Sandrock, Carl2019-08-062019-08-062019-09-032018Streicher, S 2018, Plant-wide fault and disturbance screening using combined network centrality and information-theoretic causality measure analysis, MEng Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/70898>A2020http://hdl.handle.net/2263/70898Dissertation (MEng)--University of Pretoria, 2018.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.en© 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.UCTDDissertation