Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Computers and Chemical Engineering 31 (2006) 4150 New approaches for representing, analyzing and visualizing
 

Summary: Computers and Chemical Engineering 31 (2006) 4150
New approaches for representing, analyzing and visualizing
complex kinetic transformations
Ioannis P. Androulakis
Biomedical Engineering Department, and Chemical & Biochemical Engineering Department, Rutgers,
The State University of New Jersey, Piscataway, NJ 08854, USA
Received 12 December 2005; received in revised form 16 May 2006; accepted 16 May 2006
Available online 22 August 2006
Abstract
Complex kinetic mechanisms involving thousands of reacting species and tens of thousands of reactions are currently required for the rational
analysis of modern combustion systems. In order to represent, analyze and visualize effectively the ignition process, advanced computational
techniques will be required. Recently, we introduced a novel approach that captured the principal elemental transformations in complex reaction
mechanisms in the form of graphs. In this work, we propose new approaches in order to arrive at a compact representation of the information
content of these graphs utilizing machine learning principles, such as feature selection in time series and hashing. These approaches allow the
projection of the totality of the information contained in the graphs describing the chemical transformations onto a single scalar. The temporally
evolving graphs are treated as streaming data and locality-preserving hashing allows the unique assignment of a scalar "motif" value to each such
graph. Analysis of those motifs allows the quick identification of "clusters" of identical reaction graphs that correspond to regimes with similar
kinetic characteristics. The approach is illustrated with highly complex kinetic mechanisms describing pentane autoignition. It is demonstrated
how this novel representation allows the identification of regions where similar temporal history of the chemical transformations is experienced.
2006 Elsevier Ltd. All rights reserved.

  

Source: Androulakis, Ioannis (Yannis) - Biomedical Engineering Department & Department of Chemical and Biochemical Engineering, Rutgers University

 

Collections: Engineering; Biology and Medicine