基于机器学习的CO<sub>2</sub>催化加氢制航空煤油筒式反应器数学模拟分析与优化

黄成; 刘操; 黄磊; 崔灵瑞; 马宏方; 曹发海

doi:10.12434/j.issn.2097-2547.20240131

您当前的位置：

首页 >

文章列表页 >

基于机器学习的CO₂催化加氢制航空煤油筒式反应器数学模拟分析与优化

二氧化碳利用

- 基于机器学习的CO₂催化加氢制航空煤油筒式反应器数学模拟分析与优化
- Mathematical simulation analysis and optimization of cylinder reactor for CO₂ catalytic hydrogenation to jet fuel based on machine learning
- 低碳化学与化工 2024, 49(7): 120-128.
- 作者机构：
  
  1.华东理工大学化工学院，上海 200237
  2.华东理工大学化工学院大型工业反应器工程教育部工程研究中心，上海 200237
- 作者简介：
  
  黄成（1999—），硕士研究生，研究方向为二氧化碳利用工艺开发，E-mail：chenghuang2492@163.com。
  崔灵瑞（1992—），博士，助理研究员，研究方向为重质油及二氧化碳利用工艺开发，E-mail：cuilr@ecust.edu.cn。
- 基金信息：
- DOI：10.12434/j.issn.2097-2547.20240131
  中图分类号： TQ018
- 纸质出版日期：2024-07-25，
  
  收稿日期：2024-04-01，
  
  修回日期：2024-05-08，
- 稿件说明：
扫描看全文
黄成,刘操,黄磊等.基于机器学习的CO₂催化加氢制航空煤油筒式反应器数学模拟分析与优化[J].低碳化学与化工,2024,49(07):120-128.

HUANG Cheng,LIU Cao,HUANG Lei,et al.Mathematical simulation analysis and optimization of cylinder reactor for CO₂ catalytic hydrogenation to jet fuel based on machine learning[J].Low-carbon Chemistry and Chemical Engineering,2024,49(07):120-128.
黄成,刘操,黄磊等.基于机器学习的CO₂催化加氢制航空煤油筒式反应器数学模拟分析与优化[J].低碳化学与化工,2024,49(07):120-128. DOI： 10.12434/j.issn.2097-2547.20240131.

HUANG Cheng,LIU Cao,HUANG Lei,et al.Mathematical simulation analysis and optimization of cylinder reactor for CO₂ catalytic hydrogenation to jet fuel based on machine learning[J].Low-carbon Chemistry and Chemical Engineering,2024,49(07):120-128. DOI： 10.12434/j.issn.2097-2547.20240131.

摘要

借助Fe基催化剂，CO

通过加氢反应可成功转化为高附加值的航空煤油，具有很好的工业应用潜力。目前缺少准确适宜的CO

加氢合成航空煤油的反应器模型，因此亟需对其反应模型进行构建，为相关工艺的工业化提供参考。通过机器学习探究实验条件和关键组分CO和CO

物质的量分数关系，构建了关键组分CO和CO

预测模型；基于Anderson-Schulz-Flory分布规律，构建了碳链增长模型，进一步构建了产物分布模型；基于催化床层物料衡算、热量衡算和压降计算，建立了筒式固定床反应器的一维拟均相模型；通过模拟一定工艺条件得到了CO

加氢合成航空煤油的反应结果，并对工艺条件进行了模拟优化。模拟结果表明，反应器进口温度升高、空速增大，CO

转化率降低，航空煤油集总组分C

时空产率增加；操作压力增大，CO

转化率增加，C

时空产率在反应压力为2.0 MPa时最高；饱和沸腾水温度升高，CO

转化率和C

时空产率增加。最佳CO

加氢制航空煤油反应条件：反应器进口温度为275 ℃、进口压力为2.0 MPa、空速为4000 h

-1

和饱和沸腾水温度为294 ℃。此时CO

转化率为21.95%，C

时空产率为12.14 g/(L·h)，床层压降为0.20 MPa。

Abstract

With Fe-based catalyst

can be successfully converted into high value-added jet fuel via hydrogenation

showing great potential of industrial application. There is a lack of accurate and appropriate reactor model for CO

hydrogenation to jet fuel

therefore it is urgent to construct reaction model and provide reference for the industrialization of related processes. The relationship between experimental conditions and mole fraction of key components CO and CO

was explored through machine learning

and the prediction model of the key components CO and CO

was constructed. Based on the Anderson-Schulz-Flory distribution

the carbon chain growth model was established

and the product distribution model was further constructed. Moreover

based on the calculation results of material balance

heat balance and pressure drop

homogeneous one-dimensional model of cylindrical fixed-bed reactors was established. The results of CO

hydrogenation to jet fuel were obtained by simulating operation conditions

and the operation conditions were optimized. The simulation results show that CO

conversion rate decreases and space time yield of jet f

uel lumped component C

increases with the increase of inlet temperature and space velocity. CO

conversion rate increases with the increase of operating pressure. The operating pressure of maximum space time yield of C

is 2.0 MPa. CO

conversion rate and space time yield of C

increase with temperature of saturated boiling water increasing. The optimum reaction conditions are inlet temperature of 275 ℃

inlet pressure of 2.0 MPa

space velocity of 4000 h

-1

and saturated boiling water temperature of 294 ℃. Currently

the CO

conversion rate is 21.95%

the space time yield of C

is 12.14 g/(L·h)

and the pressure drop is 0.20 MPa.

关键词

CO2加氢航空煤油机器学习筒式固定床反应器反应器模拟

Keywords

CO2 hydrogenationjet fuelmachine learningcylindrical fixed-bed reactorreactor simulation

references

KALLIO P, PASZTOR A, AKHTAR M K, et al. Renewable jet fuel [J]. Current Opinion in Biotechnology, 2014, 26: 50-55.

ZHANG L, DANG Y R, ZHOU X H, et al. Direct conversion of CO2 to a jet fuel over CoFe alloy catalysts [J]. The Innovation, 2021, 2(4): 100170.

YAO B Z, XIAO T C, MAKGAE O A, et al. Transforming carbon dioxide into jet fuel using an organic combustion-synthesized Fe-Mn-K catalyst [J]. Nature Communications, 2020, 11(1): 6395.

ARAB S, COMMENGE J M, PORTHA J F, et al. Methanol synthesis from CO2 and H2 in multi-tubular fixed-bed reactor and multi-tubular reactor filled with monoliths [J]. Chemical Engineering Research and Design, 2014, 92(11): 2598-2608.

徐春华. 大型甲醇合成工艺技术研究进展[J]. 化学工程与装备, 2019, (5): 230-232.

XU C H. Research progress of large-scale methanol synthesis technology [J]. Chemical Engineering and Equipment, 2019, (5): 230-232.

代松涛, 马宏方, 张海涛, 等. 60万吨/年甲醇合成反应器模拟及分析[J]. 计算机与应用化学, 2017, 34(7): 533-536.

DAI S T, MA H F, ZHANG H T, et al. Simulation and analysis of methanol synthesis reactor with the scale of 600 kt/a [J]. Computer and Applied Chemistry, 2017, 34(7): 533-536.

JIANG K, ASHWORTH P. The development of carbon capture utilization and storage (CCUS) research in China: A bibliometric perspective [J]. Renewable and Sustainable Energy Reviews, 2021, 138: 110521.

JIANG K, ASHWORTH P, ZHANG S Y, et al. Print media representations of carbon capture utilization and storage (CCUS) technology in China [J]. Renewable and Sustainable Energy Reviews, 2022, 155: 111938.

SUNPHORKA S, CHALERMSINSUWAN B, PIUMSOMBOON P. Artificial neural network model for the prediction of kinetic parameters of biomass pyrolysis from its constituents [J]. Fuel, 2017, 193: 142-158.

胡振中. 生物质热溶富碳过程的脱氧—芳构化机理及其产物的电化学应用研究[D]. 武汉: 华中科技大学, 2022.

HU Z Z. Research on deoxygenating and aromatization mechanism of “thermal-dissolution based carbon enrichment” treatment of biomass and electrochemical applications of products [D]. Wuhan: Huazhong University of Science and Technology, 2022.

曹发海, 马宏方, 崔灵瑞, 等. 筒式反应器: 216879280U [P]. 2022-07-05.

CAO F H, MA H F, CUI L R, et al. Cylindrical fixed-bed reactor: 216879280U [P]. 2022-07-05.

VAN DER LAAN G P, BEENACHERS AA C M, KRISHNA R. Multicomponent reaction engineering model for Fe-catalyzed Fischer-Tropsch synthesis in commercial scale slurry bubble column reactors [J]. Chemical Engineering Science, 1999, 54(21): 5013-5019.

WILLAUER H D, ANANTH R, OLSEN M T, et al. Modeling and kinetic analysis of CO2 hydrogenation using a Mn and K-promoted Fe catalyst in a fixed-bed reactor [J]. Journal of CO2 Utilization, 2013, 3/4: 56-64.

李晨, 马向东, 应卫勇, 等. ZrO2改性对Co-Ru/γ-Al2O3催化剂F-T合成性能的影响[J]. 天然气化工—C1化学与化工, 2008, 33(2): 17-20.

LI C, MA X D, YING W Y, et al. Effect of ZrO2 modification on catalytic performance of Co-Ru/γ-Al2O3 for Fischer-Tropsch synthesis [J]. Natural Gas Chemical Industry, 2008, 33(2): 17-20.

GHOFRAN P M, ATASHI H, ZOHDI F H, et al. Effect of temperature on deactivation models of alumina supported iron catalyst during Fischer-Tropsch synthesis [J]. Petroleum Science and Technology, 2019, 37(5): 500-505.

DE-SMIT E, WECKHUYSEN B M. The renaissance of iron-based Fischer-Tropsch synthesis: On the multifaceted catalyst deactivation behavior [J]. Chemical Society Reviews, 2008, 37(12): 2758-2781.

浏览量

下载量

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

高分散Ru/Si₃N₄催化剂的制备及其在CO₂加氢中的应用

造孔剂诱导合成FeCoCuAl催化剂及其与ZSM-5复合催化CO₂加氢制高碳烃性能

均苯三甲酸辅助合成CuZnAlZr催化CO₂加氢制甲醇

基于IAO-PNN模型的天然气水合物生成条件预测研究

基于机器学习的CO2催化加氢制航空煤油筒式反应器数学模拟分析与优化

Mathematical simulation analysis and optimization of cylinder reactor for CO2 catalytic hydrogenation to jet fuel based on machine learning

DOI：10.12434/j.issn.2097-2547.20240131

摘要

Abstract

关键词

Keywords

references

基于机器学习的CO₂催化加氢制航空煤油筒式反应器数学模拟分析与优化

Mathematical simulation analysis and optimization of cylinder reactor for CO₂ catalytic hydrogenation to jet fuel based on machine learning