ANNs-based surrogate is iteratively refined by sequential sampling. As Figure indicates, with more data added, the fitting performance improves (MSE decreases). Meanwhile, the decreasing rate of MSE becomes slower (Figure ) and the absolute value of relative slope tends to be smaller (Figure ). Following this, the added ratio decreases (Figure ) as well as the same trend is indicated for the sampling rate (Figure ). The algorithm is terminated when |A@=BCDE@ <=>?@ , | < 0.02, and 25 data points in total are sampled until the final iteration. To evaluate the performance of surrogate in a more direct way, we simulate the concentration profiles of three species using physical model and surrogate model, respectively. Figure shows that the regression performance of the iteratively-refined surrogate gradually improves with iteration. The surrogate obtained in the final iteration (Iteration 8) can perfectly model the original concentration profiles of the three species.