Real-time mechanistic analysis of electrochemical noise data is essential for rapid identification of localized corrosion by plant operators. Recent developments towards this goal include: Intelligent Noise Data Reduction techniques to eliminate uninformative data; neural nets which learn how to categorize corrosion mechanisms from data patterns; multivariate analysis which allows the identification of combinations of plant process parameters that cause damage. These techniques can be combined to facilitate pro-active management of the corrosion problem, including consideration of corrosion mechanisms within the plant optimization process.

Subject
Costs, Correlation, Modeling, Corrosion rate, Optimization, Electrochemical noise, Entropy, Corrosion modeling, Electrochemical data, Corrosion data, Corrosion monitoring, Corrosion mechanisms, Risk reduction
© 1999 Association for Materials Protection and Performance (AMPP). All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise) without the prior written permission of AMPP. Positions and opinions advanced in this work are those of the author(s) and not necessarily those of AMPP. Responsibility for the content of the work lies solely with the author(s).
1999
Association for Materials Protection and Performance (AMPP)
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