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A dynamic sample augmentation Kriging metamodeling method for industrial residue hydrogenation process

Residue hydrogenation process (RHP) plays an important role in efficient utilization of heavy oil resources. The high‐fidelity model of RHP is so complex that its optimization cost is expensive and even intractable. Furthermore, in the actual industrial processes, due to the low sampling frequencies of some sensors, only a few sampling points can be obtained with some missing values. The insufficient samples can directly affect the performance of the final model. Therefore, a new adaptive dynamic sampling (ADS) method for Kriging metamodeling is proposed. Based on a small number of valid industrial sample points, the proposed method adaptively obtains key information sampling points and selectively adds key information sampling points to update the model. First, the difference maximization strategy and the outlier distance strategy are proposed to obtain new sampling points with both global and regionalized information. Then, the new key points are used to iteratively update the Kriging predictor to obtain satisfactory accuracy. Finally, the calculation result of benchmark cases and two actual experiments validate the effectiveness of the proposed method for constructing surrogate models of complex industrial processes.