Rajendran Arvind, Subraveti Sai Gokul, Pai Kasturi Nagesh, Prasad Vinay, Li Zukui
Donadeo Innovation Centre for Engineering, University of Alberta, 9211-116 Street NW, Edmonton, AB T6G 1H9, Canada.
SINTEF Energy Research, Trondheim 7019, Norway.
Acc Chem Res. 2023 Sep 5;56(17):2354-2365. doi: 10.1021/acs.accounts.3c00335. Epub 2023 Aug 22.
ConspectusAdsorption using solid sorbents is emerging as a serious contender to amine-based liquid absorption for postcombustion CO capture. In the last 20+ years, significant efforts have been invested in developing adsorption processes for CO capture. In particular, significant efforts have been invested in developing new adsorbents for this application. These efforts have led to the generation of hundreds of thousands of (hypothetical and real) adsorbents, e.g., zeolites and metal-organic frameworks (MOFs). Identifying the right adsorbent for CO capture remains a challenging task. Most studies are focused on identifying adsorbents based on certain adsorption metrics. Recent studies have demonstrated that the performance of an adsorbent is intimately linked to the process in which it is deployed. Any meaningful screening should thus consider the complexity of the process. However, simulation and optimization of adsorption processes are computationally intensive, as they constitute the simultaneous propagation of heat and mass transfer fronts; the process is cyclic, and there are no straightforward design tools, thereby making large-scale process-informed screening of sorbents prohibitive.This Account discusses four papers that develop computational methods to incorporate process-based evaluation for both bottom-up (chemistry to engineering) screening problems and top-down (engineering to chemistry) inverse problems. We discuss the development of the machine-assisted adsorption process learning and emulation (MAPLE) framework, a surrogate model based on deep artificial neural networks (ANNs) that can predict process-level performance by considering both process and material inputs. The framework, which has been experimentally validated, allows for reliable, process-informed screening of large adsorbent databases. We then discuss how process engineering tools can be used beyond adsorbent screening, i.e., to estimate the practically achievable performance and cost limits of pressure vacuum swing adsorption (PVSA) processes should the ideal bespoke adsorbent be made. These studies show what conditions stand-alone PVSA processes are attractive and when they should not be considered. Finally, recent developments in physics-informed neural networks (PINNS) enable the rapid solution of complex partial differential equations, providing tools to potentially identify optimal cycle configurations. Ultimately, we provide areas where further developments are required and emphasize the need for strong collaborations between chemists and chemical engineers to move rapidly from discovery to field trials, as we do not have much time to fulfill commitments to net-zero targets.
综述
使用固体吸附剂进行吸附正成为燃烧后二氧化碳捕集领域中基于胺的液体吸收法的有力竞争者。在过去20多年里，人们在开发用于二氧化碳捕集的吸附工艺方面投入了大量精力。特别是，在开发适用于此应用的新型吸附剂方面投入了大量努力。这些努力已产生了数十万种（假设的和实际的）吸附剂，例如沸石和金属有机框架材料（MOF）。确定用于二氧化碳捕集的合适吸附剂仍然是一项具有挑战性的任务。大多数研究都集中在基于某些吸附指标来识别吸附剂。最近的研究表明，吸附剂的性能与它所应用的工艺密切相关。因此，任何有意义的筛选都应考虑工艺的复杂性。然而，吸附过程的模拟和优化计算量很大，因为它们涉及传热和传质前沿的同时传播；该过程是循环的，并且没有直接的设计工具，从而使得基于大规模工艺的吸附剂筛选难以实现。
本综述讨论了四篇论文，这些论文开发了计算方法，将基于工艺的评估纳入自下而上（从化学到工程）的筛选问题和自上而下（从工程到化学）的逆问题中。我们讨论了机器辅助吸附过程学习与仿真（MAPLE）框架的开发，这是一种基于深度人工神经网络（ANN）的替代模型，它可以通过考虑工艺和材料输入来预测工艺水平的性能。该框架已通过实验验证，能够对大型吸附剂数据库进行可靠的、基于工艺的筛选。然后我们讨论了工艺工程工具如何能在吸附剂筛选之外得到应用，即估计如果制造出理想的定制吸附剂，变压真空吸附（PVSA）工艺实际可达到的性能和成本极限。这些研究表明了独立的PVSA工艺在哪些条件下具有吸引力以及何时不应予以考虑。最后，物理信息神经网络（PINN）的最新进展能够快速求解复杂的偏微分方程，为潜在地识别最优循环配置提供了工具。最终，我们指出了需要进一步发展的领域，并强调化学家和化学工程师之间需要紧密合作，以便迅速从发现阶段进入现场试验阶段，因为我们没有太多时间来履行净零目标的承诺。
