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RECORD NUMBER: 124 OF 265

OLS Field Name OLS Field Data
Main Title Prediction of Biodegradation Kinetics Using a Nonlinear Group Contribution Method.
Author Tabak, H. H. ; Govind, R. ;
CORP Author Environmental Protection Agency, Cincinnati, OH. Risk Reduction Engineering Lab. ;Cincinnati Univ., OH. Dept. of Chemical Engineering.
Publisher c1993
Year Published 1993
Report Number EPA/600/J-94/488;
Stock Number PB95-136917
Additional Subjects Reaction kinetics ; Molecular structure ; Biodegradation ; Predictions ; Neural nets ; Nonlinear systems ; Pollution regulations ; Organic compounds ; Reprints ; Structure-activity relationships ; SBRs(Structure-biodegradation relationships) ; Respirometry ; Group contribution method
Holdings
Library Call Number Additional Info Location Last
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Status
NTIS  PB95-136917 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 03/06/1995
Collation 12p
Abstract
The fate of organic chemicals in the environment depends on their susceptibility to biodegradation. Hence, development of regulations concerning their manufacture and use requires information on the extent and rate of biodegradation. In the paper, a nonlinear group contribution method has been developed using neural networks; it is trained using literature data on the first-order biodegradation kinetic rate constant for a number of priority pollutants. The trained neural network is then used to predict the biodegradation kinetic constant for a new list of compounds, and the results have been compared with the experimental values and the predictionobtained from a linear group contribution method. It has been shown that the nonlinear group contribution method using neural networks is able to provide a superior fit to the training set data and produce a lower prediction error than the previous linear method.