Abstract: [Objectives]　Aiming at the operational state evaluation problem of carbon capture systems, this study proposes a comprehensive evaluation method based on subjective-objective weighting and improved grey relational analysis to improve the real-time operational level of carbon capture systems. [Methods]　Firstly, key indicators are initially screened through qualitative analysis. Based on the XGBoost-SHAP model and the Pearson correlation coefficient method, critical related factors are quantitatively identified, and a multi-dimensional evaluation index system covering carbon reduction effectiveness and economic parameters is established. Subsequently, a hybrid weight determination strategy combining the entropy weight method (objective weighting) and the analytic hierarchy process (subjective weighting) is adopted. The traditional grey relational model is optimized by introducing a dynamic resolution coefficient, enabling quantitative comparison of the performance under different operational conditions of the carbon capture system. Finally, a case study is conducted on a carbon capture system with an annual capture capacity of 150,000 tons. [Results]　The system performance shows significant differences with changing operational conditions. The grey relational degrees are ranked in descending order as follows: low-temperature and low-concentration (0.288), low-temperature and high-concentration (0.281), high-temperature and high-concentration (0.248), high-temperature and low-concentration (0.183). Among these, the overall performance of low-temperature conditions is superior to that of high-temperature conditions. [Conclusions]　The proposed method can effectively reflect the operational state of the carbon capture system under different operational conditions and provides a decision-making basis for subsequent evaluation and analysis.

Key words: dual carbon, carbon neutrality, carbon capture, utilization and storage (CCUS), carbon emission, carbon reduction, entropy weight method, analytic hierarchy process, evaluation index