Early detection of significant abnormal changes is highly desirable for oil refinery processes, which consist of sophisticated unit operations handling hazardous and flammable inventories and operating at high temperature and pressure. Close monitoring and anomaly detection are vital for avoiding major accidents and losses and enable intervention before failure occurrence. A new big data analytics tool called Plant Health Index (PHI) is proposed in this work. PHI is a statistical anomaly detection software that trains its model using online normal plant operation, then uses statistical analytics to detect anomalies. For detecting the anomalies, a combined method of multivariate analysis of residuals and nonparametric models of the process is employed. The methodology provides a structured representation of the plant variables to ease the detection of problems along with the detection of operation changes of the system. The PHI system has been tested on a hydrotreating units in a refinery, which consists of catalytic reactors and separators. The current implementation tagged 170 process variables and proved effective in capturing the normal operational conditions of the plant. When placed online, PHI was able of detecting anomalies that are difficult to detect using the control system and before being detected by the alarm system.
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