Materials Performance

OCT 2018

Materials Performance is the world's most widely circulated magazine dedicated to corrosion prevention and control. MP provides information about the latest corrosion control technologies and practical applications for every industry and environment.

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OCTOBER 2018 WWW.MATERIALSPERFORMANCE.COM 44 that condition could substantially lower the corrosion rate by slowing corro- sion kinetics. In the first stage there was the "concept." The next stage was adaption of several highly useful quantitative criteria or specific "metrics" that concisely define successful control. Examples include the 100 mV instant off potential, and the protection potential criterion of –850 mV vs. copper/copper sulfate (Cu/CuSO 4 ) electrode as well as other semi-quantitative "rules of thumb." These metrics have become standardized and widely accepted. However, this stage was just one level of sophistication on the rung of progress. That is because such a criterion could not be achieved spatially across an entire cathodically protected structure at all locations—verification of the ICCP threshold level was only achieved at first at a few spots on the structure. In stage three, enough was known about the theory of ICCP and the metrics for success that this knowledge could be combined with growing computational capabilities to map the potential distribution across the entire structure. Computer generated finite element potential and current distributions are now fairly routine and can examine the spatial dependency of ICCP. Distrib- uted remote sensors (i.e., reference electrodes) can monitor the potential at many locations. The potential distribution on a pipeline can now be explored in detail to assess ICCP levels. A similar "set of stages" can be used to describe other corrosion control strategies such as coatings, safe limits for high-strength materials given the threat of hydrogen embrittlement in harsh environments, and choice of a corrosion-resistant alloy. Concepts lead to simple metrics. Simple metrics are now being replaced with more sophisticated models and tools. Stages of maturity define the current progress in most if not all corrosion control strategies. What is next? There are many promising new corrosion control strategies yet to emerge. One benefits from the coming age of cyber physical systems. Here corrosion control will likely follow along the lines of the "smart cities internet of things" concept where many varieties of distributed sensors will in real time interrogate the corrosion "state of health" of a structure or sys- tem and algorithms or digital tools will make decisions either automatically or with owner inputs. In the future, many of the sensors needed will be pow- ered by the nearby environment, harvesting energy from their surroundings without connection to the grid. The cyber physical world is here to stay and will likely expand into corrosion. These strategies will create enormous amounts of data. It is said that 90% of the world's data has been generated just in the last two years. This data collected is rich in information but too large to manage. Materials informatics, data sciences, and machine learning are but a few strategies that rely on such increasingly large amounts of data such as those generated from all those sensors. Data sciences approaches will be necessary to understand how to handle and interpret all that infor- mation but could establish relationships and trends impossible to see other- wise that could aid corrosion control. Such relationships might not be detected using conventional approaches. Data sciences approaches may also reveal relationships between environmental or material factors and corrosion that might not be discovered by conventional means. All these possibilities and more point toward a bright and exciting future in the corrosion control industry. NEIL G . T HOMP S ON In the 2017 Frank Newman Speller Award lecture, Narasi Sridhar described knowledge-based predictive analytics. I believe that knowledge-based ana- lytics will make the largest difference in how we approach corrosion man- agement. I am not referring to simple data trending, or data-centric correlative analysis (as Dr. Sridhar describes it), but combining correlative analysis with predictive modeling. These models can be empirical/semi- empirical models based on available data, expert-knowledge base models,

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