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Co-immobilization of Rhizomucor miehei lipase and Candida antarctica lipase B and optimization of biocatalytic biodiesel production from palm oil using response surface methodology

机译:响应面分析法共固定米氏根瘤菌脂肪酶和南极假丝酵母脂肪酶B的固定化以及棕榈油生物催化生物柴油生产的优化

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Lipases from Candida antarctica B (nonspecific lipase) and Rhizomucor miehei (1,3-specific lipase) were simultaneously immobilized on epoxy functionalized silica gel under mild conditions. The results showed rapid and simple immobilization of 4-15 mg of CALB:RML (different ratios 4:1, 2:1, 1.5:1, 1:1) on 1 g of support after 6 h. The thermal stability of derivatives and also their stability in methanol were greatly improved compared to the single immobilized enzyme. All the derivatives were also used to catalyze the transesterification of palm oil with methanol to produce fatty acid methyl esters (FAMEs). Response surface methodology (RSM) and a central composite rotatable design (CCRD) was used to study the effects of five factors, reaction temperature, methanol/oil ratio, reaction time, t-butanol concentration and CALB:RML ratio on the fatty acid methyl esters (FAME) yield. A quadratic polynomial equation was obtained for methanolysis reaction by multiple regression analysis. The optimum combinations for the reaction were CALB:RML ratio (2.5:1), t-butanol to oil (39.9 wt%), temperature (35.6 degrees C), methanol:oil ratio (5.9), reaction time 33.5 h. FAME yield of 78.3.5%, which was very close to the predicted value of 75.2%, was obtained. Verification experiment confirmed the validity of the predicted model. (C) 2019 Elsevier Ltd. All rights reserved.
机译:在温和条件下,将南极假丝酵母B(非特异性脂肪酶)和Mhihi根瘤菌(1,3-特异性脂肪酶)的脂肪酶同时固定在环氧官能化硅胶上。结果显示6小时后,将4-15 mg的CALB:RML(不同比例4:1、2:1、1.5:1、1:1)快速简单地固定在1 g载体上。与单一固定化酶相比,衍生物的热稳定性及其在甲醇中的稳定性大大提高。所有的衍生物还被用来催化棕榈油与甲醇的酯交换反应,从而生成脂肪酸甲酯(FAME)。使用响应表面方法(RSM)和中央复合可旋转设计(CCRD)研究了五个因素,反应温度,甲醇/油比,反应时间,叔丁醇浓度和CALB:RML比对脂肪酸甲基的影响酯(FAME)的收率。通过多元回归分析获得了用于甲醇分解反应的二次多项式方程。该反应的最佳组合是CALB:RML比(2.5:1),叔丁醇与油(39.9 wt%),温度(35.6摄氏度),甲醇:油比(5.9),反应时间33.5小时。获得的FAME产率为78.3.5%,非常接近预测值75.2%。验证实验证实了预测模型的有效性。 (C)2019 Elsevier Ltd.保留所有权利。

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