In order to solve air pollution problems including particulate matter and global warming, the solvent development and process design which mitigate causative factors such as CO2 and SOx are essential.  This project focused on the optimization of CO2 capture pilot plant with new water lean amine solvent, K2Sol, and the parameter estimation through pilot-scale experiments data. A Gaussian process Bayesian optimization algorithm was employed for finding the optimal operating conditions. Furthermore, CEPL developed a Bayesian parameter estimation method which infers the parameter posterior inference under pilot-scale experimental data sets.