The Russ Outstanding Research Paper Award recognizes excellence in research and scholarship exhibited by the faculty of Russ College and is awarded by recognition from a committee consisting of previous award recipients. (For more information click here.)
The team chosen for the award at the Institute for Corrosion and Multiphase Technology focused their efforts on modeling experimental data obtained from a series of experiments on corrosion inhibition of mild steel in CO₂ saturated aqueous solutions. Regression analysis was performed using different ML algorithms (Artificial Neural Network, Random Forest, Support Vector Machines, and K Nearest Neighbors) to model experimental data of time-varying corrosion rates of mild steel specimens when corrosion inhibitors were added to the system in different concentrations and dose schedules and with and without pre-corrosion. One of the main conclusions was that the sensitivity of corrosion rates to changes in the environmental variables could be well-predicted by the trained RF model, which can eliminate the need to perform extensive experiments for different solution conditions in the lab. The paper can be found here.