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离子液体催化麻疯树籽油制备生物柴油工艺 被引量:1

Process of synthesis of biodiesel from jatropha curcas seed oil catalyzed by ionic liquids
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摘要 离子液体作为绿色液体催化剂具有优良的性能而倍受关注,然而关于离子液体催化合成生物柴油的报道很少。文中以N-甲基咪唑、吡啶为原料,合成N-(4-磺酸基)苄基吡啶硫酸氢根盐离子液体,其结构经IR,1HNMR所证实;以该离子液体为催化剂,催化麻疯树籽油合成生物柴油。在单因子实验基础上,通过正交试验法对反应温度、催化剂的用量、反应时间和醇油摩尔比等影响合成生物柴油的因素进行了优化,同时也对离子液体的稳定性进行了检验。实验结果表明:在反应温度140℃、醇油摩尔比为15∶1、反应时间6 h和催化剂用量为油质量的5%工艺条件下,生物柴油产率可达89.9%,且离子液体的稳定性好,可循环使用5次。此方法制备的生物柴油的主要质量指标与国内外生物柴油生产标准接近。 Ionic liquids are emerging as environmental-friendly catalysts with controllable framework and extraordinary properties that attract more attention,but few reports of which are available for the applications of ionic liquids as promising catalysts for the production of biodiesel. N-( 4-sulfonic)-benzyl-pyridium hydrogen sulfate with an alkanesulfonic acid group was synthesized with N-methylimidazole and pyridine as starting materials and characterized by IR and^1 HNMR. Subsequently,the synthesis of biodiesel from jatropha curcas seed oil catalyzed by this ionic liquid was studied. The main factors such as ionic liquid dosage,molar ratio of methanol to jatropha curcas seed oil, reaction temperature and reaction time which affected the transesterification reaction were investigated by single factor experiment. On that basis,the optimal extracting conditions affecting the synthesis of biodiesel was researched by the orthogonal test. It shows that the optimum conditions of the synthesis of biodiesel are as follows: molar ratio of methanol to oil 15∶ 1,reaction temperature 140 ℃,reaction time 6 h,dosage of ionic liquid equal to 5% of the oil mass. Under these optimal conditions,the biodiesel yield can reach 89. 9%. The ionic liquid has no obvious change in catalytic activity after being used 5 times. The performances of biodiesel are comparable to the ASTM standards in America and those in China.
出处 《化学工程》 CAS CSCD 北大核心 2014年第9期16-20,30,共6页 Chemical Engineering(China)
基金 广西自然科学基金资助项目(2012GXNSFAA053036)
关键词 离子液体 麻疯树籽油 生物柴油 正交试验优化 ionic liquid jatropha curcas seed oil biodiesel orthogonal experiment optimization
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