Elucidating the impact of carbon nanoparticles and surfaces on gas hydrates and developing machine learning kinetic models

阐明碳纳米粒子和表面对天然气水合物的影响并开发机器学习动力学模型

• 批准号: RGPIN-2022-03673
• 负责人: Englezos, Peter
• 金额: $ 2.4万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746767
• 关键词: Elucidating; impact; carbon; nanoparticles; surfaces

Gas or clathrate hydrates are non-stoichiometric crystalline inclusion compounds formed by water acting as a host to many atomic and molecular guests such as hydrogen, xenon, nitrogen, carbon dioxide, and hydrogen sulfide among others. Gas hydrates are of interest in a broad spectrum of human activities related to energy, especially to mitigate the risk of hydrate formation in hydrocarbon production, processing and transportation facilities. In addition, there is a growing interest for the development of new energy efficient technologies such as carbon dioxide capture, seawater desalination, natural gas and hydrogen storage. The earth's methane hydrates are also of interest as potential positive feedback to intensify climate change driven phenomena. The long-term goal of the proposed research program is to advance our fundamental understanding of gas hydrates. Understanding the thermodynamic conditions that favour the formation of gas hydrates and the factors affecting the kinetics is required for the design of facilities and processes to mitigate and manage the risk of hydrates and to develop gas hydrate technologies for energy, environmental and other applications. Moreover, our goal is also to understand how to properly integrate such knowledge into hydrate-based technologies within a sustainable process design framework.    In this proposal, we aim to determine the impact of suspended carbon nanotubes, silica nanoparticles and cellulose nanocrystals on gas hydrate nucleation and crystal growth. This work will be done with gas/liquid crystallization experiments in custom-made high pressure stirred reactors (crystallizers) as well as with high pressure calorimetry. Statistical tests of significance will be employed to decide whether there is an effect or not. There is a need to reconcile reports and this will be done by using statistical tests of significance. Moreover, we aim to probe the mechanism by which carbon nanotube and cellulosic nanocrystal surfaces influence hydrate formation at the molecular level by developing a molecular dynamics (MD) simulation framework. This effort builds on our previous MD simulation work which showed how silica surfaces influence gas hydrate formation and dissociation. That work was supported by a previous Discovery grants program. We also aim to develop a new generation of hydrate kinetic models based on machine learning. This effort builds on our work and expertise in parameter estimation and optimization. The new models will be integrated into gas hydrate-based processes to describe industrial scale crystallizers. Following this integration, we aim to develop a generalized method based on thermodynamics to assess the energy efficiency of these technologies. The technology readiness level of the technologies will also be assessed. The proposed research will be carried by two Doctoral and two Masters students. Five undergraduate students will also be involved in the research.

气体或围层水合是由水作为许多原子和分子宾客（例如氢，Xenon，氮，氮，二氧化碳和硫化氢等）的宿主形成的非化学计量晶体包含化合物。气体水合物在与能源有关的广泛人类活动中引起了人们的关注，尤其是为了减轻碳氢化合物生产，加工和运输设施中水合物形成的风险。此外，人们对开发新的节能技术（例如二氧化碳捕获，海水脱盐，天然气和氢存储）的兴趣越来越大。地球的甲烷水合物也引起了人们的关注，因为潜在的积极反馈加剧了气候变化驱动现象。拟议的研究计划的长期目标是提高我们对气体水合物的基本理解。了解有利于形成气体水合物的热力学条件以及影响动力学的因素，即设计设施和过程需要减轻和管理水合的风险并开发用于能源，环境和其他应用的气体水合技术的风险。此外，我们的目标还是了解如何在可持续的过程设计框架内正确地将这种知识正确整合到基于水合物的技术中。在此提案中，我们旨在确定悬浮的碳纳米管，二氧化硅纳米颗粒和纤维素纳米晶体对气体水合物成核和晶体生长的影响。这项工作将通过定制高压发达反应器（结晶器）以及高压量热法的气体/液体结晶实验完成。是否有效果，将进行显着性统计检验。需要调和报告，这将通过使用重要性统计测试来完成。此外，我们旨在探测碳纳米管和纤维素纳米晶体表面通过开发分子动力学（MD）模拟框架在分子水平上影响水合物形成的机制。这项工作基于我们以前的MD模拟工作，该工作表明了二氧化硅表面如何影响气体水合物形成和解离。这项工作得到了以前的Discovery Grant计划的支持。我们还旨在根据机器学习开发新一代的水合动力学模型。这项工作以我们的工作和参数估计和优化的专业知识为基础。新模型将集成到基于气体水合物的过程中，以描述工业规模的结晶器。在这种整合之后，我们旨在开发一种基于热力学的广义方法，以评估这些技术的能源效率。技术准备水平也将被评估。拟议的研究将由两个博士和两个硕士学生进行。五名本科生也将参与研究。