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Optimization of catalysts using specific, description-based genetic algorithms
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DOI：10.1021/ci700218p.png)
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This paper deals with the key optimization task that has to be solved when improving the performance of many chemical processes-optimization of the catalysts used in the reaction via the optimization of its composition and preparation. A novel approach is presented that allows for the preservation of the advantages of genetic algorithms developed specifically for the optimization of catalytic materials but avoids the disadvantageous necessity to reimplement the algorithm when the scope of the optimized materials changes. Its main idea is to automatically generate problem-tailored implementations from requirements concerning the materials with a program generator. For the specification of such requirements, a formal description language, called catalyst description language, has been developed.
Keyword:
PARAFFIN ISOMERIZATION CATALYSTS
ARTIFICIAL NEURAL-NETWORK
HIGH-THROUGHPUT
OXIDATIVE DEHYDROGENATION
DESIGN
DISCOVERY
ETHYLENE
ETHANE
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期刊
IF:
5.3
论文数:
9.1K
被引数:
4.0W
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Development of a genetic algorithm for molecular scale catalyst design.png)
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