Abstract: Provided herein are methods for dynamic bias management between online process analyzers and laboratory certification tests. In many refinery processes, online analyzers are used to determine any number of properties to ensure the product being produced meets a given target or specification. Refinery laboratory tests are typically used for certification, and therefore, biases between said certification tests and online process analyzers need to be managed to control/optimize the manufacturing process. The methods employ an exponential-weighted moving average (EWMA) dynamic correction factor based on historical process analyzer data versus laboratory certification samples in conjunction with structural bias correct functions to achieve this bias management.

Abstract: The present invention is a method for detecting an abnormal event for process units of a Delayed Coking Unit. The method compares the operation of the process units to statistical and engineering models. The statistical models are developed by principal components analysis of the normal operation for these units. The engineering models are based statistical 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.