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Life cycle assessment and multi-objective optimization of utility system of biomass gasification process for
α -olefins production- Low-carbon Chemistry and Chemical Engineering Vol. 50, Issue 1, Pages: 65-75(2025)
- 作者机构：
1.华东理工大学 化学工程联合国家重点实验室，上海 200237
2.华东理工大学 能源化工过程智能制造 教育部重点实验室，上海 200237
3.郑州大学 先进功能材料制造教育部工程研究中心，河南 郑州 450001
- 作者简介：
- 基金信息：
DOI：10.12434/j.issn.2097-2547.20240159
CLC： TQ221.2;X828Published：25 January 2025，
Received：16 April 2024，
Revised：25 June 2024，
- 稿件说明：
移动端阅览
XI CHUANDONG, YANG ZIXU, CAO CHENXI, et al. Life cycle assessment and multi-objective optimization of utility system of biomass gasification process for
α -olefins production. [J]. Low-carbon chemistry and chemical engineering, 2025, 50(1): 65-75.XI CHUANDONG, YANG ZIXU, CAO CHENXI, et al. Life cycle assessment and multi-objective optimization of utility system of biomass gasification process for
α -olefins production. [J]. Low-carbon chemistry and chemical engineering, 2025, 50(1): 65-75. DOI： 10.12434/j.issn.2097-2547.20240159.
摘要
公用工程优化在节能减排中具有重要作用。为了有效提升生物质气化生产
α
-烯烃工艺的经济效益和环境绩效，采用非支配排序遗传算法II（NSGA-II），以经济成本和环境影响最小为目标，构建了生命周期公用工程系统多目标优化模型。分别选择煤、天然气和不凝气作为系统燃料，同时引入脱硫脱硝过程以满足SO
2
和NO
x
的排放标准。对比了优化前后工艺全流程的生命周期评价结果，验证了该优化方法的有效性。结果表明，在成本优先的情况下，选择煤和不凝气作为系统燃料能够获得较优的解决方案，其经济成本为115.56 CNY/h。在环境优先的情况下，选择天然气和不凝气作为系统燃料更具优势，其环境影响评价为3.92 × 10
-12
h
-1
。经过多目标优化（优化方案2B），工艺全流程的全球变暖潜力降低了24.54%，酸化潜力降低了30.55%，富营养化降低了4.20%，颗粒物降低了71.03%，其他环境影响类别评价亦有所改善。这验证了本文优化方法在促进
α
-烯烃生产工艺向可持续发展转型中的应用潜力。
Abstract
Utility optimization plays a crucial role in energy conservation and emission reduction. To effectively enhance the economic and environmental performance of the biomass gasification process for
α
-olefin production
a multi-objective optimization model for the life cycle utility system was constructed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II)
targeting minimal economic cost and environmental impact. Coal
natural gas
and non-condensable gas were selected as system fuels
with desulfurization and denitrification processes introduced to meet SO
2
and NO
x
emission standards. The life cycle assessment results before and after optimization were compared to verify the effectiveness of the optimization method. The results indicate that
under cost-prioritized conditions
selecting coal and non-condensable gas as system fuels provides a more optimal solution
with an economic cost of 115.56 CNY/h. Under environment-prioritized conditions
selecting natural gas and non-condensable gas as system fuels is more advantageous
with an environmental impact assessment of 3.92 × 10
-12
h
-1
. After
multi-objective optimization (optimized scheme 2B)
the global warming potential of the entire process
acidification potential
eutrophication potential and particulate matter are reduced by 24.54%
30.55%
4.20% and 71.03%
respectively
and improvements also observe in the assessments of other environmental impact categories. This validates the potential application of the optimization method in promoting the sustainable development transition of the
α
-olefin production process.
关键词
生命周期评价生物质气化α-烯烃生产工艺公用工程多目标优化
Keywords
life cycle assessmentbiomass gasificationα-olefin production processutility systemmulti-objective optimization
references
HEIDENREICH S, FOSCOLO P U. New concepts in biomass gasification [J]. Progress in Energy and Combustion Science, 2015, 46: 72-95.
KAEWLUAN S, PIPATMANOMAI S. Potential of synthesis gas production from rubber wood chip gasification in a bubbling fluidised bed gasifier [J]. Energy Conversion and Management, 2011, 52(1): 75-84.
BAKSHI B R, GHOSH T, LEE K. Engineering, markets, and human behavior: An essential integration for decisions toward sustainability [J]. Current Opinion in Chemical Engineering, 2019, 26: 164-169.
LIPTOW C, TILLMAN A M, JANSSEN M. Life cycle assessment of biomass-based ethylene production in Sweden is gasification or fermentation the environmentally preferable route? [J]. The International Journal of Life Cycle Assessment, 2015, 20(5): 632-644.
JIANG J R, RONG B G, FENG X. Olefin production via methanol integrated with light hydrocarbon conversion: Novel process designs, techno-economic analysis, and environmental analysis [J]. Industrial & Engineering Chemistry Research, 2023, 62(37): 15036-15050.
WILLIAMS H P. The reformulation of two mixed integer programming problems [J]. Mathematical Programming, 1978, 14(1): 325-331.
FLOUDAS C A, GROSSMANN I E. Synthesis of flexible heat exchanger networks for multiperiod operation [J]. Computers & Chemical Engineering, 1986, 10(2): 153-168.
THIRUNAVUKKARASU M, SAWLE Y, LALA H. A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques [J]. Renewable and Sustainable Energy Reviews, 2023, 176: 113192.
ALZAHRANI A M, ZOHDY M, YAN B. An overview of optimization approaches for operation of hybrid distributed energy systems with photovoltaic and diesel turbine generator [J]. Electric Power Systems Research, 2021, 191: 106877.
AKYOL S, ALATAS B. Plant intelligence based metaheuristic optimization algorithms [J]. Artificial Intelligence Review, 2016, 47(4): 417-462.
DEB K. Multi-objective optimization using evolutionary algorithms [M]. New York: John Wiley & Sons, 2001.
杨路, 刘硕士, 罗小艳, 等. MTO烯烃分离过程的多目标操作优化[J]. 化工学报, 2020, 71(10): 4720-4732.
YANG L, LIU S S, LUO X Y, et al. Multi-objective operation optimization of olefin separation process for MTO plant [J]. CIESC Journal, 2020, 71(10): 4720-4732.
XI C D, FU K H, CAO C X, et al. Production of α-olefins from biomass gasification: Process development and multi-objective optimization for techno-economic and environmental goals [J]. Carbon Capture Science & Technology, 2024, 11: 100203.
FENG X D, LIU S J, YUE K, et al. Insight into the promotional effect of Mn-modified nitrogenous biochar on the NH3-SCR denitrification activity at low temperatures [J]. Energy, 2023, 285: 129323.
XU X J, WU Y N, XIAO Q Y, et al. Simultaneous removal of NOx and SO2 from flue gas in an integrated FGD-CABR system by sulfur cycling-mediated Fe(II)EDTA regeneration [J]. Environmental Research, 2022, 205: 112541.
WANG Q F, WANG D, LI Z G, et al. Utilization of desulfurization gypsum potentially impairs the efforts for reducing Hg emissions from coal-fired power plants in China [J]. Fuel, 2022, 312: 122898.
CÓRDOBA P, LI B, LI J, et al. Behaviour and speciation of inorganic trace pollutants in a coal-fired power plant equipped with DENOX-SCR-ESP-NH3FGD controls [J]. Fuel, 2021, 289: 119927.
CAI R, KE X, HUANG Y, et al. Applications of ultrafine limestone sorbents for the desulfurization process in CFB boilers [J]. Environmental Science & Technology, 2019, 53(22): 13514-13523.
殷风光, 时轩. 湿法烟气脱硫装置运行中影响脱硫效率的因素分析[C]//北京能源与环境学会. 2018清洁高效燃煤发电技术交流研讨会论文集. 2018: 5.
YIN F G, SHI X. Factors affecting desulfurization efficiency in the operation of wet flue gas desulfurization units [C]//Beijing Energy and Environment Society. Proceedings of the 2018 Symposium on Clean and Efficient Coal-Fired Power Generation Technology. 2018: 5.
KURUPPU U, RAHMAN A, SATHASIVAN A. Enhanced denitrification by design modifications to the standard permeable pavement structure [J]. Journal of Cleaner Production, 2019, 237: 117721.
BEERBAUM D, BERNHARDT D, BECKMANN M. Modeling of the SNCR process—Based on a semianalytical spray model approach [J]. Thermal Science and Engineering Progress, 2023, 43: 102008.
YANG G, LUO X, LIU S, et al. Modulating active oxygen species on α-MnO2 with K and Pb for SCR of NO at low temperatures [J]. Catalysis Science & Technology, 2023, 13(23): 6685-6698.
NGUYEN T D B, LIM Y I, EOM W H, et al. Experiment and CFD simulation of hybrid SNCR-SCR using urea solution in a pilot-scale reactor [J]. Computers & Chemical Engineering, 2010, 34(10): 1580-1589.
张弛. 烟气脱硝中尿素法制氨工艺比较[J]. 化学工业与工程技术, 2014, 35(6): 35-39.
ZHANG C. Comparison on technologies of preparing ammonia by urea in flue gas denitration [J]. Energy Chemical Industry, 2014, 35(6): 35-39.
谭青, 冯雅晨. 我国烟气脱硝行业现状与前景及SCR脱硝催化剂的研究进展 [J]. 化工进展, 2011, 30(S1): 709-713.
TAN Q, FENG Y C. Present status and perspective of Chinese fuel gas denitration industry and research progress of SCR catalysts [J]. Chemical Industry and Engineering Progress, 2011, 30(S1): 709-713.
WANG Z, PENG X Y, CAO S X, et al. NOx emission prediction using a lightweight convolutional neural network for cleaner production in a down-fired boiler [J]. Journal of Cleaner Production, 2023, 389: 136060.
奚传栋. 生物质基线性α-烯烃生产工艺的多目标优化[D]. 上海: 华东理工大学, 2024.
XI C D. Multi-objective optimization of biomass-based α-olefin production process [D]. Shanghai: East China University of Science and Technology, 2024
亿科环境. 中国生命周期基础数据库[DB/OL]. http://www.ike-global.com/#/products-2/chinese-lca-database-clcdhttp://www.ike-global.com/#/products-2/chinese-lca-database-clcd.
IKE Environmental. Chinese Life Cycle Database [DB/OL]. http://www.ike-global.com/#/products-2/chinese-lca-database-clcdhttp://www.ike-global.com/#/products-2/chinese-lca-database-clcd.
DREYER L C, NIEMANN A L, HAUSCHILD M Z. Comparison of three different LCIA methods: EDIP97, CML2001 and eco-indicator 99 [J]. The International Journal of Life Cycle Assessment, 2003, 8(4): 191-200.
ISO.Environmental management: Life cycle assessment: Requirements and guidelines: ISO 14044: 2006 [S]. Genève: ISO, 2006.
LI H X, ZHAO L. Life cycle assessment and multi-objective optimization for industrial utility systems [J]. Energy, 2023, 280: 128213.
DEHGHANI Z, RAHIMPOUR M R, SHARIATI A. Simulation and multi-objective optimization of a radial flow gas-cooled membrane reactor, considering reduction of CO2 emissions in methanol synthesis [J]. Journal of Environmental Chemical Engineering, 2021, 9(2): 104910.
LU J W, WANG Q, ZHANG Z X, et al. Surrogate modeling-based multi-objective optimization for the integrated distillation processes [J]. Chemical Engineering and Processing-Process Intensification, 2021, 159: 108224.
SU Y, JIN S M, ZHANG X P, et al. Stakeholder-oriented multi-objective process optimization based on an improved genetic algorithm [J]. Computers & Chemical Engineering, 2020, 132: 106618.
THAFSEER M, AL ANI Z, GUJARATHI A M, et al. Towards process, environment and economic based criteria for multi-objective optimization of industrial acid gas removal process [J]. Journal of Natural Gas Science and Engineering, 2021, 88: 103800.
ZHANG Z Y, CHENG X Q, XING Z Y, et al. Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-II algorithms [J]. Chaos, Solitons & Fractals, 2023, 176: 114183.
CHEN M R, ZENG G Q, LU K D. Constrained multi-objective population extremal optimization based economic-emission dispatch incorporating renewable energy resources [J]. Renewable Energy, 2019, 143: 277-294.
DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
HUIJBREGTS M A J, STEINMANN Z J N, ELSHOUT P M F, et al. ReCiPe2016: A harmonised life cycle impact assessment method at midpoint and endpoint level [J]. The International Journal of Life Cycle Assessment, 2016, 22(2): 138-147.
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