Prediction of SiC Oxidation Reaction Behavior Based on Back Propagation Artificial Neural Network

Abstract

Abstract: Non-oxide refractory raw materials represented by SiC are widely used in the metallurgical high-temperature industry as important components of high-temperature structural materials. In practice, the oxidation behavior of SiC accelerates the failure of corresponding refractories at high temperatures, resulting in a significant reduction of their service life. Therefore, it is important to understand the oxidation behavior of non-oxide refractory raw materials at high temperature. Kinetic models are the most common means to analyze the oxidation behavior. However, the establishment of kinetic models often requires a lot of data processing work, and it is difficult to meet the two conditions of high descriptive accuracy and simple model parameters at the same time. With the exploration of artificial intelligence and big data technology in the field of materials, back propagation artificial neural network (BP-ANN) is expected to make a breakthrough in this area. In this paper, a typical non-oxide refractory raw material SiC was taken as an example, and the oxidation behavior of SiC was trained and predicted by building a neural network. The relative errors of the predicted results and experimental data are less than 3%, and the relative errors of the reaction activation energy and reaction rate constant calculated by regression with the predicted data are less than 4% with the results calculated by the experimental data. This shows that BP-ANN has great potential in studying the oxidation behavior of non-oxide refractory raw materials.

Key words: refractory, high-temperature structural material, non-oxide material, oxidation, SiC, back propagation artificial neural network, model optimization, reaction activation energy

CLC Number:

TQ175

ZHAO Chunyang, WANG Enhui, FANG Zhi, GUO Chunyu, DUAN Xingjun, HOU Xinmei. Prediction of SiC Oxidation Reaction Behavior Based on Back Propagation Artificial Neural Network[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2021, 40(10): 3213-3218.

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