NSR催化剂上贵金属宏观和微观分布的优化研究

Due to the increasingly stringent NOx emission regulations, the development of NOx storage and reduction (NSR) technique is currently of great interest and importance to environmental and catalytic engineers. During the last decade, the gas-solid interactions involved in NSR operation have been extensively studied and a widely accepted microkinetic model has been established. However, the micro-mechanisms and kinetics of inter-component spillover reactions remain unclear to the research community, which has become a primary barrier for further improvement of NSR efficiency by systematic optimization of the precious metal distribution. In this project, the microkinetics of spillover reactions in NSR processes using Pt/BaO/Al2O3 monolithic catalysts and H2 as the reductant will be investigated first as an extension of our previous studies. For this purpose, all experiments are specifically designed to diminish mass and heat transfer effects, rather than to satisfy any kind of performance criteria. In addition, the complicated NSR process is divided into several sub-processes that are easier to study individually. As such, the uncertainties introduced by the relevance of various sub-processes and kinetic parameters can be eliminated. This part of the project is aimed to identify the spillover species, elucidate the mechanisms of spillover reactions, and construct a microkinetic model that can be used to explain the effects of Pt loading and distribution on NSR performance. The kinetic model parameters will also be estimated with the guidance of parametric sensitivity studies. Then, Pt distribution in both macro- and micro- scales will be systematically optimized, using the microkinetic model and parameters formerly determined, to reduce the overall Pt loading, lessen reductant consumption and enhance the selective conversion of NOx towards desired products. As a result of the intrinsic complexity of NSR process, the manipulation of Pt distribution involves a multi-objective optimization problem with multiple parameters and constraints, some of which may be discrete and indifferentiable. This problem will be solved in this study by the use of nondominated sorting genetic algorithm II with jumping genes (NSGA-II-JG). The obtained optimal solutions form a curve in the objective function space, generally referred to as the pareto optimality. Its trend will be analyzed together with the parameter sensitivity to reveal the inherent connections between each of the operating parameters and the objective functions. To our knowledge, this is the first attempt to examine the feasibility of improving NSR efficiency by adjusting distribution of precious metal on the monolithic catalysts. This work will present a theoretical framework consisting of experiments, microkinetic modeling, and optimization that may be applied to similar studies. The research findings will provide practical guidance for industrial design and optimization of NSR processes in the future.

氮氧化物存储还原（NSR）技术是近年来环境化工研究领域的一个重要课题。目前NSR过程的微观机理和动力学在国际上仍是一个难点问题，这使得贵金属在催化剂上分布的研究无法有效展开。本项目将在申请人以往工作的基础上，进一步深化以H2为还原剂的NSR过程的基础理论研究，以微观反应机理和动力学为导向，有针对性地设计实验路线并选择操作条件，尽量消除或降低传质传热过程的影响；系统性地把NSR这一复杂过程分解成若干较易研究的子过程，避免各子过程及动力学参数相关性造成的不确定因素；阐明NSR催化剂Pt/BaO/Al2O3上溢流反应的微观机理，建立一个较为完善的微观动力学模型并估算模型参数。在此基础上，对Pt宏观和微观分布的优化问题进行合理定义并有效求解。结合优化条件下目标函数的变化趋势和参数敏感性分析，揭示操作参数对目标函数的影响规律。本项目研究成果将为深入开展NSR过程的设计与优化提供必要的理论依据。

氮氧化物存储还原（NSR）是一个周期性操作的催化过程，也是环境化工领域的一个热点方向。本项目在国家自然科学基金资助下进行，旨在进一步深化NSR过程的基础理论研究，建立一个较为完善的微观动力学模型并估算模型参数，对周期性操作过程的多目标优化问题进行合理定义和有效求解，并在此基础上对贵金属Pt分布进行优化。按照申请书和计划书安排，首先将NSR复杂的过程拆分成若干若干相对简单的子过程，根据驻态实验结果，建立不同子过程的微观动力学模型。驻态模拟过程中提出一个新的整体式反应器模型，即可包括微观动力学和温度的影响，又可规避表面活性物种焓的估算；开发出一种新的延拓算法，可以避免显性Jocobian矩阵的推导，简化了变成问题；通过拟合实验数据估算模型参数。随后对动态过程进行了探索，着重探讨了温度变化对吸附过程的影响，并根据实验现象确立了一个以NO2为核心的溢流反应动力学模型，明确了传质现象在反应器内部起到的决定性影响作用。对周期性操作过程的多目标优化进行了尝试，发现由于温度对吸附强度的影响，在温度渐变区域会形成浓度的高度富集。优化结果表明，相同总负载前提下，Pt的宏观和微观分布对NSR性能具有显著影响；相同转化效果限制下，可通过对宏观分布的调控，有效降低Pt总负载。此外，还对从工程角度对废气回收新材料的开发进行了一些尝试。提出一种原位合成负载型多孔吸附剂的新方案，将四甲基胍这一有机强碱限制在分子筛微孔内，与酸性气体通过氢键结合，从而可以实现常温下的可逆吸附；畅通的介孔结构可以有效提高传质效率；通过整体式设计显著降低床层压降。相关成果在AIChE Journal、Chemical Communications等期刊上发表SCI论文6篇。项目研发期间，培养研究生9名，6名已经毕业并获得硕士学位。