The increased energy demand within South Africa has led to continued periods of load shedding. This has had an adverse impact on industry, quality of life and the economy as a whole. A larger requirement for production time, reduced downtime and an enlarged focus on health and safety have steered industry towards a paradigm shift in inspection and maintenance. These activities have progressed from a predominantly time-based (prescriptive) approach towards a risk-based approach.
Generally accepted standards like BS EN 16991:2018 and API RP 580 give a comprehensive outline of the basic elements for developing, implementing and maintaining a risk-based inspection program. API RP 581 takes this outline one step further and contains the quantitative methods that support the minimum guidelines presented by API RP 580. Similarly, Kent W. Mühlbauer’s approach has developed a relative risk ranking model for petroleum and gas pipelines, which outlines a qualitative method for representing risk. None of these models are however directly applicable to predicting the failure of pressurised boiler equipment due to the mechanism of corrosion fatigue.
API RP 580 / 581 was primarily developed for the oil and gas industry and have practical limitations when applied to pressurised equipment typically found in utilities. BS EN 16991:2018 supplies a framework for utilities, but doesn’t go into the specific detail of how to structure, formulate and apply a risk based management model. The methodology laid out by Kent W. Mühlbauer, while practical and easily implemented, was designed for oil and gas pipelines.
A systematic methodology to evaluate the risk associated with specific failure mechanisms in boilers, such as corrosion fatigue, does not exist or is not readily available. A comprehensive risk-based predictive model, using aspects of the abovementioned standards and guides, was developed to demonstrate the predictability of corrosion fatigue in sub-critical boilers. Weightings were assigned to contributory causes to corrosion fatigue, which then allocated relative risk ranks to certain segments within a boiler. Operators and owners of boilers can derive benefit from this model by focusing inspection, maintenance and alteration activities on those equipment locations with the highest relative risk score. / Dissertation (MEng (Metallurgical Engineering))--University of Pretoria 2020. / Eskom Power Plant Engineering Institute (EPPEI)
Supervisor: Mr. L. Reddy / Materials Science and Metallurgical Engineering / MEng (Metallurgical Engineering) / Unrestricted
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/78349 |
| Date | January 2020 |
| Creators | Rode, Bianca |
| Contributors | Mostert, R.J. (Roelf), u27198457@tuks.co.za, Wannenburg, Johann |
| Publisher | University of Pretoria |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Rights | © 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. |
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