NDLI: Neural networks for fitting PES data distributions of asphaltene interaction

Content Provider	IEEE Xplore Digital Library
Author	Gasca, E. Pacheco, J.H. Alvarez, F.
Copyright Year	2009
Description	Author affiliation: División de Estudios de Posgrado e Investigación, Instituto Tecnológico de Toluca. Av. Tecnológico s/n, Metepec 52140, Edo. Méx., A.P. 890, México (Gasca, E.; Pacheco, J.H.) \|\| Programa de Ingeniería Molecular, Instituto Mexicano del Petróleo. Eje Central Lázaro Cárdenas 152, D. F. 07730 México (Alvarez, F.)
Abstract	Neural networks methodology is a tool to get potential energy surface (PES) in cases where there is too much dispersion of data; hence, a binding energy fitting can be found with this methodology on asphaltene-asphaltene molecular interaction. A data distribution of intermolecular pair potential (U) interaction in a vacuum between two molecular asphaltenes systems using compass classical force field has been previously reported (Energy Fuels 2006, 20, 195). In the latter, all possible interactions between the species were taken into account. Focusing in their data distribution, we have applied neural networks on the following molecule-molecule geometry orientations with the purpose of obtaining energy vs. contact distance, which is the minimum distance where the interacting species is not equal to zero: i) face-to-face distribution of asphaltene-asphaltene interactions; ii) all geometry asphaltene-asphaltene discrete distributions, and iii) the random distribution of asphaltene-asphaltene interactions. Neural networks fit provide a potential energy surface through a function approximation, for a data distribution of high dispersion; hence a binding energy is found with this methodology.
Starting Page	946
Ending Page	952
File Size	3318587
Page Count	7
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178950
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Surface fitting Potential energy Petroleum Vacuum systems Geometry Function approximation Artificial intelligence Elementary particle vacuum Artificial neural networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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