Calculating hansen solubility parameters of polymers with genetic algorithms

Abstract

In materials engineering, it is often essential to know what are the best solvents to process any polymer, and employing methods based on Hansen solubility parameters are an effective way to find them. In this work the Hansen solubility parameters of polyether sulfone, lignin, and bitumen have been calculated by an alternative optimization procedure. It has been shown that, by applying an evolutionary strategy to Hansen's correlation method, it is possible to improve the fitting of solubility spheres. Compared with previous calculations, most quality‐of‐fit parameters are optimized. As a result, the sphere radii are reduced and, except for lignin, at least one of the solubility parameters is considerably changed (by 0.9–1.5 MPa1/2). Shortcomings of the correlation method are also pointed out, such as lack of data quality evaluation on set of solvents and uncertain character for partially solving solvents. At least the former could be handled by a proposed parameter called fill factor. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 39696.

Solvent screening for the fractionation of industrial kraft lignin

“…Recently, genetic algorithms were applied for calculating lignin HSPs ( Vebber et al 2014 ), and the obtained values (21.7, 14.2, 16.9, and 13.5 MPa 1/2 for δ D , δ P , δ H , and R 0 ) are in good agreement with previously published data ( Hansen and Bj ö rkman 1998 ). Based on these values, the REDs were calculated for each of the aforementioned solvents and compared with the amount of lignin solubilized x sol .…”

“…The HSPs of lignin used for the calculation could be the source of error. Lignin HSPs calculated by Hansen (1969) were referred to milled wood lignin (MWL), whereas Vebber et al (2014) gave no hints to the type of lignin considered. In view of the structural differences between KL and MWL, significant differences in terms of solubility parameters are expectable.…”

Convex solubility parameters for polymers

“…Our review of the literature revealed that the solubility parameters of a solute can vary significantly just by changing the algorithm used to compute the sphere, that is, even without changing the observed data, different spheres can be obtained using a different algorithm to fit the data. For example, independent calculations by Vebber et al 29 and Gharagheizi 30 for the d D , d P , and d H parameters of polyethersulfone (PES) vary individually by as much as 20%, and there is a 24% discrepancy in the computed radius of each Hansen sphere despite using the exact same data set.…”

“…Nevertheless, this gives rise to discussion of statistical measures such as sensibility (the proportion of all observed good solvents that lie inside the solubility region) and specificity (the proportion of all bad solvents that lie outside the solubility region). 29 4. The requirement of a spherical solubility space implies that the intermolecular interactions and thermodynamics related to the dispersive, polar, and hydrogen-bonding parameters equally influence the solubility behavior.…”

“…Since its introduction, several efforts have been directed at the development of more sophisticated numerical algorithms for computing the center and radius of the Hansen sphere, including optimization strategies, genetic algorithms, and even nonlinear programming approaches. [28][29][30] However, despite its practicality and convenience, a few critical issues with the Hansen solubility theory have been discussed over the years.…”