Performance-based screening of porous materials for CO₂ capture and gas separation requires development of multiscale simulation workflows where physiochemical characteristics of adsorbents are obtained from molecular simulations, while separation performance of materials is evaluated at the process level by comparing overall energy efficiency and productivity in a particular process configuration. Practical implementation of these workflows requires: (a) accurate calculation of various material properties some of which are poorly estimated so far (e.g. specific heat capacity), (b) consistent treatment of the process variables that cannot be calculated from molecular simulations but are crucial for process modelling (e.g. pellet size and porosity), (c) improving computational efficiency of the workflows by reducing the search space in process optimization. In this study, we focus on four representative materials in the context of the vacuum swing adsorption process for carbon capture to probe these issues. We report on several observations with important implications for the theoretically achievable process efficiency, the computational efficiency of the multiscale workflows and on the consistency of materials rankings. We demonstrate that if size and porosity of adsorbent pellets are optimized, efficiency and productivity of the process can be substantially improved. We show the maximum performance of a material achievable in a particular process depends on a complex combination of both intrinsic material properties and process variables. This is evident from the ranking of the materials being different for a process with optimizable pellet size and porosity, compared to the reference case where these two properties are fixed. Analysis of the cycles on the Pareto fronts reveals common patterns for these variables for all the materials under consideration. We demonstrate that this observation reflects some optimum balance in the competition between diffusive processes into the pellet and convection flow processes across the bed. We attempt to capture this balance in a universal dimensionless metric which is explicitly proposed here for the first time. Application of such universal metrics could be very important in improving the efficiency of the optimization algorithms by narrowing down the multidimensional search space.

中文翻译：

---

在燃烧后碳捕集的过程驱动材料筛选中探索新的效率来源

基于性能的多孔材料用于CO ₂捕集和气体分离的筛选需要开发多尺度模拟工作流，其中从分子模拟获得吸附剂的理化特性，同时通过比较整体能源效率和生产效率在过程级别评估材料的分离性能。特定的过程配置。这些工作流程的实际实施要求：（a）准确计算各种材料性能，到目前为止，其中某些材料的估算尚不充分（例如，比热容），（b）一致处理无法从分子模拟中计算出但至关重要的过程变量用于过程建模（例如颗粒尺寸和孔隙率），（c）通过减少流程优化中的搜索空间来提高工作流程的计算效率。在这项研究中，我们将重点介绍四种具有代表性的材料，这些材料在真空摆动吸附过程中进行碳捕获，以探讨这些问题。我们报告了对理论上可实现的过程效率，多尺度工作流的计算效率以及材料排名的一致性具有重要意义的几种观察结果。我们证明，如果吸附粒料的尺寸和孔隙率得到优化，则可以大大提高该方法的效率和生产率。我们显示出在特定过程中可获得的材料的最大性能取决于固有材料属性和过程变量的复杂组合。从具有可优化的颗粒尺寸和孔隙率的工艺的材料排名来看，这与固定了这两个属性的参考案例相比是明显的。在帕累托前沿的循环分析揭示了所考虑的所有材料的这些变量的通用模式。我们证明，该观察结果反映了扩散过程进入颗粒过程和穿过床的对流过程之间的竞争中的某些最佳平衡。我们试图以一种通用的无量纲度量来捕获这种平衡，这在这里是首次明确提出。通过缩小多维搜索空间，此类通用度量的应用对于提高优化算法的效率可能非常重要。