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.

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Disturbances that spread plant-wide in a chemical process pose challenges to maintenance staff. Connections within the plant and the presence of multiple causal paths mean it is not straightforward to locate the root disturbance because the effects can propagate and be detected elsewhere. Measurementbased methods use quantitative process history to generate hypotheses about the root cause, while a separate strand of work in the literature has used causal maps and digraphs. It has been reported that both approaches can give spurious solutions, however. The idea behind this article is to reduce the number of spurious solutions by combining basic and readily-available information about the connectivity of the process with the results from causal measurement-based analysis. Connectivity information is captured from an XML description of the process schematic that complies with the CAEX schema. The capabilities of the approach and its potential for future development are discussed.

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