The
addition of sorbent to capture CO2 in steam methane
reforming has become a method to reduce CO2 emissions in
conventional hydrogen production. The aim of this paper is to propose
a fast method for predicting the capture performance of sorbents by
applying the eXtreme Gradient Boosting (XGBoost) method. First, the
effects of inlet temperature, velocity, CO2 mass fraction,
and initial material inventory height on the sorbent CaO capture efficiency
and resulting flow regime are systematically analyzed, while an extensive
database is constructed. Second, a comparative assessment is conducted
to determine the relative significance of the four parameters in influencing
the capture efficiency. Finally, the XGBoost model is trained and
deployed to enhance the computational accuracy of capture efficiency
predictions. The results emphasize that temperature has the most significant
effect on capture efficiency. Increasing the temperature and decreasing
the gas velocity helped to increase the capture efficiency. The initial
material inventory height of 0.3 m proved to be favorable for CO2 capture compared to 0.1 and 0.2 m, especially under gas velocity
conditions of 0.3 m/s. In addition, the XGBoost model can be used
to quickly predict the CO2 capture efficiency of fluidized
beds by using less training data and obtaining the prediction result
with a high accuracy of R2 = 0.9699 in 2 s. This model
has potential applications for the prediction of the sorbent capture
efficiency.