The operational data of advanced process systems have met with explosive growth, but its fluctuations are so slight that the number of the extracted representative samples is quite limited, making it difficult to reflect the nature of the process and to establish prediction models. In this study, inspired by the process of fisherman repairing nets, a Kriging-based virtual sample generation (VSG) named Kriging-VSG is proposed to generate feasible virtual samples in data sparse regions. Then, the accuracy of prediction models is further enhanced by applying the generated virtual samples. In order to reasonably find data sparse regions, a distance-based criterion is imposed on each dimension to identify important samples with large information gaps. Similar to the process of fisherman repairing nets, a certain dimension is initially fixed at different quantiles. A dimension-wise interpolation process using Kriging is then performed on the center between important samples with large information gaps. To validate the performance of the proposed Kriging-VSG, two numerical simulations and a real-world application from a cascade reaction process for high-density polyethylene are carried out. The results indicate that the proposed Kriging-VSG outperforms other methods.

先进过程系统的操作数据爆炸性地增长，但是其波动很小，以至于提取的代表性样本数量非常有限，从而难以反映过程的性质和建立预测模型。在这项研究中，受渔民维修网的启发，提出了一种基于Kriging的虚拟样本生成（VSG），称为Kriging-VSG，用于在数据稀疏区域生成可行的虚拟样本。然后，通过应用生成的虚拟样本进一步提高了预测模型的准确性。为了合理地找到数据稀疏区域，在每个维度上都采用了基于距离的标准，以识别具有较大信息缺口的重要样本。类似于渔民修理网的过程，某个尺寸最初固定在不同的分位数上。然后在具有较大信息空白的重要样本之间的中心执行使用Kriging的按维度的插值过程。为了验证所提出的Kriging-VSG的性能，进行了两个数值模拟和级联反应过程在高密度聚乙烯中的实际应用。结果表明，提出的Kriging-VSG优于其他方法。