking, induced by a range of cationic polyacrylamides with different structures, varying in molar mass and degree of branching. The flocculation process was monitored continuously using the LDS technique. Using an experimental design strategy it was possible to produce a model which allows us to relate the aggregates structure, described by the scattering exponent (SE), with the PEL characteristics and concentration (R 2 around 79%). The Partial Least Squares (PLS) regression method was selected to perform the regression, given the significant collinearity among the input variables. The results obtained lead us to conclude that the polymer intrinsic viscosity (related with the PEL molar mass and charge density, and supplying information about the volume of the polymer molecule in the solution) and the degree of branching, are the parameters with a stronger influence on the resulting flocs structure. The PLS model developed was successfully validated using an independent data set, which provides confidence on its consistency and prediction accuracy.