Affiliation:
1. College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao 266042, China
Abstract
Machine learning for online monitoring of abnormalities in fluid catalytic cracking process (FCC) operations is crucial to the efficient processing of petroleum resources. A novel identification method is proposed in this paper to solve this problem, which combines cyclic two-step clustering analysis with a convolutional neural network (CTSC-CNN). Firstly, through correlation analysis and transfer entropy analysis, key variables are effectively selected. Then, the clustering results of abnormal conditions are subdivided by a cyclic two-step clustering (CTSC) method with excellent clustering performance. A convolutional neural network (CNN) is used to effectively identify the types of abnormal operating conditions, and the identification results are stored in the sample database. With this method, the unknown abnormal operating conditions before can be identified in time. The application of the CTSC-CNN method to the absorption stabilization system in the catalytic cracking process shows that this method has a high ability to identify abnormal operating conditions. Its use plays an important role in ensuring the safety of the actual industrial production process and reducing safety risks.
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering
Reference27 articles.
1. Cai, W., Deng, R., Gao, C., Wang, Y., Ning, W., Shu, B., and Chen, Z. (2023). Evaluation techniques for shale oil lithology and mineral composition based on principal component analysis optimized clustering algorithm. Processes, 11.
2. Hidden Markov Model-Based Statistics Pattern Analysis for Multimode Process Monitoring: An Index-Switching Scheme;Ning;Ind. Eng. Chem. Res.,2014
3. A novel method for detecting processes with multi-state modes;Wang;Control Eng. Pract.,2013
4. Dynamic Multimode Process Modeling and Monitoring Using Adaptive Gaussian Mixture Models;Xie;Ind. Eng. Chem. Res.,2012
5. Multimode Process Monitoring Approach Based on Moving Window Hidden Markov Model;Wang;Ind. Eng. Chem. Res.,2018