Abstract:
Finding the source of a disturbance in complex systems such as industrial chemical processing plants can be a difficult task and require a significant amount of engineering hours. In many cases, a systematic elimination procedure is considered to be the only feasible approach but can cause significant process upsets. Practitioners desire robust alternative approaches.
This study evaluates methods for ranking process elements according to the magnitude of their influence in a complex system. The use of data-driven causality estimation techniques to infer an information transfer network among process elements is studied. Graph centrality measures are then applied to rank the process elements according to their overall effect.
A software implementation of the proposed methods forms part of this work.
