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Performance and evaluation of calcined limestone as catalyst in biodiesel production from high viscous nonedible oil

机译:高粘性非食用油生物柴油生产中煅烧石灰石作为催化剂的性能和评价

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Biodiesel production by transesterification of rubber seed oil (RSO) using calcium oxide (CaO) derived from calcined limestone as a heterogeneous catalyst is presented in this study. Optimization of process parameters affecting the conversion of RSO to biodiesel is done by design of experiments (DOE) and an effective comparison of two different optimization methods, namely, response surface methodology (RSM) and artificial neural networks (ANN) is presented. A high conversion of 95.2% was obtained at 12:1 methanol: Oil molar ratio, 4 (wt%) catalyst and 5 hr of reaction time. The proposed design model of RSM is found to fit well with the predicted conversion and with molar ratio and reaction time as the significant process parameters affecting the conversion. Best validation performance of 8.8991 occurred at epoch 4 with a mean square error (MSE) of 1.55 in ANN model trained with Levenberg-Marquardt algorithm. By comparing the predicted coefficient of determination, R~2, values of 0.8452 obtained by using RSM, and 0.9939 obtained by using ANN for biodiesel conversion, it is concluded that ANN model is the best model for predicting the percentage conversion of RSO to biodiesel with minimum error between experimental and predicted values.
机译:本研究介绍了衍生自煅烧石灰石的氧化钙(CaO)作为非均相催化剂的橡胶籽油(RSO)的橡胶籽油(RSO)的生物柴油生产。通过设计实验(DOE)的设计和两种不同优化方法的有效比较来完成影响RSO转换为生物柴油的过程参数的优化,即呈现了两种不同优化方法的有效比较,即响应面方法(RSM)和人工神经网络(ANN)。在12:1甲醇:油摩尔比,4(wt%)催化剂和5小时的反应时间中获得高转化率为95.2%。发现RSM的建议设计模型与预测的转化率良好,并且具有摩尔比和反应时间,作为影响转换的重要过程参数。 8.8991的最佳验证性能发生在EPOCH 4中,ANN模型中的1.55均为1.55的均线误差(MSE),采用Levenberg-Marquardt算法培训。通过比较预测的判定系数R〜2,通过使用RSM获得的0.8452的值,并通过使用ANN用于生物柴油转换而获得的0.9939,得出结论,ANN模型是预测RSO对生物柴油的百分比转换的最佳模型实验和预测值之间的最小误差。

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