Mitigating the Threat of Corrosion Through Data-Driven Models  

1. Cleanse the Data

Before beginning any model, statistical tools and methods are used to cleanse the data. This is a basic but often overlooked step that, if skipped, can skew the results and provide an inaccurate model. Often, data is plagued with quality issues that can lead to bad decision-making. By presenting the machine with a cleaner data set, it will be able to make better, more accurate predictions. Data cleansing refers to the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted. If we don’t cleanse the data, it creates a “garbage in, garbage out” scenario.  

2. Feed Data to the Machine

Once the data is prepared, it can be fed into the machine to train the machine to understand how corrosion rates manifest in the field. This is done through supervised machine learning, where the machine is fed data examples to learn patterns. For example, the machine is given operating and design data associated with circuits, such as temperature, pressure, stream constituents, metallurgy, and observed corrosion rate. We feed the machine more data examples like this, and as the machine is exposed to the data, it starts to learn relationships between how those pieces of data correlate with the corrosion rate observed in the field.  

After we’ve fed the machine all these examples, it will learn how and, in most cases, why the corrosion rate is presenting as it is. Then, we can use this to make predictions on areas or circuits the machine has never seen before. This could be a circuit with some attributes the machine has seen before but could have different variants, such as temperatures, metallurgy configurations, or other design or operating conditions—but because of what the machine has learned, it can make reasonable predictions for this new scenario.

3. Validate with SME

Finally, once the machine has provided its estimates, they are sent back to the SME for review and validation. The SME can help identify the need for deviations or make sure that the results make sense both from a unit history and industry expectations. Any updates that are made are then fed back into the machines to help them continue to learn for future analysis. This approach marries subject matter expert knowledge with data science which provides a solution that’s better than either party could offer independently.