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纳秒脉冲等离子体甲烷转化与重油加氢反应耦合基础研究

项目来源

国家自然科学基金(NSFC)

项目主持人

邵涛

项目受资助机构

中国科学院电工研究所

项目编号

51637010

立项年度

2016

立项时间

未公开

研究期限

未知 / 未知

项目级别

国家级

受资助金额

300.00万元

学科

工程与材料科学-电气科学与工程-高电压与放电

学科代码

E-E07-E0705

基金类别

重点项目

关键词

纳秒脉冲放电 ; 等离子体裂解 ; 等离子体转化 ; 低温等离子体 ; 重油加氢 ; nanosecond-pulse discharge ; low-temperature plasma ; plasma cracking ; plasma conversion ; heavy oil hydrogenation

参与者

杨清河；聂红；任成燕；韩伟；王瑞雪；胡大为；孙广生；张润强；徐旭哲

参与机构

中石化石油化工科学研究院有限公司

项目标书摘要：随着原油资源日益紧缺，采用加氢技术将原油中高占比的重油转化为轻质油，有利于优化我国能源结构和发展战略。为了解决传统重油加氢技术采用高温高压临氢环境及催化剂带来的能耗高、催化剂易结焦失活、轻油收率低等缺点，提出一种等离子体甲烷转化与加氢反应相耦合的新型重油加氢路线，即采用纳秒脉冲放电技术转化甲烷，同时实现重油高效加氢并增产高附加值低碳烯烃。通过掌握适用于重油加氢反应的高活性纳秒脉冲等离子体激励技术，实现对甲烷等离子体中活性粒子和产物选择性的调控，尤其是对氢自由基产生、输运及其反应时空分布的诊断，优化氢自由基与重油反应的耦合时间和作用方式，并为调控产物选择性、提高能量利用效率等提供依据。研究反应机理和动力学过程，掌握纳秒脉冲等离子体与重油反应耦合体系中物理化学反应机制。研究结果可实现电工技术与炼油化工优势互补和有机结合，丰富脉冲等离子体的技术创新，具有重要的理论价值和应用前景。

Application Abstract: With the increasing scarcity of crude resource,hydrogenation technology can be used for heavy oil(with a high proportion in crude oil)into light oil,and the technology can help to optimize the structure of China's energy strategy.Because of very strict reaction conditions with high temperature and hydrogen pressure and the usage of series of catalysts,there meanwhile exist many obstacles,such as high energy-consumption,big investment for devices,easy-inactivation for the catalysts,low-yield for light oils,and so on.Therefore,a novel route is proposed for a coupling of plasma conversion of methane and heavy oil hydrogenation,that is,this project puts forward a nanosecond-pulsed discharge plasma technology to activate CH4,which can produce hydrogen radicals to achieve efficient heavy oil hydrogenation and meanwhile yield high value-added light olefins.Nanosecond-pulsed discharge is used as the excitation technique for highly active plasmas that suitable for heavy oil hydrogenation reactions.The selectivity of active particles and products in CH4 plasmas is controlled to establish the corresponding chemical reaction kinetics,especially,the spatio-temporal distribution is diagnosed on the generation,transport,and reaction processes of the critical hydrogen radicals.The reaction conditions of plasmas coupled with heavy oil hydrogenation are investigated.The studies on the reaction kinetic and mechanism are carried out to guide the optimization of the nanosecond-pulse discharge plasma technology of heavy oil hydrogenation.The work can enable a combination of different technical advantages from oil refining,chemistry and electrical engineering fields,as well as enrich analytic methods and advance technical innovation of low-temperature pulsed plasma technology,thereby will be of theoretical significance and applicable potential.

项目受资助省

北京市

项目结题报告(全文)

随着原油资源重质化程度不断升高，亟需提升重油加工能力，以保障我国能源安全。本项目提出等离子体甲烷活化与加氢反应相耦合的新型重油加氢路线，实现了常温常压重油加氢并副产乙烯，同时避免了甲烷制氢能耗高、催化剂易失活、工艺流程长等传统重油加氢技术瓶颈问题，具有重要科学和工程意义。围绕脉冲等离子体甲烷和重油高效转化，掌握了适用于重油加氢反应的脉冲等离子体源技术，实现了甲烷等离子体中活性粒子和产物选择性调控，优化了氢自由基与重油反应耦合方式，揭示了脉冲等离子体与重油耦合体系的反应机制，取得重要结果如下:① 采用脉冲介质阻挡放电(DBD)等离子体，结合结构化镍基催化剂，解决了竞争反应消耗氢自由基等问题，发现了等离子体加氢反应“窗口”条件，突破了常规加氢反应的高温高压氢气条件限制，实现了常温常压甲烷直接加氢;② 揭示了甲烷放电中氢自由基产生和输运过程，优化了氢自由基与重油作用方式，国内率先测定氢自由基密度为10^15 cm^-3量级，约150-200微秒衰减1个数量级，寿命为毫秒量级，实现了厘米级氢自由基输运;③ 建立了甲烷等离子体加氢反应的反应动力学模型，并进行了密度泛函计算，从分子、原子层面揭示了甲烷脉冲DBD等离子体加氢反应的反应机制和动力学过程;④ 提出了甲烷等离子体—重油共裂解制乙烯路线，通过调控脉冲火花放电等离子体，甲烷和重油转化率均超过80%,氢气选择性分别为40%和7%,乙炔选择性均超过60%和40%。裂解气经后级催化生成乙烯，乙烯总收率超过25%。项目研究成果为碳基能源小分子高效转化技术和重油大分子高值化利用技术提供了科学理论依据和关键技术支撑，缩短了甲烷转化和重油加工的工艺流程、减少碳排放，助力我国“碳达峰、碳中和”目标。同时培养了一批等离子体能源化工的专业技术人才，丰富了脉冲等离子体的技术创新，推进了电工技术与炼油化工交叉融合，具有重要的理论价值和应用前景。

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  • 1.Deep Learning + Complex Physics Field Modeling: Illustrated by the Example of Numerical Investigation on Low Temperature Plasma

    • 关键词：
    • Complex networks;Deep neural networks;Feedforward neural networks;Learning systems;Partial differential equations;Complex physic field;Constraints method;Deep learning;Field model;Learning efficiency;Learning fields;Low temperature plasmas;Numerical investigations;Physical field;Physical process
    • Zhao, Chaoqun;Pan, Jie;Li, Bin;Liu, Yun
    • 《10th Frontier Academic Forum of Electrical Engineering, FAFEE 2022》
    • 2023年
    • December 7, 2022 - December 8, 2022
    • Xian, China
    • 会议

    In deep learning field, the appropriate selection of constraints directly affects the learning efficiency and learning results of a network. Partial differential equations (PDEs) which are extremely accurate compared to the constraint methods employed in traditional neural networks are natural constraint models in complex physical fields. In this paper, based on this premise we propose a new method to solve complex physical field simulation problems. We approximate the variables in a complex physical field by building a feedforward deep neural network while applying the chain rule of calculus to encode the corresponding PDEs into the loss function to add constraints. It is worth noting that we have only used part of the equations of the physical process rather than all of them. In other words, instead of solving the equations we learn the whole physical process via the partial PDEs constraints and a few data points. We verify the effectiveness of the deep learning method via learning low-temperature plasma model that is composed of complex physical processes. This technique presents a paradigm for the simulation of complex physical field problems. © 2023, Beijing Paike Culture Commu. Co., Ltd.

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