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.
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