生物柴油
超临界流体
甲醇
生物柴油生产
产量(工程)
生物燃料
制浆造纸工业
螺旋(铁路)
乙醇
材料科学
化学
数学
催化作用
废物管理
有机化学
工程类
复合材料
数学分析
作者
Obie Farobie,Nur Hasanah,Yukihiko Matsumura
出处
期刊:Procedia environmental sciences
[Elsevier]
日期:2015-01-01
卷期号:28: 214-223
被引量:43
标识
DOI:10.1016/j.proenv.2015.07.028
摘要
Non-catalytic biodiesel production in supercritical methanol (SCM) and supercritical ethanol (SCE) was conducted using spiral reactor. The experimental data were used to create artificial neural network (ANN) model in order to predict biodiesel yield. The results showed that ANN was the powerful tool to estimate biodiesel yield that was proven by a high value (0.9980 and 0.9987 in SCM and SCE, respectively) of R and a low value (2.72×10-5, 1.68×10-3, and 2.30×10-3 in SCM and 2.24×10-4, 4.49×10-4, and 5.03×10-4 in SCE for training, validation, and testing, respectively) of mean squared error (MSE). For biodiesel production in SCM, the highest yield of biodiesel was determined of 1.01 mol/mol corresponding to the actual biodiesel yield of 1.00 mol/mol achieved at 350 °C, 20 MPa within 10 min; whereas, for SCE, the highest yield of biodiesel was observed of 0.97 mol/mol corresponding to the actual biodiesel yield of 0.96 mol/mol achieved at 400 °C, 20 MPa within 25 min.
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