Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature

Abstract

In this work, a database containing 1337 data points for selective CO oxidation over Cu-based catalysts was constructed from 20 research publications and used for knowledge extraction by artificial neural networks. The experimental CO conversions reported in each publication were successfully predicted by a neural network trained using the data from the remaining 19 publications unless that one publication contained unique variables. The effects and relative significances of the catalyst preparation variables (such as Cu loading, second metal additive, support type, and preparation method) and operating variables (such as reaction temperature, feed composition, and feed flow rate/catalyst weight ratio) were also determined quite successfully by the artificial neural networks. We conclude that neural network modeling can be used to extract valuable experience and knowledge accumulated in the published data and can help researchers to plan new experiments in a more effective manner.

Analysis of O2 Adsorption Stability and Strength Over Gold Clusters Using DFT and Logistic Regression

“…After repeating these four times, the G value of 93.9 was calculated by combining the validation subsets covering the entire data. This value is almost the same as that of the model (96.2) shown in Table 6, indicating the high generalization accuracy of the model [43,44].…”

Data-Mining for Processes in Chemistry, Materials, and Engineering

“…By extracting a large number of experimental literatures, they classified the catalysts with the best Faradaic efficiency, max activity, or most selective pathway. Other catalytic applications through data-mining can be found in References [53,54]. Since most of the chemical and reaction-related processes are based on temperature, pressure, component, composition, and energetic values, it is expected that the data-mining strategy shown here is general and should be applicable for addressing other similar chemical issues through machine learning.…”

Application of Artificial Neural Networks for Catalysis: A Review

“…Recently, with the developing concept of data mining, Günay and Yildirim successfully used 1337 data points from 20 studies of selective CO oxidation over Cu-based catalysts. They concluded that ANN modeling could be used to extract valuable experimental results from previous literature data and provides powerful guidance for future experimental designs [46]. In addition to catalysis, Raccuglia et al further found that a similar concept could even help assist the materials discovery from failed experimental data [47].…”