Simulation of a system to simultaneously recover CO2 and sweet carbon-neutral natural gas from wet natural gas: A delve into process inputs and units performances

Abdulhalim Musa Abubakar , Lukman Buba Umdagas , Moses NyoTonglo Arowo , Marwea Al-Hedrewy , Mahlon Kida Marvin , Noureddine Elboughdiri , Aminullah Zakariyya Abdul , Jenisus O. Dejarlo , Rezkallah Chafika
{"title":"Simulation of a system to simultaneously recover CO2 and sweet carbon-neutral natural gas from wet natural gas: A delve into process inputs and units performances","authors":"Abdulhalim Musa Abubakar ,&nbsp;Lukman Buba Umdagas ,&nbsp;Moses NyoTonglo Arowo ,&nbsp;Marwea Al-Hedrewy ,&nbsp;Mahlon Kida Marvin ,&nbsp;Noureddine Elboughdiri ,&nbsp;Aminullah Zakariyya Abdul ,&nbsp;Jenisus O. Dejarlo ,&nbsp;Rezkallah Chafika","doi":"10.1016/j.cles.2024.100156","DOIUrl":null,"url":null,"abstract":"<div><div>The growing need for carbon-neutral energy solutions necessitates the development of efficient systems for carbon dioxide (CO<sub>2</sub>) recovery and the production of sweet carbon-neutral natural gas (CNNG) from wet natural gas. Despite existing approaches, limitations in process optimization, solvent efficiency, and output purity persist. This study aims to address these gaps by simulating a system for simultaneous recovery of CO<sub>2</sub> and CNNG using an integrated three-stage process, modeled in Aspen Plus V8.8. The unique aspect of this work lies in employing the ENRTL-RK base model, coupled with sensitivity analyses to optimize input parameters across 13 interconnected process units, including compressors, heat exchangers, and extraction columns. Key innovations include the novel configuration of units, yielding a recovery efficiency of 95.94% for CNNG and a CO<sub>2</sub> purity of 93.185% at optimal conditions, surpassing conventional methods. The performance of the monoethanolamine (MEA) solvent was enhanced by careful adjustment of input parameters, improving its absorption efficiency by 12% compared to standard operational settings. Sensitivity analysis revealed critical parameters such as feed pressure and solvent flow rate as primary drivers for maximizing output efficiency. This study also provides a detailed quantitative assessment of power requirements, with a compressor brake horsepower (BHP) of 18,2605 watts at 110 bar discharge pressure. It addresses the existing research gap by introducing a systematic approach to process optimization, significantly improving the purity and recovery of CNNG and CO<sub>2</sub> while minimizing energy consumption. The results not only demonstrate the viability of this process but also provide a foundation for further refinement in sustainable gas processing technologies.</div></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cleaner Energy Systems","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772783124000505","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The growing need for carbon-neutral energy solutions necessitates the development of efficient systems for carbon dioxide (CO2) recovery and the production of sweet carbon-neutral natural gas (CNNG) from wet natural gas. Despite existing approaches, limitations in process optimization, solvent efficiency, and output purity persist. This study aims to address these gaps by simulating a system for simultaneous recovery of CO2 and CNNG using an integrated three-stage process, modeled in Aspen Plus V8.8. The unique aspect of this work lies in employing the ENRTL-RK base model, coupled with sensitivity analyses to optimize input parameters across 13 interconnected process units, including compressors, heat exchangers, and extraction columns. Key innovations include the novel configuration of units, yielding a recovery efficiency of 95.94% for CNNG and a CO2 purity of 93.185% at optimal conditions, surpassing conventional methods. The performance of the monoethanolamine (MEA) solvent was enhanced by careful adjustment of input parameters, improving its absorption efficiency by 12% compared to standard operational settings. Sensitivity analysis revealed critical parameters such as feed pressure and solvent flow rate as primary drivers for maximizing output efficiency. This study also provides a detailed quantitative assessment of power requirements, with a compressor brake horsepower (BHP) of 18,2605 watts at 110 bar discharge pressure. It addresses the existing research gap by introducing a systematic approach to process optimization, significantly improving the purity and recovery of CNNG and CO2 while minimizing energy consumption. The results not only demonstrate the viability of this process but also provide a foundation for further refinement in sustainable gas processing technologies.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
模拟从湿天然气中同时回收二氧化碳和甜碳中性天然气的系统:对工艺输入和装置性能的深入研究
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
3.00
自引率
0.00%
发文量
0
期刊最新文献
Simulation of a system to simultaneously recover CO2 and sweet carbon-neutral natural gas from wet natural gas: A delve into process inputs and units performances Optimizing a hybrid wind-solar-biomass system with battery and hydrogen storage using generic algorithm-particle swarm optimization for performance assessment Design and implementation of a control system for multifunctional applications of a Battery Energy Storage System (BESS) in a power system network Optimizing textile dyeing and finishing for improved energy efficiency and sustainability in fleece knitted fabrics Techno economic study of floating solar photovoltaic project in Indonesia using RETscreen
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1