Overall, the effect of two principles can be merged to improve the efficiency of surrogate generation for GTL. As shown in Figure , the total time spent on surrogate generation for GTL can be reduced by 60% compared to equally sampling without a classifier. A separate test dataset is used to evaluate the performance of the surrogate obtained in the final iteration. We employ the boxplot for the relative errors between the surrogate predictions and the rigorous simulations for the 10 outputs. As shown in Figure , most outputs can be well predicted with relative errors smaller than 5%, and some are even smaller than 1%, e.g., the mass flowrate for the fuel products. The fitting for the utility is not ideal, and the relative error of the electricity consumption can go up to 15%. This is probably due to the insufficient feature selection for utility fitting. For example, the electricity consumption is related to the units of pumps and compressors, while no relevant features are selected into the inputs for the surrogate training. Meanwhile, no features related to heat exchangers are chosen, so the fitting performance of the utility is not as good as the mass flowrates. However, the motivation behind surrogate is to build a reduced-order model to replace the original full-order physical model, and thus sacrificing partial accuracy is unavoidable but acceptable. for GTL case studies.