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Article(id=1295068038181704123, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1295068001842262748, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202506114, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750262400000, receivedDateStr=2025-06-19, revisedDate=1753200000000, revisedDateStr=2025-07-23, acceptedDate=1753632000000, acceptedDateStr=2025-07-28, onlineDate=1786697881502, onlineDateStr=2026-08-14, pubDate=1777046400000, pubDateStr=2026-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1786697881502, onlineIssueDateStr=2026-08-14, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1786697881502, creator=13701087609, updateTime=1786697881502, updator=13701087609, issue=Issue{id=1295068001842262748, tenantId=1146029695717560320, journalId=1210938733613449225, year='2026', volume='55', issue='4', pageStart='1', pageEnd='190', issueExtLink='null', onlineDate='null', pubDate='1777046400000', pubDateStr='2026-04-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1786697872839, creator='13701087609', updateTime=1786698854295, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1295072118417416228, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1295068001842262748, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1295072118417416229, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1295068001842262748, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=53, endPage=61, ext={EN=ArticleExt(id=1295068038504665532, articleId=1295068038181704123, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Carbon emission accounting and analysis for a coal and biomass co-fired power plant coupled with carbon capture and storage, columnId=1211002405299294959, journalTitle=Thermal Power Generation, columnName=Thermal energy science research, runingTitle=null, highlight=null, articleAbstract=
[Objective]

Co-combustion of coal and biomass coupled with CCS technology has negative carbon emission potential, which is one of the important paths to realize the low-carbon transformation of coal power. This study aims to further explore the carbon reduction potential of this technology in enterprise-scale application.

[Methods]

A carbon accounting system is established at the enterprise level. Taking a 350 MW coal and biomass co-combustion plant coupled with CCS technology as the research object, the optimization of the carbon accounting model for the combustion process, desulfurization process, and indirect emission is carried out based on the whole process of “combustion end - CCS end”. The carbon flow analysis of multi-source emissions is carried out to quantitatively evaluate the impacts of biomass type, blending ratio and carbon capture efficiency on carbon emissions.

[Results]

The results show that straw blending has a slightly better emission reduction effect than wood blending. Increasing the blending ratio and carbon capture efficiency will increase the indirect carbon emissions of the CCS system. Under the conditions of less than 20% blending ratio and 80%~100% carbon capture efficiency, increasing the blending ratio of biomass can get more net emission reduction benefits than increasing the carbon capture efficiency. There is a significant parameter coupling effect among biomass type, blending ratio and carbon capture efficiency.

[Conclusion]

The results of the study provide data support and decision-making basis for power generation enterprises to formulate low-carbon transition strategies.

, authors=Shihao ZHANG, Yue ZHAO, Xiaoshan LI, Fan WU, Cong LUO, Liqi ZHANG, authorsList=Shihao ZHANG, Yue ZHAO, Xiaoshan LI, Fan WU, Cong LUO, Liqi ZHANG, authorCompany=null, correspAuthors=Xiaoshan LI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1295068041553924553, articleId=1295068038181704123, tenantId=1146029695717560320, journalId=1210938733613449225, language=CN, title=煤掺烧生物质耦合CCS电厂的碳排放核算与分析, columnId=1211002405437706993, journalTitle=热力发电, columnName=热能科学研究, runingTitle=null, highlight=null, articleAbstract=
【目的】

煤掺烧生物质耦合碳捕集与封存（carbon capture and storage，CCS）技术具有负碳排放潜力，是实现煤电低碳化改造的重要路径之一，本文旨在进一步探究该技术在企业尺度下规模化应用的碳减排潜力。

【方法】

从企业层面建立碳核算体系，以某350 MW煤掺烧生物质耦合CCS技术的电厂为研究对象，基于“燃烧端-CCS端”全流程，开展燃烧过程、脱硫过程、间接排放的碳核算模型优化。综合多源排放碳流分析，定量评估生物质种类、掺烧比例和碳捕集效率等因素对碳排放的影响。

【结果】

结果表明秸秆掺烧较木材掺烧减排效果略优；提升掺烧比例和碳捕集效率均会导致CCS系统间接碳排放量增加；在掺烧比例低于20%和碳捕集效率80%~100%的条件下，增加生物质掺烧比例较提高碳捕集效率可获得更多的净减排收益；生物质种类、掺烧比例和碳捕集效率之间存在显著的参数耦合效应。

【结论】

研究结果可为发电企业制定低碳转型策略提供数据支撑和决策依据。

, authors=张世豪, 赵悦, 李小姗, 邬凡, 罗聪, 张立麒, authorsList=张世豪, 赵悦, 李小姗, 邬凡, 罗聪, 张立麒, authorCompany=null, correspAuthors=李小姗, authorNote=

张世豪（2001），男，硕士研究生，主要研究方向为典型行业碳核算，。

, correspAuthorsNote=
李小姗（1990），女，博士，副教授，主要研究方向为碳捕集与污染物控制，。
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张世豪（2001），男，硕士研究生，主要研究方向为典型行业碳核算，。

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张世豪（2001），男，硕士研究生，主要研究方向为典型行业碳核算，。

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Progress in Energy and Combustion Science, 2003, 29(2): 89-113., articleTitle=Fuel nitrogen conversion in solid fuel fired systems, refAbstract=null), Reference(id=1295068051788026411, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, doi=null, pmid=null, pmcid=null, year=2018, volume=null, issue=null, pageStart=19, pageEnd=22, url=null, language=null, rfNumber=[23], rfOrder=32, authorNames=谭超, journalName=null, refType=null, unstructuredReference=谭超. 燃煤电厂碳排放监测方法研究[D]. 广州：华南理工大学，2018：19-22., articleTitle=燃煤电厂碳排放监测方法研究, refAbstract=null), Reference(id=1295068051846746668, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, doi=null, pmid=null, pmcid=null, year=2018, volume=null, issue=null, pageStart=19, pageEnd=22, url=null, language=null, rfNumber=[23], rfOrder=33, authorNames=TAN Chao, journalName=null, refType=null, unstructuredReference=TAN Chao. Research on carbon emission monitoring methods of coal-fired power plants[D]. Guangzhou: South China University of Technology, 2018: 19-22., articleTitle=Research on carbon emission monitoring methods of coal-fired power plants, refAbstract=null), Reference(id=1295068051909661229, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, doi=null, pmid=null, pmcid=null, year=2022, volume=352, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[24], rfOrder=34, authorNames=LONG J, WU X, YANG Q, journalName=Journal of Cleaner Production, refType=null, unstructuredReference=LONG J, WU X, YANG Q, et al. Tracing energy-water-greenhouse gas nexus in national supply chains: China 2017[J]. Journal of Cleaner Production, 2022, 352: 131586., articleTitle=Tracing energy-water-greenhouse gas nexus in national supply chains: China 2017, refAbstract=null), Reference(id=1295068051980964398, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, doi=null, pmid=null, pmcid=null, year=2024, volume=null, issue=null, pageStart=48, pageEnd=51, url=null, language=null, rfNumber=[25], rfOrder=35, authorNames=王旭涛, journalName=null, refType=null, unstructuredReference=王旭涛. 中国煤电碳减排及低碳转型路径研究[D]. 杭州：浙江大学，2024：48-51., articleTitle=中国煤电碳减排及低碳转型路径研究, refAbstract=null), Reference(id=1295068052064850479, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, doi=null, pmid=null, pmcid=null, year=2024, volume=null, issue=null, pageStart=48, pageEnd=51, url=null, language=null, rfNumber=[25], rfOrder=36, authorNames=WANG Xutao, journalName=null, refType=null, unstructuredReference=WANG Xutao. Research on carbon emission reduction and low-carbon transition path of coal power in China[D]. Hangzhou: Zhejiang University, 2024: 48-51., articleTitle=Research on carbon emission reduction and low-carbon transition path of coal power in China, refAbstract=null), Reference(id=1295068052144542256, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, doi=null, pmid=null, pmcid=null, year=2022, volume=51, issue=4, pageStart=23, pageEnd=29, url=null, language=null, rfNumber=[26], rfOrder=37, authorNames=谭厚章, 杨富鑫, 阮仁晖, journalName=热力发电, refType=null, unstructuredReference=谭厚章，杨富鑫，阮仁晖，等. 掺烧生物质对660 W燃煤机组锅炉影响研究[J]. 热力发电，2022，51（4）：23-29., articleTitle=掺烧生物质对660 W燃煤机组锅炉影响研究, refAbstract=null), Reference(id=1295068052220039729, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, doi=null, pmid=null, pmcid=null, year=2022, volume=51, issue=4, pageStart=23, pageEnd=29, url=null, language=null, rfNumber=[26], rfOrder=38, authorNames=TAN Houzhang, YANG Fuxin, RUAN Renhui, journalName=Thermal Power Generation, refType=null, unstructuredReference=TAN Houzhang, YANG Fuxin, RUAN Renhui, et al. Study on influence of biomass co-firing on a 660 MW pulverized coal fired boiler[J]. Thermal Power Generation, 2022, 51(4): 23-29., articleTitle=Study on influence of biomass co-firing on a 660 MW pulverized coal fired boiler, refAbstract=null), Reference(id=1295068052291342898, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, doi=null, pmid=null, pmcid=null, year=2016, volume=null, issue=null, pageStart=77, pageEnd=79, url=null, language=null, rfNumber=[27], rfOrder=39, authorNames=International Energy Agency, journalName=20 years of carbon capture and storage: accelerating future deployment, refType=null, unstructuredReference=International Energy Agency. 20 years of carbon capture and storage: accelerating future deployment[R/OL]. OECD, 2016: 77-79. https://www.iea.org/reports/20-years-of-carbon-capture-and-storage., articleTitle=null, refAbstract=null)], funds=[Fund(id=1295068047363035657, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, awardId=2021YFF0601003, language=EN, fundingSource=National Key Research and Development Program(2021YFF0601003), fundOrder=null, country=null), Fund(id=1295068047446921738, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, awardId=2021YFF0601003, language=CN, fundingSource=国家重点研发计划项目(2021YFF0601003), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1295068041776222666, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, xref=null, ext=[AuthorCompanyExt(id=1295068041784611275, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, companyId=1295068041776222666, language=EN, country=null, province=null, 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methods, figureFileSmall=qCAhyVih58I6SbfCeegZxg==, figureFileBig=7Y6FrcKLma2ePf1OROhGkQ==, tableContent=null), ArticleFig(id=1295068046230573562, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=CN, label=图3, caption=不同方法得到的燃烧过程碳排放总量, figureFileSmall=qCAhyVih58I6SbfCeegZxg==, figureFileBig=7Y6FrcKLma2ePf1OROhGkQ==, tableContent=null), ArticleFig(id=1295068046293488123, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=EN, label=Fig.4, caption=Annual values of carbon emissions from the desulphurisation process obtained by different methods, figureFileSmall=M/LK169IW+OEjQyS56E4Nw==, figureFileBig=UjFbjLq26KVS+UVrv4IAPA==, tableContent=null), ArticleFig(id=1295068046356402684, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=CN, label=图4, caption=不同方法得到的脱硫过程碳排放年值, figureFileSmall=M/LK169IW+OEjQyS56E4Nw==, figureFileBig=UjFbjLq26KVS+UVrv4IAPA==, tableContent=null), ArticleFig(id=1295068046423511549, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=EN, label=Fig.5, caption=Total carbon emissions and compositions with different technology combinations, figureFileSmall=t39fUqjv3IYWcGENP5QADw==, figureFileBig=NhPpoZ26BnLrUZf/0436IQ==, tableContent=null), ArticleFig(id=1295068046490620414, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=CN, label=图5, caption=不同技术组合下的碳排放总量及组成, figureFileSmall=t39fUqjv3IYWcGENP5QADw==, figureFileBig=NhPpoZ26BnLrUZf/0436IQ==, tableContent=null), ArticleFig(id=1295068046561923583, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=EN, label=Fig.6, caption=Carbon emission reductions from coal and biomass co-firing coupled with CCS technology in 350 MW units, figureFileSmall=PhG+yaWPuWarL0SRnPuZvg==, figureFileBig=2dsEzJZ4gAVNQk11knqYpQ==, tableContent=null), ArticleFig(id=1295068046654198272, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=CN, label=图6, caption=煤掺烧生物质耦合CCS技术应用在350 MW机组可获得的碳减排量, figureFileSmall=PhG+yaWPuWarL0SRnPuZvg==, figureFileBig=2dsEzJZ4gAVNQk11knqYpQ==, tableContent=null), ArticleFig(id=1295068046733890049, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=EN, label=Tab.1, caption=

Proximate and ultimate analysis of fuels

, figureFileSmall=null, figureFileBig=null, tableContent=
燃料工业分析元素分析
war（M）/%war（A）/%war（V）/%war（FC）/%Qnet/（MJ·kg–1）war（C）/%war（H）/%war（O）/%war（N）/%war（S）/%
煤16.005.0428.0550.9124.364.003.7810.080.670.43
秸秆4.1313.5667.7714.5413.738.096.1537.310.700.06
木材35.004.2948.6912.0213.131.733.8724.620.410.08
), ArticleFig(id=1295068046817776130, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=CN, label=表1, caption=

燃料工业分析及元素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
燃料工业分析元素分析
war（M）/%war（A）/%war（V）/%war（FC）/%Qnet/（MJ·kg–1）war（C）/%war（H）/%war（O）/%war（N）/%war（S）/%
煤16.005.0428.0550.9124.364.003.7810.080.670.43
秸秆4.1313.5667.7714.5413.738.096.1537.310.700.06
木材35.004.2948.6912.0213.131.733.8724.620.410.08
), ArticleFig(id=1295068046893273603, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=EN, label=Tab.2, caption=

Methodologies for accounting carbon emissions from desulphurisation processes

, figureFileSmall=null, figureFileBig=null, tableContent=
量化方法计算公式参数说明
碳酸盐法 Es=B×I×TR×44100B为脱硫剂消耗量，t/a；I为脱硫剂中碳酸盐含量；TR为转化率。
石膏法 Es=MD×ω(CaSO4)×44172MD为石膏产生量，t/a；ω（CaSO4）为石膏中CaSO4·2H2O含量。
二氧化硫法 ES=Q×ω(SO2)×η(SO2)1−η(SO2)×4464Q为烟气体积流量，m3/a；ω（SO2）为单位体积烟气中SO2质量，kg/m3；η（SO2）为脱硫效率。
硫平衡法 ES=M×SA×η(SO2)×4432M为燃料消耗量，t/a；SA为燃料含硫量。
), ArticleFig(id=1295068046977159684, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=CN, label=表2, caption=

脱硫过程碳排放核算方法

, figureFileSmall=null, figureFileBig=null, tableContent=
量化方法计算公式参数说明
碳酸盐法 Es=B×I×TR×44100B为脱硫剂消耗量，t/a；I为脱硫剂中碳酸盐含量；TR为转化率。
石膏法 Es=MD×ω(CaSO4)×44172MD为石膏产生量，t/a；ω（CaSO4）为石膏中CaSO4·2H2O含量。
二氧化硫法 ES=Q×ω(SO2)×η(SO2)1−η(SO2)×4464Q为烟气体积流量，m3/a；ω（SO2）为单位体积烟气中SO2质量，kg/m3；η（SO2）为脱硫效率。
硫平衡法 ES=M×SA×η(SO2)×4432M为燃料消耗量，t/a；SA为燃料含硫量。
), ArticleFig(id=1295068047040074245, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=EN, label=Tab.3, caption=

Annual values of discounted carbon emissions from power generation equipment

, figureFileSmall=null, figureFileBig=null, tableContent=
掺烧比例投资费用/（×105元·a–1）部门碳排放强度/（kg·元–1）①[24]碳排放年值/（×104 t）
01 3700.1782.443
5%1 370/3.20.178/0.1712.448
10%1 370/1.80.178/0.1712.446
15%1 370/1.30.178/0.1712.446
20%1 370/1.10.178/0.1712.444
), ArticleFig(id=1295068047098794502, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=CN, label=表3, caption=

发电设备带来的折算碳排放年值

, figureFileSmall=null, figureFileBig=null, tableContent=
掺烧比例投资费用/（×105元·a–1）部门碳排放强度/（kg·元–1）①[24]碳排放年值/（×104 t）
01 3700.1782.443
5%1 370/3.20.178/0.1712.448
10%1 370/1.80.178/0.1712.446
15%1 370/1.30.178/0.1712.446
20%1 370/1.10.178/0.1712.444
), ArticleFig(id=1295068047174291975, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=EN, label=Tab.4, caption=

Annual values of carbon emissions indirectly generated by CCS system

, figureFileSmall=null, figureFileBig=null, tableContent=
捕集效率掺烧比例
05%10%15%20%
80%26.826.326.526.626.8
85%28.427.928.128.328.4
90%30.129.529.729.930.1
95%31.731.131.331.531.7
100%33.332.832.933.133.3
), ArticleFig(id=1295068047245595144, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1295068038181704123, language=CN, label=表4, caption=

由CCS系统带来的间接碳排放年值

, figureFileSmall=null, figureFileBig=null, tableContent=
捕集效率掺烧比例
05%10%15%20%
80%26.826.326.526.626.8
85%28.427.928.128.328.4
90%30.129.529.729.930.1
95%31.731.131.331.531.7
100%33.332.832.933.133.3
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煤掺烧生物质耦合CCS电厂的碳排放核算与分析
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张世豪 , 赵悦 , 李小姗 , 邬凡 , 罗聪 , 张立麒
热力发电 | 热能科学研究 2026,55(4): 53-61
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热力发电 |热能科学研究 2026 , 55 (4) : 53 -61
煤掺烧生物质耦合CCS电厂的碳排放核算与分析
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张世豪 , 赵悦, 李小姗 , 邬凡, 罗聪, 张立麒
作者信息
  • 华中科技大学能源与动力工程学院，湖北 武汉 430074
通讯作者:
李小姗（1990），女，博士，副教授，主要研究方向为碳捕集与污染物控制，。
作者简介:

张世豪（2001），男，硕士研究生，主要研究方向为典型行业碳核算，。

Carbon emission accounting and analysis for a coal and biomass co-fired power plant coupled with carbon capture and storage
Shihao ZHANG , Yue ZHAO, Xiaoshan LI , Fan WU, Cong LUO, Liqi ZHANG
Affiliations
  • School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
出版时间: 2026-04-25 doi: 10.19666/j.rlfd.202506114
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摘要
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【目的】

煤掺烧生物质耦合碳捕集与封存（carbon capture and storage，CCS）技术具有负碳排放潜力，是实现煤电低碳化改造的重要路径之一，本文旨在进一步探究该技术在企业尺度下规模化应用的碳减排潜力。

【方法】

从企业层面建立碳核算体系，以某350 MW煤掺烧生物质耦合CCS技术的电厂为研究对象，基于“燃烧端-CCS端”全流程，开展燃烧过程、脱硫过程、间接排放的碳核算模型优化。综合多源排放碳流分析，定量评估生物质种类、掺烧比例和碳捕集效率等因素对碳排放的影响。

【结果】

结果表明秸秆掺烧较木材掺烧减排效果略优；提升掺烧比例和碳捕集效率均会导致CCS系统间接碳排放量增加；在掺烧比例低于20%和碳捕集效率80%~100%的条件下，增加生物质掺烧比例较提高碳捕集效率可获得更多的净减排收益；生物质种类、掺烧比例和碳捕集效率之间存在显著的参数耦合效应。

【结论】

研究结果可为发电企业制定低碳转型策略提供数据支撑和决策依据。

燃煤  /  生物质  /  发电  /  碳核算  /  碳减排
[Objective]

Co-combustion of coal and biomass coupled with CCS technology has negative carbon emission potential, which is one of the important paths to realize the low-carbon transformation of coal power. This study aims to further explore the carbon reduction potential of this technology in enterprise-scale application.

[Methods]

A carbon accounting system is established at the enterprise level. Taking a 350 MW coal and biomass co-combustion plant coupled with CCS technology as the research object, the optimization of the carbon accounting model for the combustion process, desulfurization process, and indirect emission is carried out based on the whole process of “combustion end - CCS end”. The carbon flow analysis of multi-source emissions is carried out to quantitatively evaluate the impacts of biomass type, blending ratio and carbon capture efficiency on carbon emissions.

[Results]

The results show that straw blending has a slightly better emission reduction effect than wood blending. Increasing the blending ratio and carbon capture efficiency will increase the indirect carbon emissions of the CCS system. Under the conditions of less than 20% blending ratio and 80%~100% carbon capture efficiency, increasing the blending ratio of biomass can get more net emission reduction benefits than increasing the carbon capture efficiency. There is a significant parameter coupling effect among biomass type, blending ratio and carbon capture efficiency.

[Conclusion]

The results of the study provide data support and decision-making basis for power generation enterprises to formulate low-carbon transition strategies.

coal-fired  /  biomass  /  power generation  /  carbon accounting  /  carbon emission reduction
张世豪, 赵悦, 李小姗, 邬凡, 罗聪, 张立麒. 煤掺烧生物质耦合CCS电厂的碳排放核算与分析. 热力发电, 2026 , 55 (4) : 53 -61 . DOI: 10.19666/j.rlfd.202506114
Shihao ZHANG, Yue ZHAO, Xiaoshan LI, Fan WU, Cong LUO, Liqi ZHANG. Carbon emission accounting and analysis for a coal and biomass co-fired power plant coupled with carbon capture and storage[J]. Thermal Power Generation, 2026 , 55 (4) : 53 -61 . DOI: 10.19666/j.rlfd.202506114
正文
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在“双碳”目标背景下，传统燃煤发电因高碳排放等问题面临严峻的环保与减排压力[1-2]。截至2022年底，我国电力行业碳排放约占全社会碳排放的47%，其中燃煤发电占到电力行业的80%以上[3]。近年来，我国相关政策大力推动煤电低碳化改造。2024年6月，国家发展改革委和国家能源局联合印发了《煤电低碳化改造建设行动方案（2024—2027年）》，明确提出生物质掺烧、绿氨掺烧、碳捕集利用与封存等煤电低碳化技术路线[4]，并重点支持多技术耦合的改造项目。其中，煤掺烧生物质耦合碳捕集与封存（carbon capture and storage，CCS）技术因具备负碳排放潜力和高效环保等特性[5-6]，成为煤电低碳化改造的重要路径之一。
国内外对该技术从不同角度进行了碳减排评估。Xie等人[7]基于化学模拟和生命周期评估的比较系统评估框架，比较了不同机组容量下掺烧生物质和CCS技术组合的能源性能和环境影响。Sammarchi等人[8]分析了中国内蒙古现有燃煤电厂CCS和掺烧生物质情景下包括原材料获取和最终处理阶段的生命周期碳排放潜力，计算出发电阶段和全生命周期内的CO2减排总量。Yi等人[9]对英国有无CCS技术的生物质/燃煤发电厂的CO2排放进行生命周期分析，发现发电过程释放的CO2在全生命周期中占主导地位，而来自生物质供应链的CO2排放量相对较少。樊静丽等[10]评估了黑龙江特定秸秆发电厂应用CCS技术后不同运输方案下的全生命周期温室气体排放和环境影响，其中碳减排潜力受生物质运输方式影响较大。
该技术的温室气体减排潜力在很大程度上取决于其部署方案。Yang等人[11]基于综合环境控制模型开发了10个生命周期评估模型，以量化不同生物质掺烧比例（0~100%）下的温室气体减排潜力。Lu等人[12]得到了87.7%捕集效率下实现直接碳排放和生命周期温室气体零排放对应的掺烧比例，分别为20%和35%。López等人[13]认为生物质掺烧比例为40%~50%的300 MW发电厂是经济与风险之间的折衷解决方案。
综上所述，针对煤掺烧生物质耦合CCS技术，现有研究已基于掺烧比例、捕集效率、能源耦合方式等不同技术配置对其全生命周期碳排放与经济可行性的差异进行了系统性评估。然而，这类评估多从技术全生命周期或行业宏观层面出发，从企业层面对该技术在规模化应用中的碳减排潜力的研究较少。现有研究集中于全域掺烧比例，且设定CCS捕集效率在80%~90%。由于生物质掺烧比例低于20%时不会对锅炉机组运行造成明显影响[14]，目前多数发电企业选择低比例生物质掺烧方案，未来再逐步提高掺烧比例。此外，大多数CCS技术评估研究中的碳捕集效率均设定在80%~ 90%[12]，但事实上电厂燃烧后捕集效率已突破99%以上的技术障碍[15]。现阶段20%以下生物质掺烧比例和80%~100%碳捕集效率不同技术组合下的碳减排潜力有待进一步研究。
对此，本文立足于“燃烧端-CCS端”全流程，以350 MW煤掺烧生物质耦合CCS电厂为研究对象，从企业层面构建碳核算模型。基于碳流分析，考虑包含燃烧过程碳排放、脱硫过程碳排放、间接排放的多源排放，定量评估生物质种类、20%以下掺烧比例和不同碳捕集效率对碳排放量和碳减排量的影响。研究结果旨在为发电企业碳排放核算提供参考，明确各环节具体的碳排放量，并为煤掺烧生物质耦合CCS改造路径的优化提供科学依据和数据支撑。
1　碳核算模型及计算方法
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选取某350 MW煤掺烧生物质耦合CCS电厂为研究对象。掺烧方式为直接掺烧，锅炉燃烧方式为四角切圆型，燃烧煤种为神华混煤，选取秸秆、木材2种生物质燃料与煤混燃，燃料的工业分析、元素分析如表1所示。
由于碳排放量核算与电厂运行时长密切相关，结合电厂实际运行数据，设定机组经营期为15年，年平均发电小时数为5 000 h。
1.1　碳核算模型
1.1.1　核算边界和排放源
首先明确核算边界，包括确定核算范围和用途、物理边界和时间边界。对于电厂企业层面的碳核算，从碳流全局出发，将基于煤掺烧生物质耦合CCS技术的电力生产碳排放源分为电厂能量转化、CO2捕集与封存等核心环节，涵盖企业可控的直接排放与关键间接排放。基于电厂对各类温室气体排放源的控制能力以及排放量，设定直接与间接排放的核算边界。
从直接排放源出发，世界气象组织2022年度《温室气体公报》提供的数据显示，电站循环流化床中N2O排放体积分数在0.005%~0.050%，根据文献[16-18]，当生物质燃烧温度低于1 123 K时，N2O带来的温室效应能力至少为CO2的6%，因此由生物质掺烧产生的N2O造成的环境影响不容忽视。从间接排放源考虑，燃煤电厂需要通过新增设备和工艺实现生物质掺烧并加装CCS进行改造。为明确电厂改造前后的差异，在仅针对不同技术条件下的碳排放展开分析时，将设备所导致的碳排放纳入核算范畴。
根据以上分析，结合现有电厂碳核算方法，对以下温室气体排放源进行核算：化石燃料燃烧产生的CO2排放、燃料燃烧产生的N2O等效碳排放、脱硫过程的CO2排放等直接碳排放，外购电力产生的CO2排放、资源设备带来的CO2排放等间接碳排放，如图1所示。
1.1.2　计算框架
在确定排放边界和排放源后，煤掺烧生物质耦合CCS电厂的CO2排放当量EGHG（t/a）可由式（1）计算：
EGHG=(Eff+Es)×(1−ηCCS)−Eb×ηCCS+E(N2O)×GGWP(N2O)+Ee+Ea
式中：Eff为化石燃料燃烧直接产生的年CO2排放量，t/a；Es为脱硫工艺直接产生的年CO2排放量，t/a；ηCCS为碳捕集技术的年平均捕集效率；Eb为生物质燃料燃烧产生的年CO2排放量，t/a；E（N2O）为燃料燃烧直接产生的年N2O排放量，t/a；GGWP（N2O）为N2O相比CO2的全球变暖潜势值，t/t；Ee为外购电力间接产生的年CO2排放量，t/a；Ea为资源设备间接产生的CO2排放量，t/a。
1.2　多源碳排放计算方法
1.2.1　化石燃料燃烧过程
化石燃料燃烧直接产生的CO2排放量Eff计算如下：
Eff=∑j=112∑i=1n(MC,j,i×Car,j,i×OOF,C,j×4412)
式中：MC，j，i为第j月、第i种化石燃料的消耗量，t；Car，j，i为第j月、第i种化石燃料的收到基含碳量，%；OOF，C，j为第j月化石燃料碳氧化率。
1.2.2　燃料燃烧过程N2O排放
N2O作为主要温室气体来源之一，国内煤电企业未正式将其纳入温室气体核算体系，这主要归因于N2O的转化率受温度影响较大，随着温度升高，N2O含量显著降低。为了更全面评估燃煤掺烧生物质发电的减排效果，本文参考赵悦等[19]归纳的适合核算N2O的场景。
炉膛温度低于1 123 K时，燃烧过程中N2O排放量需加以核算，若达到以下条件之一，可适当放宽限值[20-22]：
1）过量空气系数大于1.2；
2）烟气中CO排放量较高，或烟气中O2浓度较高；
3）燃料中氧氮比较高，煤阶较高，生物质含氮量较高。
1.2.3　脱硫过程碳排放
脱硫工艺中常采用碳酸盐作为脱硫剂去除烟气中的酸性气体，在吸收SO2的同时释放一定量CO2。脱硫过程有4种常见的碳核算方法，包括碳酸盐法、二氧化硫法、石膏法、硫平衡法。这4种方法分别从脱硫反应的反应物和生成物出发，根据分子量关系计算CO2排放量[23]，如图2所示。碳酸盐法和石膏法分别基于脱硫装置中反应物脱硫剂、生成物脱硫石膏与CO2存在的分子量关系来计算碳排放量，二氧化硫法和硫平衡法则分别根据初始烟气中实际存在的SO2、燃料含硫量与CO2存在1：1的分子量关系求得脱硫过程碳排放量，其计算机理见反应式（3）、式（4）。
CaCO3+SO2+12H2O→CaSO3⋅12H2O+CO2
2CaCO3⋅12H2O+O2+3H2O→2CaSO3⋅2H2O
基于4种脱硫工艺碳核算方法计算原理，结合燃煤电厂数据的可获取程度，列出脱硫过程碳排放量ES的计算方法，如表2所示。
1.2.4　间接碳排放
基于煤掺烧生物质耦合CCS技术改造的煤电企业在电力生产环节的间接碳排放源包括外购电力产生的碳排放、资源设备间接产生的碳排放。
外购电力间接产生的碳排放Ee计算如下：
Ee=ADe×EFe
式中：ADe为企业的净购入电量，MW·h；EFe为区域电网年平均供电排放因子，t/（MW·h）。
由于生物质与煤粉在燃烧特性上存在显著不同，燃煤电厂进行生物质掺烧改造时，需新增燃料储存、制粉、管道及辅机等配套系统设备。考虑技术可行性、经济性及安全性，不同掺烧方案新增设备各不相同。同时还需要加装碳捕集、运输和封存设备。为评估改造前后由设备变化产生的碳排放，采用基于经济产出的排放因子法计算资源设备产生的间接碳排放Ea：
Ea=ADa×EFa
式中：ADa为设备投资价格，元；EFa为不同部门基于经济产出的温室气体排放因子，t/万元。
排放因子随价格波动而变化，由于本文仅作静态分析，不考虑价格波动情况，参考Long等人[24]提供的中国149个部门基于经济产出的温室气体排放强度取定值。
为区分掺烧比例不同带来的设备投资差异，采用式（7）计算煤电机组掺烧生物质改造投资成本。
ADb,l=ADb,k×(Cl×slCk×sk)m
式中：ADb，k、ADb，l分别指机组k和l的投资成本，元；Ck、Cl分别指机组k和l的装机容量，kW；sk、sl分别指机组k和l对应的生物质掺烧比例；m为比例因子，本文取0.79。
2　结果与分析
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2.1　燃烧过程碳排放量
图3给出了采用3种不同方法得到的燃烧过程碳排放量：方法1使用基于反平衡法得到的碳氧化率计算煤基碳排放和生物质碳排放；方法2采用《IPCC国家温室气体清单指南》给出的碳氧化率推荐值得到煤基碳排放，采用反平衡法得到生物质碳排放；方法3利用数值模拟得到的出口烟气质量流量和CO2质量分数得到碳排放总量，将该方法得到的结果作为真实值。
由图3可以看出：3种方法得到的碳排放总量相差较小；针对秸秆和木材两类生物质掺烧情况，方法2得到的碳排放均最大，这是由于生物质燃料含有较多挥发分和水分，且固定碳含量低于煤炭，容易出现燃烧不充分的情况，导致掺烧后碳氧化率低于推荐值99%。相较于方法1，方法2在纯煤燃烧时的偏差较大，而在掺烧5%~20%生物质时得到的碳排放更接近实际碳排放。
受燃烧稳定性的影响，掺烧木材时3种方法得到的碳排放差异不显著，最大偏差仅为1.76%，因此在掺烧木材的情景下，采用基于反平衡法实测得到的碳氧化率和99%碳氧化率进行计算均能得到较为准确的结果。掺烧秸秆时，由于煤粉碳氧化率表现出更为明显的波动，2种方法得到的碳排放与实际碳排放有一定差别，但采用方法1的误差仍在5%以内。
在控制发电量相同的情况下，随着生物质掺烧比例增加，包括生物质碳排放在内的燃烧过程总排放并未明显减少，且在掺烧高碳含量秸秆时呈现碳排放量先降后升的非线性变化特征。然而，由于秸秆和木材是碳中和燃料，其碳排放可予以豁免。在此核算框架下，当掺烧比例由基准工况提升至20%时，燃烧过程碳排放量从155×104 t/a递减至121×104 t/a。基准工况下掺烧5%生物质可促使燃烧过程碳排放量降低7%，掺烧比例每增加5%，相应产生约7×104 t/a的碳减排量。对比生物质类型对碳排放的影响，秸秆掺烧相较木材掺烧展现出约1%的相对减排优势。
2.2　脱硫过程碳排放量
从脱硫反应的反应物和生成物出发，脱硫过程碳排放的计算方法包括了碳酸盐法、石膏法、硫平衡法、二氧化硫法。由于电厂脱硫剂消耗数据时间跨度较大且现场石膏量收集统计比较困难，基于电厂实际参数和模拟得到的SO2排放数据对比了硫平衡法和二氧化硫法的计算结果，如图4所示。由图4可以看出，随着掺烧比例增加，脱硫过程的碳排放量从0.43×104 t/a逐渐降低至0.36×104 t/a，掺烧5%生物质可促使脱硫过程碳排放量降低4%，秸秆在脱硫过程中的减排效果略优于木材，这是因为含硫量低的生物质代替燃煤使得燃料的总含硫量降低。
硫平衡法和二氧化硫法得到的碳排放差异为0.3%~4%，前者结果略高。硫平衡法燃料中的硫元素完全燃烧生成SO2，因此基于硫平衡法得到的碳排放结果理论上是脱硫过程的最高碳排放量，这也是采用该方法得到的碳排放量较高的原因。二氧化硫法得到的是实际脱硫过程的碳排放量，由于该结果基于模拟得到的SO2排放量计算，受不同掺烧比例下燃烧特性的影响，脱硫过程碳排放速率与掺烧比例呈现非线性变化规律，且掺烧秸秆和木材在不同掺烧比例下的下降幅度较一致。
2.3　间接碳排放量
由于电厂设备消耗的电能绝大部分来自自身发电量，只有在机组启动等较少情况下才会消耗厂外购电，因此一般不考虑外购电力部分的间接碳排放。为评估燃煤电厂掺烧生物质耦合CCS技术改造带来的间接环境影响，对设备以及项目实施过程中带来的碳排放年值进行折算。
根据国家能源局统计数据，2009—2021年间300 MW煤电机组单位投资成本约为4 200元/kW；参考《中国发展生物混燃的市场创新机制和政策法规研究》中的工程案例，25 MW机组掺烧15%生物质改造费用为1.2×107元。基于上述数据，并结合基于经济产出的温室气体排放强度，估算包括生物质掺烧改造在内的发电设备间接带来的碳排放，结果见表3。由表3可以看出：在0~20%掺烧范围内，发电设备间接带来的碳排放量在2.44×104 t/a左右变化；对于350 MW燃煤机组，掺烧生物质必然会导致间接碳排放量增加，但由于耦合生物质技术带来的间接碳排放增量占燃煤发电系统间接碳排放总量的比例始终低于0.2%，表明生物质掺烧设备的附加碳排放对设备间接碳排放评估影响可忽略。
CCS系统的成本与碳捕集吸收剂的选择密切相关。乙醇胺由于成本低、吸收率高、可回收性好，被广泛应用于CCS中试和工业示范工厂[25]，因此选取乙醇胺吸收剂作为基础吸收剂。为了简化计算CCS系统的成本，参考相关案例固定值，即单位成本为800元/（t·a）。选取CCS相关设备制造部门碳排放强度为0.191 kg/元，得到CCS技术改造带来的间接碳排放量，见表4。
在相同捕集效率条件下，生物质掺烧比例的变化将影响CCS系统的间接碳排放水平。由表4可以看出：相比纯煤工况，掺烧5%生物质可使间接碳排放降低约0.5×104 t/a；随着生物质掺烧比例增加，间接碳排放水平呈缓慢上升趋势，增长幅度在（0.1~0.2）×104 t/a。这种关联性主要源于CCS系统运行成本与碳捕集量的正相关关系，当捕集效率保持恒定时，不同生物质掺烧比例将导致系统捕集量产生差异。碳捕集效率对系统间接碳排放量的影响更显著，当捕集效率每提升5%时，系统间接碳排放量增加约1.6×104 t/a。在碳捕集效率为100%的极端条件下，CCS系统间接碳排放量峰值可达33.3×104 t/a，约为发电系统间接碳排放量的13.6倍。
2.4　碳排放总量
通过基于优化后的碳核算模型，得到煤掺烧生物质耦合CCS电厂不同技术组合下的碳排放总量及组成，如图5所示。在本文中，锅炉炉膛温度高于1 300 K[26]，因此无需考虑N2O生成。
在0~20%生物质掺烧比例下，电厂在加装CCS系统后，碳排放总量从（153.7~159.5）×104 t/a锐减至（0.3~60.3）×104 t/a。这表明耦合CCS系统可实现显著减排效果，原因在于捕集系统能使直接碳排放至少降低80%，甚至达到负碳排放。然而CCS设备制造环节引发的上游碳排放使间接碳排放量级扩大约12倍，一定程度上抑制了减排效果，这种耦合效应导致即使在极限碳捕集效率下，系统仍存在（0.3~35.9）×104 t/a的残余碳排放。碳捕集效率提升对电厂减排效益同样呈现双重作用机制。当碳捕集效率从80%提升至85%时，直接碳排放量降低7.7×104 t/a，而间接碳排放量增加1.6×104 t/a。随着捕集效率进一步提高，直接碳排放量逐渐降低并成为负值，实现负排放，间接碳排放增幅保持不变。
在生物质协同减排方面，不同种类生物质的减排效益存在差异。秸秆掺烧较木材掺烧具有更优的全生命周期碳排放表现，其碳排放总量低2%~5%。从掺烧比例对碳排放的影响来看，当掺烧比例从5%提升至20%时，系统直接碳排放呈线性下降趋势，而间接碳排放波动幅度不超过±3.2%。在碳捕集效率为100%、秸秆掺烧比例为20%的极限工况下，碳排放总量约为0.2×104 t/a，较基准燃煤工况下降99.6%，趋近于零排放水平。
2.5　碳减排量
选取纯煤燃烧作为基准工况，得到350 MW机组掺烧生物质并耦合CCS技术的碳减排效应，如图6所示。依据现有技术水平，该机组借助煤掺烧生物质耦合CCS技术，最高每年可减少碳排放量约157×104 t，机组运营期内累积可减少碳排放量达2.36×108 t，相当于全国电力部门年减排目标的1.35%[27]，这对实现“双碳”战略目标具有显著的环境效益。值得注意的是，碳捕集效率、生物质种类与掺烧比例间存在显著的参数耦合效应，不同技术路径可能产生等效减排结果。例如，技术配置1（10%秸秆掺烧比例+95%碳捕集效率）与技术配置2（20%木材掺烧比例+85%碳捕集效率）的碳减排量差值小于0.2%，呈现显著的技术替代特性。
当系统仅采用生物质掺烧技术时，碳减排量为34×104 t/a，而在引入CCS系统后，碳减排量增大至原来的4倍以上。碳减排量与碳捕集效率呈一定的线性关系，在80%~100%捕集效率区间内，碳捕集效率每提升5%能带来6×104 t/a的减排增益。从燃烧侧来看，生物质掺烧比例与捕集效率间存在协同作用机制。当系统捕集效率从80%提升至100%时，生物质掺烧比例每增加5%，秸秆和木材情景下分别能使年均碳排放量减少约8.3×104、7.2×104 t。
综上所述，CCS系统自身带来的间接碳排放削弱了其理论减排潜力，导致在同等技术参数下，提升5%生物质掺烧比例较提高5%碳捕集效率可多获得38%的净减排收益。此外，生物质类型对减碳效益的影响较小，特别是在低掺烧比例下，相同工况下不同生物质燃料的碳减排量差异仅为（0.3~4.7）×104 t/a。
3　结论与展望
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1）建立了燃烧端-CCS端的碳核算模型，优化了燃烧过程、脱硫过程、间接排放的多源碳排放核算方法。推荐企业在燃烧过程中采用反平衡法实测碳氧化率以提升核算精度，脱硫环节根据数据可获得性选择硫平衡法或二氧化硫法，间接排放核算引入基于经济产出的排放因子法。
2）生物质掺烧比例低于20%时，秸秆与木材掺烧的碳排放量相差约1%，脱硫过程中硫平衡法与二氧化硫法的碳排放量差异仅0.3%~4.0%。提升掺烧比例和碳捕集效率均会导致CCS系统间接碳排放量增加。在碳排放总量方面，相较于提高碳捕集效率，增大掺烧比例可获得更多的净减排收益。
3）生物质种类、掺烧比例和碳捕集效率之间存在显著的参数耦合效应，不同技术路径组合可能产生等效减排结果。发电企业可基于减排要求和经济成本综合考量，选择最优的技术组合方案。
4）随着煤掺烧生物质耦合CCS技术的不断发展，基于提出的核算模型，未来可针对更高量级和更低年运行时长的机组进行碳核算，并考虑对经济成本进行动态分析，为不同规模电厂的低碳转型提供更全面的理论支撑。
基金
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  • 国家重点研发计划项目(2021YFF0601003)
文献
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doi: 10.19666/j.rlfd.202506114
  • 接收时间：2025-06-19
  • 首发时间：2026-08-14
  • 出版时间：2026-04-25
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  • 收稿日期：2025-06-19
  • 修回日期：2025-07-23
  • 录用日期：2025-07-28
基金
National Key Research and Development Program(2021YFF0601003)
国家重点研发计划项目(2021YFF0601003)
作者信息
    华中科技大学能源与动力工程学院，湖北 武汉 430074

通讯作者:

李小姗（1990），女，博士，副教授，主要研究方向为碳捕集与污染物控制，。
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https://castjournals.cast.org.cn/joweb/rlfd/CN/10.19666/j.rlfd.202506114
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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