权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Design of physical models of reaction kinetics using meta-modelling by taking the example of electrooxidation of methanol in alkaline media

以碱性介质中甲醇电氧化为例，利用元建模设计反应动力学物理模型

基本信息

批准号：
268987021
负责人：
Professorin Dr. Tanja Clees
金额：
--
依托单位：
Institut für Angewandte Materialien - Elektrochemische Technologien (IAM-ET)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/268987021?language=en
关键词：
Design physical models reaction kinetics

项目摘要

The quantitative determination of physical models of kinetics of complex (electro-)chemical reactions is essential for a deeper understanding of the processes that occur during reaction and, thus, also for knowledge-driven improvements of the kinetics. For electrochemical reactions, the challenges lie in qualitative and quantitative identification of processes, their interactions and especially their dependency on electric current and potential. In this project, a new approach for quantitative physical modelling of reaction kinetics is developed and it is applied to methanol oxidation in alkaline direct methanol fuel cells (ADMFCs).Due to the high energy density of methanol, ADMFCs are a highly attractive power supply for portable and mobile applications. Utilisation of an alkaline electrolyte allows usage of non-precious, i.e. inexpensive, metals like nickel as catalyst. ADMFCs using alkaline membrane electrolyte are not yet well investigated and their power is presently significantly lower than that of acidic methanol fuel cells. Slow kinetics of alkaline methanol oxidation is considered as one possible reason for the low performance. Due to the complex interaction of multiphase mass transport, ion transport, sorption processes and various reaction steps, analysis and identification of the kinetics of this reaction is a complex challenge, especially when porous technical electrodes are employed.Within the scope of this project, micro and macro kinetics as well as rate determining steps of alkaline methanol oxidation will be identified by suitable combination of physical model approaches and meta modelling. This is realised by combining knowledge about the single processes in the form of mathematical equations with a database of possible physical model approaches to describe further processes and dependencies on parameters. The crucial step is to further combine these physical equations with meta modelling to extract correct dependencies from experimental results. The method will be first applied to a simpler system without influence of mass transport processes to determine micro kinetics. This will be realised in experiments by using a rotating ring disc electrode (RRDE) dipped into an alkaline methanol solution. In a second step, macro kinetics will be examined at a porous, technical anode of a fuel cell. The measurement data that is required for model identification is generated by experimental examinations at both, RRDE level and cell level. The empirical equations of the meta model will finally be replaced stepwise by physical equations until a completely physical model is formed that describes the reaction processes at an anode of an ADMFC. Analysing this model will provide a deeper understanding of the reaction processes.

定量确定复杂(电)化学反应动力学的物理模型，对于更深入地理解反应过程中发生的过程，从而对于知识驱动的动力学改进是必不可少的。对于电化学反应，挑战在于对过程及其相互作用的定性和定量识别，特别是它们对电流和电位的依赖性。在本项目中，发展了一种新的反应动力学定量物理模拟方法，并将其应用于碱性直接甲醇燃料电池(ADMFC)的甲醇氧化。由于甲醇的高能量密度，ADMFCs是一种非常有吸引力的便携式和移动应用电源。碱性电解液的使用允许使用非贵金属，即廉价的金属，如镍作为催化剂。使用碱性膜电解液的ADMFC还没有得到很好的研究，目前它们的功率明显低于酸性甲醇燃料电池。碱性甲醇氧化动力学缓慢被认为是催化剂性能较低的可能原因之一。由于多相传质、离子传输、吸附过程和各种反应步骤的复杂相互作用，分析和识别该反应的动力学是一个复杂的挑战，特别是在采用多孔技术电极的情况下。在本项目的范围内，碱性甲醇氧化的微观和宏观动力学以及速率决定步骤将通过适当的物理模型方法和元模型相结合来识别。这是通过将数学方程形式的关于单个过程的知识与描述进一步过程和对参数的依赖性的可能的物理模型方法的数据库相结合来实现的。关键的一步是进一步将这些物理方程与元模型相结合，从实验结果中提取正确的依赖关系。该方法将首先应用于不受传质过程影响的更简单的系统，以确定微观动力学。这将在实验中通过使用浸泡在碱性甲醇溶液中的旋转环盘电极(RRDE)来实现。在第二步中，将在燃料电池的多孔技术阳极上检查宏观动力学。模型识别所需的测量数据由RRDE级别和细胞级别的实验检查生成。元模型的经验方程最终将被物理方程逐步取代，直到形成描述ADMFC阳极反应过程的完全物理模型。通过对该模型的分析，可以加深对反应过程的理解。