压缩点火发动机用芥菜油优化生产生物柴油

J. Godwin, H. Venkatesan, S. Seralathan, Premkumar T Micha
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引用次数: 0

摘要

随着化石燃料的日益枯竭,生物柴油是替代传统柴油的发展趋势。选择芥菜作为燃料来源,因为可获得性是燃料成本的主要考虑因素之一。印度贡献了世界上27%的芥菜种子。在从种子中提取油时,发现FFA值为$ gt $ 2 %,因此采用了两段反式酯化过程。研究了反式酯化反应的工艺参数,确定了最佳反应值。实验结果表明,在摩尔比0.91 v/v、催化剂浓度0.94 wt %、反应时间120 min的条件下,生物柴油收率可达98.1%。为了验证结果,利用人工神经网络(ANN)模型结合贝叶斯正则化算法对工艺参数进行优化。通过性能评价对模型进行了验证。通过对工艺参数的预测,发现人工神经网络的预测值与实验值具有较好的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized Biodiesel Production from Crotalaria juncea L. oil for usage in Compression Ignition Engine
Biodiesel being the trend in replacing the conventional diesel fuel in considerate with the depletion of fossil fuel. Crotalaria juncea L. were selected as the source because availability is one of the major considerations for the fuel cost. India contributes 27% of Crotalaria juncea L. seeds among the world population. On extracting the oil from the seeds the FFA value was found to be $\gt 2$% hence the two stage trans-esterification process was incorporated. The process parameters for the trans-esterification process were investigated for the optimum reaction values. On experimentation it was detailed that the molar ratio of 0.91 v/v, catalyst concentration of 0.94 wt % and reaction time of 120 min were the optimal process parameter values which gave the maximum biodiesel yield of 98.1 %. To validate the result, Bayesian regularized algorithm incorporated in Artificial Neural Network (ANN) model was utilized for the optimization of process parameters. The model was validated with performance evaluation. After predicting the process parameters it was found that the ANN predicted values were in correlation with experimental values.
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