A Fuzzy ARTMAP‐Based Quantitative Structure—Property Relationship (QSPR) for the Henry′s Law Constant of Organic Compounds.

Abstract

Quantitative structure-property relationships (QSPRs) for estimating a dimensionless Henry's Law constant of organic compounds at 25°C were developed based on a fuzzy ARTMAP and back-propagation neural networks using a heterogeneous set of 495 organic compounds. A set of molecular descriptors developed from PM3 semiempirical MO-theory and topological descriptors (second-order molecular connectivity index) were used as input parameters to the neural networks. Quantum chemical input descriptors included average molecular polarizability, dipole moments (total point charge, total hybridization, and total sum), ionization potential, and heat of formation. The fuzzy ARTMAP/QSPR correlated Henry's Law constant for -6.72 e logH e 2.87 with average absolute errors of 0.03 and 0.13 logH units for the overall data and the test set, respectively. The optimal 7-17-1 back-propagation/QSPR model was less accurate and exhibited larger average absolute errors of 0.28 and 0.27 logH units for the validation (recall) and test sets, respectively. The fuzzy ARTMAP-based QSPR was superior to the back-propagation and multiple linear regression/ QSPR models for Henry's Law constant of organic compounds.

Biofiltration of a styrene/acetone vapor mixture in two reactor types under conditions of styrene overloading

“…This effect could be enhanced by a lower effective aqueous layer thickness in the BF due to a limited water supply. Acetone mass transfer through the aqueous medium is higher compared to styrene because of the two orders of magnitude difference between the corresponding Henry's law coefficients (Staudinger and Roberts 2001;Yaffe et al 2003).…”