Adaptive Data-Driven Modeling Strategy Based on Feature Selection for an Industrial Natural Gas Sweetening Process

As the core process of natural gas purification plant, natural gas sweetening directly affects the production efficiency and product quality of the purification plant. However, process modeling based on sulfur content prediction presents challenges in adaptability and accuracy. To tackle this, a machine learning-based modeling approach is proposed that integrates an adaptive immune genetic algorithm with random forest (RF) to intelligently select process features as input variables for RF modeling. The industrial result indicates that the proposed method is able to remove interfering variables and adaptively achieve optimal model precision for different scenarios. It offers a novel research instrument for product quality monitoring in natural gas sweetening plants. A machine learning-based modeling approach is proposed that integrates an adaptive immune genetic algorithm with random forest to intelligently select process features as input variables for natural gas sweetening process modeling. This model can adaptively execute physical feature selection and possesses the advantages of strong predictive performance and tolerance to outliers and noise. image