Institute of Technical Thermodynamics, RWTH Aachen University , Schinkelstr. 8, 52062 Aachen, Germany.
Environ Sci Technol. 2017 Nov 21;51(22):13199-13204. doi: 10.1021/acs.est.7b01406. Epub 2017 Nov 10.
Life cycle assessment (LCA) results are inevitably subject to uncertainties. Since the complete elimination of uncertainties is impossible, LCA results should be complemented by an uncertainty analysis. However, the approaches currently used for uncertainty analysis have some shortcomings: statistical uncertainty analysis via Monte Carlo simulations are inherently uncertain due to their statistical nature and can become computationally inefficient for large systems; analytical approaches use a linear approximation to the uncertainty by a first-order Taylor series expansion and thus, they are only precise for small input uncertainties. In this article, we refine the analytical uncertainty analysis by a more precise, second-order Taylor series expansion. The presented approach considers uncertainties from process data, allocation, and characterization factors. We illustrate the refined approach for hydrogen production from methane-cracking. The production system contains a recycling loop leading to nonlinearities. By varying the strength of the loop, we analyze the precision of the first- and second-order analytical uncertainty approaches by comparing analytical variances to variances from statistical Monte Carlo simulations. For the case without loops, the second-order approach is practically exact. In all cases, the second-order Taylor series approach is more precise than the first-order approach, in particular for large uncertainties and for production systems with nonlinearities, for example, from loops. For analytical uncertainty analysis, we recommend using the second-order approach since it is more precise and still computationally cheap.
生命周期评估（LCA）的结果不可避免地存在不确定性。由于完全消除不确定性是不可能的，因此应该对 LCA 结果进行不确定性分析。然而，目前用于不确定性分析的方法存在一些缺点：通过蒙特卡罗模拟进行统计不确定性分析由于其统计性质而固有地不确定，并且对于大型系统可能变得计算效率低下；分析方法通过一阶泰勒级数展开对不确定性进行线性近似，因此仅适用于小的输入不确定性。在本文中，我们通过更精确的二阶泰勒级数展开来改进分析不确定性分析。所提出的方法考虑了来自过程数据、分配和特征化因子的不确定性。我们通过改变循环的强度，通过将分析方差与来自统计蒙特卡罗模拟的方差进行比较，来分析从甲烷裂化生产氢气的生产系统中的非线性的一阶和二阶分析不确定性方法的精度。对于没有循环的情况，二阶方法在实践中是精确的。在所有情况下，二阶泰勒级数方法都比一阶方法更精确，特别是对于大的不确定性和具有非线性的生产系统，例如来自循环的情况。对于分析不确定性分析，我们建议使用二阶方法，因为它更精确，并且仍然具有计算成本效益。
