Research on equipment corrosion diagnosis method and prediction model driven by data

Abstract

With the rapid development of the oil refining industry, safety problems caused by equipment corrosion have become increasingly important, making equipment corrosion management a key factor to ensure process safety. Corrosion diagnosis, as the first step of equipment corrosion management, is of great significance in not only ensuring the proper corrosion supervision, but also realizing safety protection of equipment. This paper addresses the problems of incompleteness as well as the subjective factors of existing methods in equipment corrosion diagnosis. The proposed solution, based on data-driven corrosion diagnosis, suggests a more comprehensive view. Special focus in this paper is on evaluation and prediction of corrosion safety state, including the identification of corrosion mode and the prediction of corrosion type and degree. This paper brings together large amount of historical data of equipment corrosion detection and solves the problem of unbalanced original data by data wrangling and the application of Borderline-SMOTE algorithm. What’s more, a prediction model that is based on Random Forest (RF) algorithm is constructed, aiming at equipment corrosion mechanism, type and degree. The results show that the model, aiming at critical mechanism identification, performs ideally after evaluation and the accuracy of the results amount to 86%. As for the classification and prediction of corrosion state, the model can be further optimized by Particle Swarm Optimization (PSO) algorithm to reach a better accuracy (92%), which verifies generalization effect compared with traditional prediction models. In addition, this solution improves the functionality and practicability of corrosion diagnosis, which is beneficial to the investigation of hidden dangers. It also can serve as an instruction for equipment safety management to ensure the stable operation for an enterprise.