Initially, MATLAB is used to run the Aspen model in the first two iterations, where nearly 20% of simulations crashed due to non-convergence issues (Figure ). The inputs/outputs in the first two iterations are used to train an SVM classifier. The obtained classifier was then applied here to screen out the potential non-converged inputs in the following iterations, thus improving the percentage of effective data (Figure ). In the final iteration, less than 9% of inputs resulted in ineffective data, with the pre-treatment of SVM. Notably, the non-converged simulations in Aspen Plus usually takes a long time to stop but deliver invalid outputs. The trained classifier lowered the possibility for the simulator to take the 'bad' inputs and tremendously reduced the average time on generating an individual data output from 61 s (1~2 iteration) to 33 s (40 th iteration). The slow-down sampling is applied to collect data iteratively. The relative slope in Figure fluctuates more significantly than the case study of PSA, see Figure . That is probably because the GTL has more outputs to fit, and the regression is more complex than the case of PSA. The observed trend in Figure indicates that the regression improvement is not significant after the 25 th iteration. Eventually, we terminate the algorithm after 40 iterations to avoid the unnecessary computation cost.