This paper proposes a new incremental sampling method for the generation of surrogate models based on the application of partial least squares regression (PLSR) as a termination criterion. Compared to existing incremental and adaptive methods, the proposed method allows the sampling algorithm to stop without needing to fit a surrogate model at each iteration step. The proposed procedure was applied to a motivating pipe model and two case studies; the reaction and the separation section of an ammonia synthesis loop. In all cases, the new sampling method allows a small number of sampling points, corresponding to a regular grid with less than two points in each independent variable. The two surrogate models of the ammonia loop are combined for overall optimization. The optimum for the combined surrogate models is close to the optimum obtained with the original model.

本文基于偏最小二乘回归（PLSR）作为终止准则，提出了一种新的替代模型生成增量采样方法。与现有的增量和自适应方法相比，该方法允许停止采样算法，而无需在每个迭代步骤都拟合替代模型。拟议的程序已应用于激励管道模型和两个案例研究。氨合成回路的反应和分离部分。在所有情况下，新的采样方法都允许少量采样点，对应于每个独立变量中少于两个点的规则网格。氨回路的两个替代模型组合在一起进行整体优化。