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http://dx.doi.org/10.9713/kcer.2020.58.2.224

Fault Detection in LDPE Process using Machine Learning Techniques  

Lee, Changsong (LG Chem Research Park)
Lee, Kyu-Hwang (LG Chem Research Park)
Lee, Hokyung (LG Chem Research Park)
Publication Information
Korean Chemical Engineering Research / v.58, no.2, 2020 , pp. 224-229 More about this Journal
Abstract
We propose a machine learning-based method for proactively detecting faults in LDPE processes and predicting equipment lifespan. It is important to detect and prevent unexpected faults in chemical processes in order to maximize safety and productivity. Since LDPE process is a high-pressure process up to 3,000 kg/㎠g or more, once ESD occurs, it can result in productivity loss due to increased maintenance periods. By collecting key variables operation data of the process and using unsupervised machine leaning methods, we developed a fault detection model which detected 4 ESDs 2.4 days prior to the occurrence. In addition, it was confirmed that the life expectancy of a hyper compressor can be predicted by using the physically significant key variables.
Keywords
Machine learning; Unsupervised learning; Principal component analysis; Hotelling's $T^2$; LDPE; Fault detection; Emergency shutdown (ESD);
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