(27a) Comparison of Statistical and Machine Learning Approaches for Fault Detection in a Continuous Catalytic Regeneration Unit
AIChE Spring Meeting and Global Congress on Process Safety
2021
2021 AIChE Virtual Spring Meeting and 17th Global Congress on Process Safety
Computing and Systems Technology Division
Computers in Operations: Process Safety and Control
Tuesday, April 20, 2021 - 11:00am to 11:20am
Data-driven fault detection and diagnosis (FDD) methods, which can be divided into two categories, statistical and machine learning (ML) approaches, have become popular in both research and applications. In this study, we will present our key findings of process monitoring using both statistical and ML techniques. For the statistical approach, the commonly used method based on principal component analysis (PCA) will be employed and will be compared with dynamic PCA extension in which the serial dependence in measured variables is considered. For further comparison, on the same data, autoencoders, which is a popular unsupervised ML technique, will be employed for fault detection on real industrial operation data from the CCR process. This comparative study is expected to result in an effective fault detection approach for an operating plant that will also substantially contribute to process sustainability and reliability.