EFRI DCheM: One-step conversion of CH4 and CO2 to liquid fuels with the use of a multi-functional pseudo catalytic system

EFRI DCheM：使用多功能伪催化系统将 CH4 和 CO2 一步转化为液体燃料

• 批准号: 2029282
• 负责人: Liang-Shih Fan
• 金额: $ 200万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029282&HistoricalAwards=false
• 关键词: EFRI; DCheM; step; conversion; CH4

Stranded natural gas resources are currently flared due to economic limitations associated with prohibitive transportation costs and small reservoir sizes. Successfully transforming these remotely distributed gas resources to useful energy products will contribute significantly to the U.S. energy economy and its energy security. The goal of this project is to develop a small-scale modular chemical processing system to convert stranded natural gas and carbon dioxide into value-added liquid fuel products. This technology as proposed is transformative and environmentally sustainable as it will achieve both the monetization of stranded gas resources and will consume carbon dioxide as a feedstock in the gas conversion process. The researchers are Ohio State University (OSU) faculty members who will use a data-driven approach to integrate the reactor system components and will further the fundamental understanding of the gas upgrading chemistry by identifying an efficient catalyst to promote the reactions. This interdisciplinary OSU project team will work with industrial partners Velocys, Inc. and Jan Lerou LLC. to leverage their industrial expertise to optimize the system design using an advanced manufacturing protocol and state-of-the-art computational tools. The proposed project will advance fundamental understanding of liquid fuel conversion processes as well as provide multiple learning experiences for K-12, undergraduate, and graduate students. The project team will work with local high schools to promote the inclusion of under-represented students in its research activities. The foundational knowledge generated by this project will also serve as a science, engineering, and technology-oriented learning resource for undergraduate and graduate education. The proposed work addresses the technological and scientific barriers to building a thermo-catalytic flared-gas reforming (TC-FGR) system for monetizing stranded gas resources. The TC-FGR system is a small-scale, modular GTL (gas to liquid) process that intensifies syngas production from natural gas and integrates a commercially demonstrated micro-channel F-T (Fischer-Tropsch) synthesis system in one reactor vessel. Furthermore, a novel pseudo-catalytic metal oxide (PMO) material will be developed to reduce the unit operations required for conventional small-scale F-T systems. The research team will develop the PMO material using first-principles computational methods and experimental parametric testing. The team also will develop a machine learning-informed integrated, flexible reactor design that intensifies modular GTL systems and will assess the economic feasibility of the proposed technology. The PMO is an iron-based metal oxide composite, capable of exhibiting several oxidation states, that allows CH4 to react with the PMO, abstracting the lattice oxygen to form partial-combustion products CO and H2. At the same time, CO2/H2O re-oxidizes the PMO forming additional CO/H2. This unique activation with the PMO acting as the oxygen mediator adds additional degrees of freedom for process optimization, enhancing the production rate of syngas as well as providing a means of controlling its composition. The unique microchannel design used by the industrial partner of this project results in enhanced heat and mass transfer capabilities; this coupled with a highly active catalyst allows for productivity that is 10-15 times higher than conventional F-T systems. The project will synergistically use multi-scale models and advanced optimization/control methods at every project step to ensure that the intensified TC-FGR system is viable in distributed applications with small economic margins. The resulting integrated, modular TC-FGR system can be deployed over several wells, providing a transformative alternative to the wasteful gas flaring that is current practice. Widespread applications of the TC-FGR stranded gas process will further mitigate greenhouse gas emissions through carbon dioxide conversion. Sustainable technology such as TC-FGR constitutes a bridge towards reducing the carbon footprint of fossil fuels.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于与高昂的运输成本和小的储层尺寸相关的经济限制，搁浅的天然气资源目前被燃烧。成功地将这些远程分布的天然气资源转化为有用的能源产品将为美国的能源经济和能源安全做出重大贡献。该项目的目标是开发一个小型模块化化学处理系统，将滞留的天然气和二氧化碳转化为增值的液体燃料产品。拟议的这项技术具有变革性和环境可持续性，因为它将实现闲置天然气资源的货币化，并将在天然气转化过程中消耗二氧化碳作为原料。研究人员是俄亥俄州州立大学（OSU）的教职员工，他们将使用数据驱动的方法来整合反应器系统组件，并通过确定有效的催化剂来促进反应，从而进一步了解气体升级化学。这个跨学科的俄勒冈州立大学项目团队将与工业合作伙伴Velocys，Inc。Jan Lerou LLC.利用他们的工业专业知识，使用先进的制造协议和最先进的计算工具来优化系统设计。拟议的项目将推进对液体燃料转化过程的基本理解，并为K-12，本科生和研究生提供多种学习经验。项目小组将与当地高中合作，促进将代表性不足的学生纳入其研究活动。该项目产生的基础知识也将作为本科和研究生教育的科学，工程和技术导向的学习资源。拟议的工作解决了技术和科学的障碍，建立一个热催化火炬气重整（TC-FGR）系统货币化搁浅的气体资源。TC-FGR系统是一种小型模块化GTL（气制液）工艺，可强化天然气合成气生产，并在一个反应器容器中集成了一个经过商业验证的微通道F-T（费托）合成系统。此外，一种新型的准催化金属氧化物（PMO）材料将被开发，以减少传统的小规模F-T系统所需的单元操作。研究小组将使用第一原理计算方法和实验参数测试开发PMO材料。该团队还将开发一种基于机器学习的集成灵活反应堆设计，以加强模块化GTL系统，并将评估拟议技术的经济可行性。PMO是一种铁基金属氧化物复合材料，能够表现出几种氧化态，其允许CH 4与PMO反应，提取晶格氧以形成部分燃烧产物CO和H2。同时，CO2/H2O再氧化PMO形成额外的CO/H2。PMO作为氧介体的这种独特活化为工艺优化增加了额外的自由度，提高了合成气的生产率，并提供了控制其组成的手段。该项目的工业合作伙伴使用的独特微通道设计增强了传热和传质能力;再加上高活性催化剂，生产率比传统的F-T系统高10-15倍。该项目将在每个项目步骤中协同使用多尺度模型和先进的优化/控制方法，以确保强化的TC-FGR系统在分布式应用中具有较小的经济利润。由此产生的集成的模块化TC-FGR系统可以部署在多个威尔斯井上，为当前实践中浪费的天然气燃烧提供了一种变革性的替代方案。TC-FGR滞留气体工艺的广泛应用将通过二氧化碳转化进一步减少温室气体排放。TC-FGR等可持续技术是减少化石燃料碳足迹的桥梁。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

SBA-16-Mediated Nanoparticles Enabling Accelerated Kinetics in Cyclic Methane Conversion to Syngas at Low Temperatures

• DOI: 10.1021/acsaem.0c01495
• 发表时间: 2020-08
• 作者: Yan Liu;L. Qin;Jianhua Pan;Yu‐Yen Chen;Josh W. Goetze;Dikai Xu;Jonathan A. Fan;L. Fan

COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information

• DOI: 10.1016/j.compchemeng.2022.107700
• 发表时间: 2021-05
• 期刊: Comput. Chem. Eng.
• 作者: J. Paulson;Congwen Lu

Constrained robust Bayesian optimization of expensive noisy black‐box functions with guaranteed regret bounds

对昂贵的嘈杂黑盒函数进行约束鲁棒贝叶斯优化，并保证后悔范围

• DOI: 10.1002/aic.17857
• 发表时间: 2022
• 期刊: AIChE Journal
• 影响因子: 3.7
• 作者: Kudva, Akshay;Sorourifar, Farshud;Paulson, Joel A.
• 通讯作者: Paulson, Joel A.

Adversarially robust Bayesian optimization for efficient auto‐tuning of generic control structures under uncertainty

对抗性鲁棒贝叶斯优化，可在不确定性下实现通用控制结构的高效自动调整

• DOI: 10.1002/aic.17591
• 发表时间: 2022
• 期刊: AIChE Journal
• 影响因子: 3.7
• 作者: Paulson, Joel A.;Makrygiorgos, Georgios;Mesbah, Ali
• 通讯作者: Mesbah, Ali