Performance-based ranking of porous materials for PSA carbon capture under the uncertainty of experimental data
Performance-based ranking of porous materials for PSA carbon capture under the uncertainty of experimental data
复制标题
实验数据不确定性下用于PSA碳捕获的多孔材料的基于性能的排序
DOI：
10.1016/j.cej.2022.135395
复制
发表时间：
2022
影响因子：
15.1
通讯作者：
Cleeton C
中科院分区：
文献类型：
--
作者：
Cleeton C
在这项研究中，我们解决的问题是如何在平衡吸附数据和吸附剂的其他属性的不确定性影响其排名在筛选研究中使用变压吸附（PSA）的碳捕获。PSA模拟模型的构建通常基于单组实验数据，这导致单个能量惩罚-生产率帕累托前沿，用作给定材料的性能指标。实际上，不同小组对同一材料进行的实验测量显示出显著的散射程度。这种散射如何传播到过程级尚不清楚。为了回答在这项研究中提出的问题，我们考虑的情况下，沸石13 X，一个众所周知的碳捕获研究的基准。使用策划的实验数据从文献中，我们开发了一个概率等温线模型，使用分层贝叶斯推理。然后，我们结合联合收割机详细的工艺优化和代理模型，探讨材料层面的不确定性对帕累托前沿的行为的影响。我们观察到，沸石13 X的性能更准确地表示由可能的帕累托前沿的云，而不是由一个单一的帕累托前沿，这种云的特性主要是由吸附数据的不确定性。鉴于观察到的工艺级性能的变化性，应谨慎对待材料排名，因为两种不同材料的云很可能会重叠。
The question we address in this study is how the uncertainty in equilibrium adsorption data and other properties of an adsorbent affects its ranking in screening studies for carbon capture using pressure swing adsorption (PSA). Construction of a model for a PSA simulation is normally based on a single set of experimental data, which leads to a single energy penalty-productivity Pareto front, used as the performance indicator for a given material. In reality, experimental measurements performed on the same material by different groups show a significant degree of scattering. How this scattering propagates to the process level is not known. To answer the question posed in this study, we consider the case of zeolite 13X, a well-known benchmark for carbon capture studies. Using curated experimental data from the literature, we develop a probabilistic isotherm model using hierarchical Bayesian inference. We then combine detailed process optimisation and surrogate models to explore the impact of material-level uncertainty on the behaviour of Pareto fronts. We observe that the performance of zeolite 13X is more accurately represented by a cloud of possible Pareto fronts rather than by a single Pareto front, and that characteristics of this cloud are mostly determined by the uncertainty in the adsorption data. Given the observed variability in process-level performance, materials ranking should be approached with caution as it is very likely that clouds of two distinct materials can overlap.