Due to the low CO2 concentration in the flue gas (~ 4%), one PSA unit cannot guarantee the required purity (90% for carbon capture). PSA in series can be an option. In this work, we mainly focus on performance of the first PSA unit, where recovery of CO2 is supposed to be high enough. The purity of CO2 should improve as well. A trade-off relationship is reported between recovery and purity, so the CO2 purity cannot be too high given the priority on recovery. Therefore, we trained an SVM classifier to choose the samples with high recovery (higher part, >50%) and purity (middle part, 25%-75%). The classifier's performance can be referred to in Figure , and eventually, only 23% of the initial-sampled data is selected to fall in the desired space. The slow-down sampling is applied to collect data iteratively. Figure indicates that the regression improvement is not significant after 10 iterations, and the corresponding sampling rate decreases in the meanwhile. The relative slope fluctuates significantly along with the iteration (Figure ), while the added ratio function helps smooth the fluctuation (Figure ).