This section presents the workflow for surrogate generation. To enhance the surrogate quality globally, this work is focused on exploration-based sampling (exploitation-based method may affect the evaluation on the influence of the sampling rate). We select Latin Hypercube Sampling (LHS), because it does not lose generality and can deliver a well-distributed sampling result. As shown in Figure , the algorithm samples initial data by LHS. Then, simulations or experiments generate the corresponding outputs. With the initial data points (or together with a few iterations), an SVM classifier is trained to separate the feasible design space from the infeasible one. The data inputs from the infeasible region are deleted, while inputs in the feasible one are passed to the simulator for outputs. To fit multiple outputs simultaneously, ANN is selected as the surrogate type. In the following iterations, data is sampled in batch by LHS for surrogate refinement, with which the sampling rate gradually slows down. We briefly demonstrate the procedure for data generation. Process simulators -Aspen Plus, Dymola, or gPROMS are powerful tools for process modelling. Still, they are not flexible for data storage and are limited in their capacity to access high-level statistic packages. Therefore, it is necessary to establish an interface between process simulators and high-level programming languages (e.g., MATLAB, Python, etc.). Advantages of MATLAB over other programming languages are: reliable optimization and its machine-learning toolboxes, which have been wellestablished for commercial use. The automation of MATLAB to process simulators has previously been reported in process design and control . As shown in Figure , the inputs are sampled by LHS in MATLAB and passed to simulators, e.g., Dymola for dynamic simulations and AspenPlus for steady-state process simulations. The obtained outputs are sent back to MATLAB for data collection.