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使用响应面法确定理想的藻类生物柴油-柴油混合燃料以改善性能和减少排放的实验研究

Experimental Investigation for Determining an Ideal Algal Biodiesel-Diesel Blend to Improve the Performance and Mitigate Emissions Using a Response Surface Methodology.

作者信息

Ramachandran Elumalai, Krishnaiah Ravi, Venkatesan Elumalai Perumal, Murugan Manickam, Medapati Sreenivasa Reddy, Sekar Prabhakar

机构信息

School of Mechanical Engineering, VIT University, Vellore 632014, India.

Department of Mechanical Engineering, Aditya Engineering College, Surampalem 533437, India.

出版信息

ACS Omega. 2023 Mar 6;8(10):9187-9197. doi: 10.1021/acsomega.2c07104. eCollection 2023 Mar 14.

DOI:10.1021/acsomega.2c07104
PMID:36936311
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10018500/
Abstract

The ongoing depletion of the world's fossil fuel sources and environmental damage has compelled the quest for alternative energy. Excellent characteristics of biodiesel include its renewable nature, safety, absence of sulfur, environmental advantages, and biodegradability, which can eradicate the above problems. In this study, algal oil was characterized to obtain the fatty acid profile, and the free fatty acid value of algal oil suggested a two-step process of esterification and transesterification for efficient biodiesel production. The performance and emission results of biodiesel and its blends (B10, B20, and B30) were investigated in a constant speed, single-cylinder, 4-stroke, 3.5 kW compression ignition engine at different loads for arriving at an appropriate fuel blend ratio. The response surface methodology technique is used to predict the ideal composition of microalgae-diesel using the experimental data with due weightage for the optimization criterion. The predicted blend ratio of B25 was tested on the engine and authenticated. The findings recorded an improvement in brake thermal efficiency to 31.42% and reduction in brake specific energy consumption to 9.82 MJ/kW h, unburned hydrocarbon to 85 ppm, carbon monoxide to 0.164% v/v, carbon dioxide to 4.115% v/v, nitrogen oxides to 691 ppm, and smoke opacity to 16.93%.

摘要

世界化石燃料资源的持续枯竭和环境破坏促使人们寻求替代能源。生物柴油的优良特性包括可再生性、安全性、无硫、环境优势和生物降解性,这些特性可以消除上述问题。在本研究中,对藻油进行了表征以获得脂肪酸谱,藻油的游离脂肪酸值表明,为高效生产生物柴油,需采用酯化和酯交换两步法。在一台恒速、单缸、四冲程、3.5千瓦的压缩点火发动机上,在不同负荷下研究了生物柴油及其混合燃料(B10、B20和B30)的性能和排放结果,以得出合适的燃料混合比。响应面法技术用于利用实验数据预测微藻柴油的理想组成,并适当考虑优化标准。对预测的B25混合比在发动机上进行了测试并得到验证。结果表明,制动热效率提高到31.42%,制动比能耗降低到9.82兆焦/千瓦时,未燃碳氢化合物降低到85 ppm,一氧化碳降低到0.164% v/v,二氧化碳降低到4.115% v/v,氮氧化物降低到691 ppm,烟度降低到16.93%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/c0acadf4d4d8/ao2c07104_0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/cac1e24ad765/ao2c07104_0002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/93b053b3d923/ao2c07104_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/f3f9a8197c14/ao2c07104_0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/2edb196cee34/ao2c07104_0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/60ed5fc7cbd3/ao2c07104_0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/c0acadf4d4d8/ao2c07104_0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/cac1e24ad765/ao2c07104_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/1c9961a15151/ao2c07104_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/6c0d60153dfc/ao2c07104_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/95541413d859/ao2c07104_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/93b053b3d923/ao2c07104_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/f3f9a8197c14/ao2c07104_0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/2edb196cee34/ao2c07104_0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/60ed5fc7cbd3/ao2c07104_0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22b7/10018500/c0acadf4d4d8/ao2c07104_0010.jpg

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本文引用的文献

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Factors Affecting Microalgae Production for Biofuels and the Potentials of Chemometric Methods in Assessing and Optimizing Productivity.影响生物燃料生产的微藻因素及化学计量学方法在评估和优化生产力方面的潜力。
Cells. 2019 Aug 7;8(8):851. doi: 10.3390/cells8080851.
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Chemometric modeling of thermogravimetric data for the compositional analysis of forest biomass.用于森林生物质成分分析的热重数据化学计量学建模
PLoS One. 2017 Mar 2;12(3):e0172999. doi: 10.1371/journal.pone.0172999. eCollection 2017.