Modeling preferential CO oxidation over promoted Au/Al2O3 catalysts using decision trees and modular neural networks

In this work, the experimental data for CO oxidation over promoted Au/Al2O3 catalysts were analyzed using decision trees and modular neural networks. The full dataset was first classified by decision trees to identify and select the conditions for high catalytic activity; then the reduced dataset containing only the promising data were modeled using neural networks, at which the compositional and operating variables were processed in a modular manner to be able to model their effects together but treat them separately. It was found that operating variables were more influential on catalytic activity than catalyst compositional variables. The temperature was found to be the most significant operating variable while Mg and Mn were the best performing promoters. It was also concluded that decision trees and neural networks can complement each other to extract easily comprehensible knowledge, and they can be used for similar catalytic systems for the same purpose.

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► Data was classified by decision trees to identify the conditions for high activity.
► Reduced dataset containing only the promising data were modeled by neural networks.
► Temperature was found to be the most significant operational variable.
► Mg and Mn were the best performing promoters.
► Decision trees with neural networks help to analyze catalysts in a systematic manner.