In continuous chemical processes, variations of process variables usually travel along propagation paths in the direction of flow. The aim of this study was to find a data-driven method for identifying the direction of variation propagation using historical process data. Transfer entropy is a recently proposed method based on the probability density function (PDF) that measures directionality of variation with respect to time. An industrial case study illustrates the method which detects the influence of a temperature controller on downstream temperature measurements. A reversal of directionality was noted during a disturbance and a physical explanation offered.

In continuous chemical processes, disturbances in the process conditions can propagate widely and cause secondary upsets in remote locations. The aim of this paper is to apply some recent data-driven methods for detection and diagnosis of process disturbances using historical process data that have been proving successful in a range of applications. An industrial case study is presented in which a plant-wide control system disturbance caused by the presence of a recycle was successfully located and then verified by further plant testing.