生物柴油冷滤点与其化学组成的定量关系
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  • 英文篇名:Quantitative correlations between biodiesel cold filter plugging point and its chemical composition
  • 作者:袁银男 ; 陈秀 ; 来永斌 ; 吕翠英 ; 崔勇 ; 梅德清 ; 华平 ; 汤艳峰
  • 英文作者:Yuan Yinnan 1 , Chen Xiu 1,2 , Lai Yongbin 1,3 , Lü Cuiying 2 , Cui Yong 4 , Mei Deqing 4 , Hua Ping 5 , Tang Yanfeng 5 (1. School of Mechanical Engineering, Nantong University, Nantong 226019, China; 2. School of Chemical Engineering, Anhui University of Science & Technology, Huainan 232001, China; 3. School of Mechanical Engineering, Anhui University of Science & Technology, Huainan 232001, China; 4. School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China; 5. School of Chemistry and Chemical Engineering, Nantong University, Nantong 226019, China)
  • 关键词:生物柴油 ; 化学分析 ; 低温流动性 ; 回归分析 ; 冷滤点 ; 气相色谱-质谱
  • 英文关键词:biodiesel, chemical analysis, regression analysis, cold flow properties, cold filter plugging point, GC-MS
  • 中文刊名:NYGU
  • 英文刊名:Transactions of the Chinese Society of Agricultural Engineering
  • 机构:南通大学机械工程学院;安徽理工大学化学工程学院;安徽理工大学机械工程学院;江苏大学汽车与交通工程学院;南通大学化学化工学院;
  • 出版日期:2013-09-01
  • 出版单位:农业工程学报
  • 年:2013
  • 期:v.29;No.219
  • 基金:国家自然科学基金资助项目(51076069);; 江苏高校优势学科建设工程资助项目(苏政办发[2011137号)
  • 语种:中文;
  • 页:NYGU201317031
  • 页数:8
  • CN:17
  • ISSN:11-2047/S
  • 分类号:220-227
摘要
生物柴油的低温流动性主要取决于化学组成。为了量化表征生物柴油组成与其冷滤点的关系,采用气相色谱-质谱与冷滤点分析技术和多元线性回归分析方法,分析了生物柴油的脂肪酸甲酯组成和冷滤点,研究了脂肪酸甲酯组成对冷滤点的影响规律。研究表明:生物柴油主要由14~24个偶数碳原子组成的长链脂肪酸甲酯组成,其中饱和脂肪酸甲酯主要为C14:0~C24:0,不饱和脂肪酸甲酯主要为C16:1~C22:1、C18:2~C20:2和C18:3。120种生物柴油油样中,乌桕梓油生物柴油的冷滤点最低,为-14℃,花生油生物柴油的冷滤点最高,为13℃。生物柴油的脂肪酸甲酯的含量与分布不同,冷滤点差异较大。冷滤点随饱和脂肪酸甲酯含量的增加呈线性升高,且碳链长的较短的增加显著;随不饱和脂肪酸甲酯含量的增加而呈线性降低,且不饱和度高的较低的降低略明显。建立了线性相关性非常显著(R=0.971)的基于组成的冷滤点预测模型。研究结果为不同环境下生物柴油的推广应用提供参考。
        Biodiesel has become one of the comparatively ideal partial alternative fuels for diesel engines because of its environmental benefits and the fact that it is a product made from renewable resources. However the less favorable cold flow properties or the low temperature operability of biodiesel fuel compared to conventional diesel is a major drawback limiting its use. The poor flow properties of biodiesel at cold temperatures are mainly due to fatty acid methyl ester composition. In order to quantify the relation between biodiesel composition and its cold filter plugging point (CFPP), fatty acid methyl ester composition, CFPP, and the influence of composition on CFPP were analyzed by gas chromatography-mass spectrometry and a cold filter plugging point test method. Correlation between fatty acid methyl ester composition and CFPP was studied with multivariate linear regression. The study shows that biodiesel is mainly fatty acid methyl ester (FAME) that is composed of 14-24 even number carbon atoms. Saturated fatty acid methyl esters (SFAMEs) are mainly C 14:0 ~C 24:0 and unsaturated fatty acid methyl ester (UFAMEs) are mainly C 16:1 ~C 22:1 , C 18:2 ~C 20:2 and C 18:3 . The cold flow property of biodiesel is mainly determined by the content and distribution of FAME. The CFPP increases linearly with increasing SFAME, and the longer the carbon chains are, the greater the increase will be. In addition, CFPP decreases linearly with the increasing unsaturated fatty acid methyl esters (UFAME), and the higher the degree of unsaturation, the greater the decrease. Among the 120 kinds of biodiesel we studied, the CFPP of sapium sebiferum methyl ester (SSME) was the lowest (-14℃ ) and the CFPP of peanut methyl ester (PNME) was the highest (13℃ ). Considering SFAMEC≤18 , SFAMEC≥20 , mono-unsaturated fatty acid methyl ester (MUFAME) and di-unsaturated fatty acid methyl ester (DUFAME) in biodiesel as independent variables, and CFPP as dependent variable, we built a CFPP quaternary linear regression prediction model. The significance of the linear regression and deviation analysis were both analyzed. The regression correlation coefficient R=0.971 shows that the CFPP of biodiesel has a very significant linear dependence with SFAMEC≤18 , SFAMEC≥20 , MUFAME and DUFAME. The variance analysis F=471.65 and significance F=2.53E-70 show that our regression equation is very significant. The deviation analysis indicates that the regression prediction model has a high accuracy. At a significance level of α=0.05, the deviation between the measured and predicted values of CFPP was ≤3 ℃. The result indicated that the regression model can predict well.
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