Chemical process fault classification based on improved marine predator optimization random forest

Ling Li; Yang Wang

doi:10.1117/12.2679634

8 May 2023 Chemical process fault classification based on improved marine predator optimization random forest

Ling Li, Yang Wang

Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 1263508 (2023) https://doi.org/10.1117/12.2679634
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China

Abstract

With the development of science and technology, chemical production technology is more and more advanced and intelligent, but the advanced technology also means the complex process, also inevitably appear complex and difficult to solve the problem, a method by Improved Marine Predators (IMPA) to optimize fault classification of chemical processes by Random Forest (RF) was proposed. First of all, Principal Component Analysis (PCA) was used to extract feature from process fault data, remove redundant data, reduce dimension, and speed up operation. Then, the improved Marine predator algorithm was used to optimize the parameters of random forest, and a fault classification model was built according to the global optimum fitness function. The fault features after PCA dimension reduction were input into the stochastic forest fault classification model optimized by the improved Marine predator algorithm to realize the fault classification of chemical processes. Tennessee-Eastman (TE) process was used to test and compare the constructed PCA-IMPA-RF fault classification model. After verification, it can be seen that the method mentioned in this paper has high accuracy of fault classification and can be effectively applied to chemical process fault classification.

Citation Download Citation

Ling Li and Yang Wang "Chemical process fault classification based on improved marine predator optimization random forest", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 1263508 (8 May 2023); https://doi.org/10.1117/12.2679634

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KEYWORDS

Chemical reactions

Oceanography

Random forests

Data modeling

Mathematical optimization

Evolutionary algorithms

Principal component analysis

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