Abstract: In accordance with various embodiments described herein are systems and methods for collecting and analyzing parameter and properties data from an operation and using the data to determining Automation Safety and Operations Performance (ASOP) metrics indicative of various performance aspects of a given operation. Some embodiments are systems and methods for determining one or more ASOP metrics or metrics selected from a group consisting of elements including but not limited to Alarm System Performance Metric, an Operator Loading Metric, a Controllability Performance Metric, a Proximity to Safety System Metric, a Demand on Safety Systems Metric, and a Control System Integrity Metric.

Abstract: In accordance with various embodiments described herein are systems and methods for collecting and analyzing parameter and properties data from an operation and using the data to determining Automation Safety and Operations Performance (ASOP) metrics indicative of various performance aspects of a given operation. Some embodiments are systems and methods for determining one or more ASOP metrics or metrics selected from a group consisting of elements including but not limited to Alarm System Performance Metric, an Operator Loading Metric, a Controllability Performance Metric, a Proximity to Safety System Metric, a Demand on Safety Systems Metric, and a Control System Integrity Metric.

Abstract: The present invention is a method for detecting an abnormal event for process units of a Fluidized Catalytic Cracking Unit. The method compares the operation of the process units to a statistical and engineering models. The statistical models are developed by principle components analysis of the normal operation for these units. In addition, the engineering models are based on partial least squares analysis and correlation analysis between variables. If the difference between the operation of a process unit and the normal model result indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

Abstract: Thousands of process and equipment measurements are gathered by the modern digital process control systems that are deployed in refineries and chemical plants. Several years of these data are historized in databases for analysis and reporting. These databases can be mined for the data patterns that occur during normal operation and those patterns used to determine when the process is behaving abnormally. These normal operating patterns are represented by sets of models. These models include simple engineering equations, which express known relationships that should be true during normal operations and multivariate statistical models based on a variation of principle component analysis. Equipment and process problems can be detected by comparing the data gathered on a minute by minute basis to predictions from these models of normal operation.