Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/104343
Author(s): José Tomás Albergaria
F. G. Martins
M. C. M. Alvim-Ferraz
C. Delerue Matos
Title: Multiple linear regression and artificial neural networks to predict time and efficiency of soil vapor extraction
Issue Date: 2014
Abstract: The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability. Â(c) 2014 Springer International Publishing.
URI: https://hdl.handle.net/10216/104343
Related Information: info:eu-repo/grantAgreement/FCT - Fundação para a Ciência e Tecnologia/Projetos Estratégicos/PEst-C/EQB/UI0511/2013/PROJECTO ESTRATÉGICO - UI 511 - 2013-2014/UI0511
Document Type: Artigo em Revista Científica Internacional
Rights: restrictedAccess
Appears in Collections:FEUP - Artigo em Revista Científica Internacional

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