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Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System

机译:离子液体的粘度:眼新的理论和委员会机器智能系统的应用

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摘要

Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three approaches: (I) a simple group contribution method based on temperature, pressure, boiling temperature, acentric factor, molecular weight, critical temperature, critical pressure, and critical volume; (II) a model based on thermodynamic properties, pressure, and temperature; and (III) a model based on chemical structure, pressure, and temperature. Furthermore, Eyring’s absolute rate theory is used to predict viscosity based on boiling temperature and temperature. To develop Model (I), a simple correlation was applied, while for Models (II) and (III), smart approaches such as multilayer perceptron networks optimized by a Levenberg–Marquardt algorithm (MLP-LMA) and Bayesian Regularization (MLP-BR), decision tree (DT), and least square support vector machine optimized by bat algorithm (BAT-LSSVM) were utilized to establish robust and accurate predictive paradigms. These approaches were implemented using a large database consisting of 2813 experimental viscosity points from 45 different ILs under an extensive range of pressure and temperature. Afterward, the four most accurate models were selected to construct a committee machine intelligent system (CMIS). Eyring’s theory’s results to predict the viscosity demonstrated that although the theory is not precise, its simplicity is still beneficial. The proposed CMIS model provides the most precise responses with an absolute average relative deviation (AARD) of less than 4% for predicting the viscosity of ILs based on Model (II) and (III). Lastly, the applicability domain of the CMIS model and the quality of experimental data were assessed through the Leverage statistical method. It is concluded that intelligent-based predictive models are powerful alternatives for time-consuming and expensive experimental processes of the ILs viscosity measurement.
机译:精确测定离子液体(ILS),尤其是粘度,在广泛的操作条件下对各种领域具有重要作用。在这项研究中,使用三种方法模拟纯ILS的粘度:(i)基于温度,压力,沸腾温度,龈针因子,分子量,临界温度,临界压力和临界体积的简单组贡献方法; (ii)基于热力学性质,压力和温度的模型; (iii)基于化学结构,压力和温度的模型。此外,EycRing的绝对速率理论用于预测基于沸腾温度和温度的粘度。为了开发模型(i),应用了简单的相关性,而对于模型(ii)和(iii),智能方法,例如由Levenberg-Marquardt算法(MLP-LMA)和贝叶斯正则化(MLP-BR)优化的多层Perceptron网络(MLP-Br) ),利用由BAT算法(BAT-LSSVM)优化的决策树(DT)和最小二乘支持向量机,以建立坚固且准确的预测范式。这些方法是使用大型数据库在大量压力和温度范围内由45种不同IL的2813实验粘度点组成的大型数据库来实现。之后,选择了四种最准确的模型来构建委员会机器智能系统(CMIS)。 eycing的理论结果预测粘度表明,虽然理论不准确,但其简单仍然有益。所提出的CMIS模型提供了最精确的响应,其绝对平均相对偏差(AARD)小于4%,用于预测基于模型(II)和(III)的ILS的粘度。最后,通过杠杆统计方法评估CMIS模型的适用性域和实验数据的质量。得出结论,基于智能的预测模型是ILS粘度测量的耗时和昂贵的实验过程的强大替代品。

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