Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran.
Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez 424, Tehran 15875-4413, Iran.
Molecules. 2020 Dec 31;26(1):156. doi: 10.3390/molecules26010156.
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.
准确确定离子液体(ILs)的物理化学特性,尤其是在广泛的操作条件下的粘度,对于各个领域都至关重要。在本研究中,使用三种方法对纯 ILs 的粘度进行建模:(I)一种基于温度、压力、沸点、偏心因子、分子量、临界温度、临界压力和临界体积的简单基团贡献方法;(II)一种基于热力学性质、压力和温度的模型;以及(III)一种基于化学结构、压力和温度的模型。此外,还使用 Eyring 绝对速率理论基于沸点和温度预测粘度。为了开发模型(I),应用了一个简单的相关关系,而对于模型(II)和(III),则使用了多层感知器网络(MLP)优化的 Levenberg-Marquardt 算法(MLP-LMA)和贝叶斯正则化(MLP-BR)、决策树(DT)和蝙蝠算法(BAT)优化的最小二乘支持向量机(BAT-LSSVM)等智能方法来建立稳健且准确的预测范例。这些方法是使用一个由 45 种不同 ILs 在广泛的压力和温度范围内的 2813 个实验粘度点组成的大型数据库来实现的。之后,选择了四个最准确的模型来构建委员会机器智能系统(CMIS)。Eyring 理论预测粘度的结果表明,尽管该理论不精确,但它的简单性仍然是有益的。基于模型(II)和(III),所提出的 CMIS 模型在预测 ILs 粘度方面提供了最准确的响应,绝对平均相对偏差(AARD)小于 4%。最后,通过杠杆统计方法评估了 CMIS 模型的适用域和实验数据的质量。结论是,基于智能的预测模型是替代 ILs 粘度测量耗时且昂贵的实验过程的有力选择。