Abstract
The stable quality of circulating water ensures the long-term stable operation of various processes in petrochemical production and achieves energy savings and emission reduction while reducing environmental pollution and yielding economic benefits to petrochemical enterprises. However, traditional circulating water quality evaluation and modeling for corrosion rate prediction suffer from adaptability and accuracy problems. To address these problems, the water quality analysis data of the circulating water in the field were subjected to data preprocessing and water quality index calculation to perform feature engineering, followed by modeling using a machine learning method that integrates the adaptive immune genetic algorithm and random forest (RF) algorithm and can intelligently select the water quality parameters to be used as the input variables for the RF modeling. Finally, the method was validated using an industrial example, and the results indicate that the method is capable of removing interference variables and is suitable for carbon steel corrosion rate prediction based on water quality models. The proposed method provides a basis for water quality management and real-time decision-making by circulating water field personnel.