Uncertainty quantification and propagation play a crucial role in designing and operating chemical processes. This study computationally evaluates the performance of commonly used uncertainty propagation methods based on their ability to estimate the first four statistical moments of model outputs with uncertain inputs. The metric used to assess the performance is the minimum number of model evaluations required to reach a certain confidence level for the moment estimates. The methods considered include Monte-Carlo simulation, numerical integration, and expansion-based methods. The true values of the moments were calculated by high-density sampling with Monte-Carlo simulations. Ninety-five functions with different characteristics were used in the computational experiments. The results reveal that, despite their accuracy, numerical integration methods’ performance deteriorates quickly with increases in the number of uncertain inputs. The Monte-Carlo simulation methods converge to the moments’ true values with the minimum number of model evaluations if model characteristics are not considered or known.

不确定性量化和传播在设计和操作化学过程中起着至关重要的作用。本研究基于常用的不确定性传播方法估计具有不确定输入的模型输出的前四个统计矩的能力，对它们的性能进行了计算评估。用于评估性能的指标是达到某个时刻估计的置信水平所需的模型评估的最小数量。考虑的方法包括蒙特卡罗模拟、数值积分和基于扩展的方法。真实的矩值是通过蒙特卡罗模拟的高密度采样计算的。在计算实验中使用了 95 个具有不同特征的函数。结果表明，尽管数值积分方法很准确，但随着不确定输入数量的增加，其性能会迅速恶化。如果不考虑或不知道模型特征，蒙特卡罗模拟方法会以最少的模型评估次数收敛到矩的真实值。