Comprehensive optimization study for the methanolysis of Linum usitatissimum oil using response surface methodology and artificial neural network†

IF 2.7 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Umer Rashid, Hafeez Ur Rehman, Muhammad Raza Ul Mustafa, Balkis Hazmi, Hifsa Khurshid, Junaid Ahmad and Jianglong Yu
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Abstract

This study investigated the methanolysis of Linum usitatissimum oil using a homogeneous catalyst. Advanced optimization techniques, such as response surface methodology (RSM) and artificial neural networks (ANNs), have been employed to examine the relationship between the reaction parameters and biodiesel yield. The study investigated four reaction variables: the methanol-to-oil molar ratio, the catalyst concentration, the reaction temperature, and the methanolysis reaction completion time using RSM and its authentication was conducted using an ANN. The ANN model, consisting of 14 neurons and a well-trained Levernberg–Marquardt backpropagation method, showed a mean square error (MSE) of 0.027 at the best validation performance of 1.52 epoch-6. The coefficients of determination, R2 for the RSM-CCD were 0.99 for the observed and 0.98 for the predicted values, proving the significance of the overall model (p-value < 0.001). Furthermore, the ANN had an R2 value of 0.97, confirming the reliability of the test and complementing the RSM results. Analysis of variance (ANOVA) and regression models identified the interactions among these variables, and temperature × time (CD), catalyst concentration × temperature (BC), and catalyst concentration × time (BD) were identified as significant factors for enhancing the biodiesel yield. The RSM-CCD gave a highest possible yield of 98.7% biodiesel that was attained in only 50.4 min using a 12 : 1 methanol-to-oil ratio, 1.25% catalyst concentration, and a reaction temperature of 65 °C, whereas 97.52% yield was predicted using the ANN. A biodiesel confirmation test was performed using infrared spectroscopy and gas chromatography (GC), while adhering to ASTM D6751 biodiesel specifications to evaluate its fuel properties such as the flash point (175 °C), kinematic viscosity (5.72 °C), cloud point (−4 °C), pour point (−9 °C), acid value (0.39 mg KOH per g), higher heating value (43 MJ kg−1), water content (0.019%) and density (897 kg m−3), thus emphasizing that the Linum usitatissimum oil has significant potential for use in biodiesel production.

Abstract Image

响应面法与人工神经网络相结合的亚麻油甲醇分解工艺综合优化研究
采用均相催化剂研究了亚麻油的甲醇分解。采用响应面法(RSM)和人工神经网络(ann)等先进优化技术研究了反应参数与生物柴油产率之间的关系。研究了甲醇油摩尔比、催化剂浓度、反应温度、甲醇解反应完成时间等4个反应变量,并利用神经网络进行了验证。该ANN模型由14个神经元和训练良好的Levernberg-Marquardt反向传播方法组成,在1.52 epoch-6的最佳验证性能下,均方误差(MSE)为0.027。RSM-CCD的决定系数R2为观测值0.99,预测值0.98,证明了整体模型的显著性(p值<;0.001)。此外,ANN的R2值为0.97,证实了测试的可靠性,并与RSM结果相补充。方差分析(ANOVA)和回归模型确定了这些变量之间的相互作用,发现温度×时间(CD)、催化剂浓度×温度(BC)和催化剂浓度×时间(BD)是提高生物柴油产量的显著因素。RSM-CCD在甲醇油比为12:1、催化剂浓度为1.25%、反应温度为65℃的条件下,在50.4 min内获得了98.7%的生物柴油的最高产率,而人工神经网络的预测产率为97.52%。使用红外光谱和气相色谱法(GC)进行生物柴油确认测试,同时遵循ASTM D6751生物柴油规范评估其燃料性能,如闪点(175°C)、运动粘度(5.72°C)、浊点(- 4°C)、倾点(- 9°C)、酸值(0.39 mg KOH / g)、高热值(43 MJ kg−1)、含水量(0.019%)和密度(897 kg m−3)。因此,强调亚麻油在生物柴油生产中具有巨大的潜力。
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来源期刊
New Journal of Chemistry
New Journal of Chemistry 化学-化学综合
CiteScore
5.30
自引率
6.10%
发文量
1832
审稿时长
2 months
期刊介绍: A journal for new directions in chemistry
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