ABSTRACT
This chapter addresses the imperative need for a systematic and comprehensive process to analyze and integrate multiple lines of evidence (MLOE) for determining the causes of corrosion damage, with a particular focus on microbiologically influenced corrosion (MIC).
Acknowledging the consensus within industry and education on the importance of multifaceted diagnostic approaches, this work proposes a structured methodology guided by the latest research findings, consensus standards, and advanced analytical techniques. The correct diagnosis of corrosion/MIC causes is deemed essential, not only for effective mitigation and prevention but also as a pivotal step toward advancing materials sustainability.
The primary categories of evidence employed in diagnosing corrosion causes are examined, including (1) operation and design, (2) the chemical environment, (3) the microbiological environment, and (4) materials and corrosion products. The interplay between data in these categories is explored, emphasizing the interconnectedness of factors contributing to corrosion. For instance, the pH of an aqueous phase is demonstrated to impact corrosion rate, physical corrosion form, types of microorganisms present, and various aspects of water chemistry such as solubility, alkalinity, and scaling tendency.
A thorough examination of relationships and interdependencies between different data types is conducted to inform an integrated approach. The outcome of this process yields a list of objectives, science-based observations supporting one or more specific abiotic and biotic corrosion mechanisms.
The chapter emphasizes the application of standards, models, and guidelines to support the integration and analysis process. Furthermore, considerations for the reliability of supporting data are incorporated to gauge the level of confidence in the final diagnostic result.
To illustrate the practical application of this analysis and integration process, a case study is presented. This case highlights a real-life situation where not all data were readily available or reliable, illustrating the adaptability and robustness of the proposed methodology. The integration of data from diverse sources enables the formulation of informed conclusions regarding the most likely causes of corrosion.
In conclusion, this chapter advocates for a holistic and evidence-based approach to diagnosing corrosion causes, offering a structured methodology that draws upon the latest research and analytical techniques. The proposed process provides a valuable framework for industry and education alike, guiding practitioners in selecting effective mitigative and preventive measures while contributing to the advancement of materials sustainability.
