Abstract-In continuous chemical processes, variations of process variables usually travel along propagation paths in the direction of the control path and process flow. This paper describes a data-driven method for identifying the direction of propagation of disturbances using historical process data. The novel concept is the application of transfer entropy, a method based on the conditional probability density functions that measures directionality of variation. It is sensitive to directionality even in the absence of an observable time delay. Its performance is studied in detail and default settings for the parameters in the algorithm are derived so that it can be applied in a large scale setting. Two industrial case studies demonstrate the method.

In continuous chemical processes, disturbances travel along propagation paths in the direction of the control path or process flow. This article applies a method based on the nearest neighbors of embedded vectors to historical process data for the purpose of identifying the direction of propagation of disturbances. The resulting measure is sensitive to directionality even in the absence of an observable time delay. Its performance is studied in two industrial case studies and default settings for the parameters in the algorithm are derived so that it can be applied in a large scale setting.

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