农业工程学报
農業工程學報
농업공정학보
2013年
17期
212-219
,共8页
袁银男%陈秀%来永斌%吕翠英%崔勇%梅德清%华平%汤艳峰
袁銀男%陳秀%來永斌%呂翠英%崔勇%梅德清%華平%湯豔峰
원은남%진수%래영빈%려취영%최용%매덕청%화평%탕염봉
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
生物柴油%化學分析%低溫流動性%迴歸分析%冷濾點%氣相色譜-質譜
생물시유%화학분석%저온류동성%회귀분석%랭려점%기상색보-질보
biodiesel%chemical analysis%regression analysis%cold flow properties%cold filter plugging point%GC-MS
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。
生物柴油的低溫流動性主要取決于化學組成。為瞭量化錶徵生物柴油組成與其冷濾點的關繫,採用氣相色譜-質譜與冷濾點分析技術和多元線性迴歸分析方法,分析瞭生物柴油的脂肪痠甲酯組成和冷濾點,研究瞭脂肪痠甲酯組成對冷濾點的影響規律。研究錶明:生物柴油主要由14~24箇偶數碳原子組成的長鏈脂肪痠甲酯組成,其中飽和脂肪痠甲酯主要為 C14:0~C24:0,不飽和脂肪痠甲酯主要為 C16:1~C22:1、C18:2~C20:2和 C18:3。120種生物柴油油樣中,烏桕梓油生物柴油的冷濾點最低,為-14℃,花生油生物柴油的冷濾點最高,為13℃。生物柴油的脂肪痠甲酯的含量與分佈不同,冷濾點差異較大。冷濾點隨飽和脂肪痠甲酯含量的增加呈線性升高,且碳鏈長的較短的增加顯著;隨不飽和脂肪痠甲酯含量的增加而呈線性降低,且不飽和度高的較低的降低略明顯。建立瞭線性相關性非常顯著(R=0.971)的基于組成的冷濾點預測模型。研究結果為不同環境下生物柴油的推廣應用提供參攷。
생물시유적저온류동성주요취결우화학조성。위료양화표정생물시유조성여기랭려점적관계,채용기상색보-질보여랭려점분석기술화다원선성회귀분석방법,분석료생물시유적지방산갑지조성화랭려점,연구료지방산갑지조성대랭려점적영향규률。연구표명:생물시유주요유14~24개우수탄원자조성적장련지방산갑지조성,기중포화지방산갑지주요위 C14:0~C24:0,불포화지방산갑지주요위 C16:1~C22:1、C18:2~C20:2화 C18:3。120충생물시유유양중,오구재유생물시유적랭려점최저,위-14℃,화생유생물시유적랭려점최고,위13℃。생물시유적지방산갑지적함량여분포불동,랭려점차이교대。랭려점수포화지방산갑지함량적증가정선성승고,차탄련장적교단적증가현저;수불포화지방산갑지함량적증가이정선성강저,차불포화도고적교저적강저략명현。건립료선성상관성비상현저(R=0.971)적기우조성적랭려점예측모형。연구결과위불동배경하생물시유적추엄응용제공삼고。
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 C14:0~C24:0 and unsaturated fatty acid methyl ester (UFAMEs) are mainly C16:1~C22:1, C18:2~C20:2 and C18:3. 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℃). <br> 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 ofα=0.05, the deviation between the measured and predicted values of CFPP was≤3℃. The result indicated that the regression model can predict well.