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Comparison of Machine Learning Algorithms in Screening Potential Additives to Ni/Al 2 O 3 Methanation Catalysts for Improving the Anti‐Coking Performance
Author(s) -
Han Xiaoxia,
Yue Lin,
Zhao Chaofan,
Jiang Shaohua,
Liu Junjie,
Li Yuting,
Ren Jun
Publication year - 2019
Publication title -
chemistryselect
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 34
ISSN - 2365-6549
DOI - 10.1002/slct.201902627
Subject(s) - principal component analysis , cluster analysis , support vector machine , methanation , radial basis function , artificial intelligence , algorithm , kriging , gaussian , computer science , kernel (algebra) , gaussian process , machine learning , gaussian function , catalysis , pattern recognition (psychology) , artificial neural network , mathematics , chemistry , biochemistry , computational chemistry , combinatorics
In this paper, the 16 physicochemical properties of 56 elements were processed through principal component analysis (PCA) transformation and Gaussian mixture model clustering. And then, a pool of eleven representative elements was chosen for subsequent experiments on resistance to carbon deposition. Based on the experimental results and the principal components of the selected elements, radial basis function network (RBFN), support vector machine (SVM) and Gaussian process regression (GPR) models were constructed, respectively. Compared with other models, the prediction results of GPR model are more accurate. It predicted that W element is the most effective additive, which was confirmed by further experiments. To our knowledge, this finding has not yet been formally reported in the literature. The application and implementation of this strategy provides new ideas for high dimensional, small sample and nonlinear catalyst data modeling.