Variable selection is an important preprocessing step in the development of effective data-driven models for CO₂ flow measurement in carbon capture and storage systems. In order to effectively quantify the importance of potential input variables to the desired output, ensemble learning is proposed and incorporated into variable selection methodology. This paper presents a tree-based heterogeneous ensemble approach to variable selection and its application to gas-liquid two-phase CO₂ flow measurement. The importance of each variable is determined through combining the importance scores from four tree-based algorithms, including decision tree regression, bootstrap aggregating of regression trees, gradient boosting decision tree and gradient boosting random forest. Then the backward elimination algorithm is applied to remove the relatively less important variables and hence a small set of input variables for data-driven models. The selection results demonstrate that the significant variables for CO₂ mass flow measurement include apparent mass flow rate, time shift, differential pressure and pressure drop while observed density, density drop, observed flow velocity and outlet temperature for prediction of gas volume fraction. To assess the validity of the selected variables, data-driven models based on gradient boosting random forest are developed. Results suggest that the relative error of the model output is mostly within 1% for CO₂ mass flowrate measurement and 5% for gas volume fraction prediction by taking the selected variables as model inputs.

变量选择是开发用于碳捕获和存储系统中CO ₂流量测量的有效数据驱动模型的重要预处理步骤。为了有效地量化潜在输入变量对所需输出的重要性，提出了集成学习并将其纳入变量选择方法。本文提出了一种基于树的非均相集成方法进行变量选择及其在气液两相 CO _{2 中的应用}流量测量。每个变量的重要性是通过结合四种基于树的算法的重要性得分来确定的，包括决策树回归、回归树的引导聚合、梯度提升决策树和梯度提升随机森林。然后应用后向消除算法来去除相对不太重要的变量，从而去除数据驱动模型的一小组输入变量。选择结果表明，CO ₂质量流量测量的重要变量包括表观质量流量、时移、压差和压降，同时观测密度、密度降、观测流速和出口温度用于预测气体体积分数。为了评估所选变量的有效性，开发了基于梯度提升随机森林的数据驱动模型。结果表明，通过将选定的变量作为模型输入，模型输出的相对误差对于 CO ₂质量流量测量大多在 1% 以内，对于气体体积分数预测在 5%以内。