Real-Time Risk Monitoring System for chemical plants

This study was performed to develop a Real-Time Risk Monitoring System which helps to do fault detection using the information from plant information systems in a chemical process. In this study, to do fault detection, principal component analysis (PCA) methods of multivariate statistical analysis were used. The fundamental notions are a set of variable combinations, that is, detection of principal components which indicate the tendency of variables and operating data. Besides classical statistic process control, PCA can reduce the dimension of variables with monitoring process. Therefore, they are known as suitable methods to treat enormous data composed of many dimensions. The developed Real-Time Risk Monitoring System can analyze and manage the plant information on-line, diagnose causes of abnormality and so prevent major accidents. It's useful for operators to treat numerous process faults efficiently.