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Aromatic compounds biodegradation under anaerobic conditions and their QSBR models [An article from: Science of the Total Environment, The]
Book Details
Author(s)H. Yang, Z. Jiang, S. Shi
PublisherElsevier
ISBN / ASINB000RR9L0Q
ISBN-13978B000RR9L05
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Science of the Total Environment, The, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Anaerobic biodegradability of 46 kinds of aromatic compounds was tested and assessed in integrate. These aromatic compounds were classified into readily, partially and poorly biodegradable compounds after their integrated assessment indices (IAI) were calculated. Some rules of anaerobic biodegradation of them were drawn. Stepwise regression and backpropagation artificial neural network (BP-ANN) methods were applied to establish quantitative structure biodegradability relationship (QSBR) based on the assessment results. In QSBR models, five molecular structure descriptors, energy of the highest occupied molecular orbital (EHOMO), total energy (TolE), molar refractivity (MR), the logarithm of the partition coefficient for n-octanol/water (LogP), and standard Gibbs free energy (G), were included. After analyzing the sensitivity of variables in QSBR models, it was found that the key molecular structure descriptors affecting anaerobic biodegradability of aromatic compounds were TolE and MR, which were in direct proportion to the anaerobic biodegradability of aromatic compounds.
Description:
Anaerobic biodegradability of 46 kinds of aromatic compounds was tested and assessed in integrate. These aromatic compounds were classified into readily, partially and poorly biodegradable compounds after their integrated assessment indices (IAI) were calculated. Some rules of anaerobic biodegradation of them were drawn. Stepwise regression and backpropagation artificial neural network (BP-ANN) methods were applied to establish quantitative structure biodegradability relationship (QSBR) based on the assessment results. In QSBR models, five molecular structure descriptors, energy of the highest occupied molecular orbital (EHOMO), total energy (TolE), molar refractivity (MR), the logarithm of the partition coefficient for n-octanol/water (LogP), and standard Gibbs free energy (G), were included. After analyzing the sensitivity of variables in QSBR models, it was found that the key molecular structure descriptors affecting anaerobic biodegradability of aromatic compounds were TolE and MR, which were in direct proportion to the anaerobic biodegradability of aromatic compounds.
