Optimized adsorption processes for next-generation adsorbents

优化下一代吸附剂的吸附工艺

• 批准号: RGPIN-2019-05018
• 负责人: Rajendran, Arvind
• 金额: $ 2.04万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738014
• 关键词: Optimized; adsorption; processes; next; generation

Adsorption technology, that uses a solid sorbent, is considered an energy efficient alternative to conventional separation processes such as distillation and absorption. Adsorption, being a modular, cyclic, unsteady-state process is challenging to design and optimize: Multiple configurations (cycles) can be synthesized; and the estimation of the process performance for a given set of operating conditions is time-consuming. Note that changing the cycle configuration can significantly alter the process performance. Hence, finding the best cycle and its optimum operating conditions using mathematical models continues to be a major bottleneck in ensuring that the fullest potential of these processes+sorbents are realized. The challenge is further compounded by the fact that the field of adsorbent synthesis is going through a revolution with 1000s of new structures reported each year. Screening these adsorbents and "marrying" them to the cycles that maximize their potentials is critical to translate these discoveries into practice. This discovery program whose long-term aim is to reduce greenhouse gas emissions by developing CO2 reduction technologies and by improving the energy efficiency of separation processes focuses on 3 projects: PROJECT 1: Develop model-free descriptions of adsorption isotherms for systems that show exotic equilibrium behaviour; extend them to describe competitive adsorption; and test their efficiencies through experimental demonstration. PROJECT 2: Develop simplified models for rapid screening of large adsorbent databases; extend them to generate process superstructures;  discover optimal processes to minimize energy consumption and plant footprint; and validate them through experiments. PROJECT 3: Develop machine learning tools for the design and optimization of adsorption processes; Incorporate them in optimization routines and validate them through experiments. As test systems, we will consider 1. O2 purification from air with the aim to lower the cost of medical oxygen; 2. Post-combustion CO2 capture, which is critical for the reduction of greenhouse gas emissions. Identifying potential adsorbents and processes will lead to lowering the cost of CO2 capture. The program leverages on 20 years of research expertise in design and optimization of adsorption processes that has resulted in multiple novel processes and first-of-a-kind pilot-scale demonstrations. HQP participating in this program will be trained in process design, optimization, energy analysis and experimentation that are vital to the Canadian economy. HQP will receive excellent training in  scientific communication and professional skills that will put them on a path of success just like the alumni who have taken up faculty positions around the world, research positions in leading industries and academic institutions.

使用固体吸附剂的吸附技术被认为是传统分离工艺（如蒸馏和吸收）的节能替代方案。吸附是一个模块化的、循环的、非稳态的过程，其设计和优化具有挑战性：可以合成多个配置（循环）;并且对于给定的一组操作条件的过程性能的估计是耗时的。请注意，改变循环配置可显著改变工艺性能。因此，使用数学模型找到最佳循环及其最佳操作条件仍然是确保这些过程+吸附剂的最大潜力得以实现的主要瓶颈。由于吸附剂合成领域正在经历一场革命，每年报道的新结构达1000种，这一挑战进一步加剧。筛选这些吸附剂并将其与最大限度地发挥其潜力的循环“结合”，对于将这些发现转化为实践至关重要。该发现计划的长期目标是通过开发CO2减排技术和提高分离过程的能源效率来减少温室气体排放，重点关注3个项目：项目1：为表现出奇异平衡行为的系统开发吸附等温线的无模型描述;将其扩展到描述竞争吸附;并通过实验演示测试其效率。项目二：开发用于快速筛选大型吸附剂数据库的简化模型;将其扩展以生成工艺上层结构;发现最佳工艺以最大限度地减少能耗和工厂占地面积;并通过实验对其进行验证。项目3：开发用于吸附工艺设计和优化的机器学习工具;将其纳入优化程序并通过实验进行验证。作为测试系统，我们将考虑1。从空气中净化氧气，以降低医用氧气的成本; 2。燃烧后二氧化碳捕获，这对减少温室气体排放至关重要。确定潜在的吸附剂和工艺将导致降低二氧化碳捕获的成本。该计划利用了20年来在吸附工艺设计和优化方面的研究专业知识，已经产生了多个新工艺和首次中试规模的示范。参与该计划的HQP将接受对加拿大经济至关重要的工艺设计，优化，能源分析和实验方面的培训。HQP将接受科学交流和专业技能方面的优秀培训，这将使他们走上成功之路，就像那些在世界各地担任教职，在领先行业和学术机构担任研究职位的校友一样。