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首页> 外文OA文献 >LIQUID-LIQUID EQUILIBRIA FOR SYSTEMS CONTAINING FATTY ACID ETHYL ESTERS, ETHANOL AND GLYCEROL AT 333.15 AND 343.15 K: EXPERIMENTAL DATA, THERMODYNAMIC AND ARTIFICIAL NEURAL NETWORK MODELING
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LIQUID-LIQUID EQUILIBRIA FOR SYSTEMS CONTAINING FATTY ACID ETHYL ESTERS, ETHANOL AND GLYCEROL AT 333.15 AND 343.15 K: EXPERIMENTAL DATA, THERMODYNAMIC AND ARTIFICIAL NEURAL NETWORK MODELING

机译：用于含有脂肪酸乙酯，乙醇和甘油在333.15和343.15K：实验数据，热力学和人工神经网络建模的系统的液体液体平衡

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Abstract In this study, the liquid-liquid equilibrium (LLE) data of systems containing ethyl linoleate/oleate/palmitate/laurate, ethanol and glycerol at temperatures ranging from 323.15 to 353.15 K were used to evaluate the performance of the NRTL, UNIFAC, Cubic-Plus-Association Equation of State (CPA EoS), and artificial neural network (ANN) models. The systems evaluated correspond to the most important components formed at the end of the ethanolysis reaction of soybean, palm and coconut oils. The temperature range selected is very important for heterogeneous catalysts, especially for high-pressure systems. The accuracy of the models was evaluated by average global deviation. UNIFAC, UNIFAC-LLE and CPA EoS models showed lower accuracy with deviations of 10.1, 8.01 and 5.95%, respectively. In spite of this predictive limitation, these models show high extrapolation capability for the description of LLE behavior when few experimental data are available in the literature. The ANN model shows the best agreement between experimental and predicted data with an average deviation of 1.12%. In this regard, ANN is offered in this work as an alternative to equations of state and activity coefficient models to be used in a more reliable and less cumbersome way for process simulators of biodiesel production and separation equipment design.
机译：摘要在本研究中，使用323.15至353.15k的温度下含有乙基锂酸酯/棕榈酸酯/棕榈酸盐/月桂酸盐，乙醇和甘油的液体平衡（LLE）数据，用于评估NRTL，UNIFAC，立方体的性能-Plus-状态（CPA EOS）和人工神经网络（ANN）模型的关联方程。评估的系统对应于大豆，棕榈和椰子油的乙溶解反应结束时形成的最重要的组分。选择的温度范围对于非均相催化剂非常重要，特别是对于高压系统。通过平均全局偏差评估模型的准确性。 UNIFAC，Unifac-Lle和CPA EOS模型表现出较低的精度，分别偏差为10.1,8.01和5.95％。尽管有这种预测的限制，但是当在文献中有很少的实验数据时，这些模型显示出LLE行为的描述的高推断能力。 ANN模型显示了实验和预测数据之间的最佳协议，平均偏差为1.12％。在这方面，在这方面提供了ANN，作为国家和活动系数模型方程的替代方案，以便以更可靠和更不繁琐的方法用于生物柴油生产和分离设备设计的过程模拟器。

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