[Objective] The carbon capture pretreatment system faces challenges such as high energy consumption

unstable purification efficiency

and significant fluctuations in SO2 absorption efficiency due to variations in pH value of washing solution. This study proposes a hybrid modeling method combining mechanism models with datadriven by taking the carbon capture pretreatment system in a power plant as the research object. [Methods] By integrating chemical reaction kinetics and decision tree algorithms

the model is implemented in Python to predict key parameters accurately

including the pH value of the washing solution and the SO2 mass concentration at the system outlet. [Results] The model yields a correlation coefficient of 0.85 and 0.80 for the pH value of the washing solution and the SO2 mass concentration at the system outlet

respectively. The values fall within an acceptable error band

indicating the proposed model has good simulation performance. Moreover

a sensitivity analysis driven by baseline plant data further reveals that the model faithfully reproduces the system response to perturbations in inlet flue-gas temperature

liquid-to-gas ratio

and alkali feed rate. [Conclusion] These outcomes furnish a quantitative foundation for subsequent optimization of the CO2-capture pretreatment system

offering clear avenues for energy minimization and robust steady-state operation.