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Evaluation of catalyst library optimization algorithms: Comparison of the Holographic Research Strategy and the Genetic Algorithm in virtual catalytic experiments

Paper ID Volume ID Publish Year Pages File Format Full-Text
44533 46038 2006 9 PDF Available
Title
Evaluation of catalyst library optimization algorithms: Comparison of the Holographic Research Strategy and the Genetic Algorithm in virtual catalytic experiments
Abstract

In this study two catalyst library optimization methods, the Holographic Research Strategy (HRS) and the Genetic Algorithm (GA) were compared based on their ability to find the optimum compositions in a given multi-dimensional experimental space. Results obtained in three different case studies were used to investigate both the rate and the certainty of the optimum search. In these case studies the activity–composition relationships were established using Artificial Neural Networks (ANNs) trained with catalytic data published earlier. The above relationships were used in “virtual optimization experiments” using both HRS and GA for catalyst library optimization. Upon using the stochastic GA its exceedingly divers mode of sampling often resulted in poor catalytic materials in the next catalyst generation. This fact resulted in a decreased rate of convergence to the optimum. In contrast, in HRS, which is a deterministic optimization algorithm, a moderate level of diversity in the catalyst library can easily be achieved. In this way an acceptable rate in optimum search can be accomplished. The visualization ability of HRS allows the illustration of all virtually tested compositions in a two-dimensional form regardless the optimization algorithm used. Upon using HRS a structured arrangement of experimental points in the virtual holograms was observed. However, when GA was applied for virtual optimization “starry sky”-like arrangement of compositions in the virtual holograms was obtained. Therefore based on virtual holograms, upon using HRS the relationship between the composition of catalytic materials and their performance can be qualitatively revealed, while no similar correlation can be obtained using GA.

Keywords
Catalyst library design; Combinatorial catalysis; Holographic Research Strategy; Genetic Algorithm; Visualization
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Evaluation of catalyst library optimization algorithms: Comparison of the Holographic Research Strategy and the Genetic Algorithm in virtual catalytic experiments
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Publisher
Database: Elsevier - ScienceDirect
Journal: Applied Catalysis A: General - Volume 303, Issue 1, 18 April 2006, Pages 72–80
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Catalysis
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us