超重力反应器天然气脱碳过程反应传质智能预测

张威; 张伟文; 李欣洋; 傅程; 黄斌; 李玉星

doi:10.12434/j.issn.2097-2547.20240032

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超重力反应器天然气脱碳过程反应传质智能预测

绿色低碳化工技术

- 超重力反应器天然气脱碳过程反应传质智能预测
- Intelligent prediction of mass transfer during natural gas decarburization in high gravity reactor
- 低碳化学与化工 2024, 49(12): 96-102.
- 作者机构：
  
  1.重庆科技大学石油与天然气工程学院，重庆 401331
  2.中国石油大庆油田有限责任公司第三采油厂，黑龙江大庆 163000
  3.青岛能源设计研究院有限公司，山东青岛 266000
  4.重庆科技大学非常规油气开发研究院，重庆 401331
  5.中国石油大学（华东）储运与建筑工程学院，山东青岛 266580
- 作者简介：
  
  张威（1992—），博士，讲师，研究方向为超重力碳捕集技术，E-mail：zhangwei@cqust.edu.cn。
  黄斌（1982—），博士，教授，研究方向油气集输及处理技术，E-mail：2022052@cqust.edu.cn。
- 基金信息：
  
  重庆市自然科学基金创新发展联合基金(2022NSCQ-LZX0205);中石油创新基金(2023DQ02-0203);重庆市教委科学技术研究项目(KJQN202101545);重庆市自然科学基金面上项目(cstc2021jcyj-msxmX0918)
- DOI：10.12434/j.issn.2097-2547.20240032
  中图分类号： TE86
- 纸质出版日期：2024-12-25，
  
  收稿日期：2024-01-25，
  
  修回日期：2024-03-29，
- 稿件说明：
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张威,张伟文,李欣洋等.超重力反应器天然气脱碳过程反应传质智能预测[J].低碳化学与化工,2024,49(12):96-102.

ZHANG Wei,ZHANG Weiwen,LI Xinyang,et al.Intelligent prediction of mass transfer during natural gas decarburization in high gravity reactor[J].Low-carbon Chemistry and Chemical Engineering,2024,49(12):96-102.
张威,张伟文,李欣洋等.超重力反应器天然气脱碳过程反应传质智能预测[J].低碳化学与化工,2024,49(12):96-102. DOI： 10.12434/j.issn.2097-2547.20240032.

ZHANG Wei,ZHANG Weiwen,LI Xinyang,et al.Intelligent prediction of mass transfer during natural gas decarburization in high gravity reactor[J].Low-carbon Chemistry and Chemical Engineering,2024,49(12):96-102. DOI： 10.12434/j.issn.2097-2547.20240032.

摘要

超重力反应器具有优异的传质性能，在天然气脱碳领域具有广阔的应用前景。为了有效预测超重力反应器的天然气脱碳性能，首先搭建了一套超重力反应器天然气脱碳实验系统，考察了超重力因子、吸收液喷淋密度、混合气体积流量和进气组成等运行参数对模拟天然气脱碳效果的影响；然后采用无量纲方法建立了运行参数与传质系数之间的映射关系；最后基于最小二乘支持向量机（LSSVM）算法，建立了智能预测模型。结果表明，超重力因子和吸收液喷淋密度均存在最优值，分别为57.62和2.04 m

/(m

·h)。增大混合气体积流量，CO

脱除效果下降，但传质系数增大。在最优模型参数（[

]

= [13557.2021

9.5876

]

）下，智能预测模型预测结果的决定系数（

）和平均相对误差（

MRE

）分别为0.9519和0.0949，预测结果的相对误差在±20%以内，说明智能预测模型具有较高的准确性。

Abstract

The high-gravity reactor has excellent mass transfer performance and has broad application prospects in the field of natural gas decarbonization. In order to effectively predict the natural gas decarburization performance of the high-gravity reactor

a set of natural gas decarburization experimental system based on high-gravity reactor was built

and the influences of operating parameters (high gravity factor

spray density o

f absorbent

mixture volume flow rate and intake composition

etc) on the CO

removal performance were investigated. Then a dimensionless method was adopted to establish the mapping relationship between operating parameters and mass transfer coefficient. Finally

based on the least square support vector machine (LSSVM) algorithm

the intelligent prediction model was established. The results show that both the high gravity factor and absorbent spray density have optimal values

which are 57.62 and 2.04 m

/(m

·h)

respectively. In addition

it is found that the CO

removal effect decreases while the mass transfer coefficient increases with the increases of mixture volume flow rate. Under the condition of optimal model parameter ([

]

= [13557.2021

9.5876

]

)

the determination coefficient (

) and average relative error (

MRE

) of prediction results of intelligent prediction model are 0.9519 and 0.0949

respectively

and the relative error of prediction results is within ±20%. It demonstrates that the intelligent prediction model has high accuracy.

关键词

超重力反应器天然气脱碳LSSVM算法传质智能预测模型

Keywords

high gravity reactornatural gas decarburizationLSSVM algorithmmass transferintelligent prediction model

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