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

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 156
Author(s):  
Seyed Pezhman Mousavi ◽  
Saeid Atashrouz ◽  
Menad Nait Amar ◽  
Abdolhossein Hemmati-Sarapardeh ◽  
Ahmad Mohaddespour ◽  
...  

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.

2021 ◽  
Vol 11 (14) ◽  
pp. 6488
Author(s):  
Giuseppe Ciaburro ◽  
Gino Iannace ◽  
Virginia Puyana-Romero ◽  
Amelia Trematerra

The identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle. This is due to the considerable complexity of a sound that is made up of many contributions at different frequencies. Each sound has a specific frequency spectrum, but when many sounds overlap it becomes difficult to discriminate between the different contributions. In this case, it can be extremely useful to have a tool that is capable of identifying the operating conditions of an acoustic source. In this study, measurements were made of the noise emitted by a wind turbine in the vicinity of a sensitive receptor. To identify the operating conditions of the wind turbine, average spectral levels in one-third octave bands were used. A model based on a support vector machine (SVM) was developed for the detection of the operating conditions of the wind turbine; then a model based on an artificial neural network was used to compare the performance of both models. The high precision returned by the simulation models supports the adoption of these tools as a support for the acoustic characterization of noise in environments close to wind turbines.


2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.


Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1050
Author(s):  
Sarrthesvaarni Rajasuriyan ◽  
Hayyiratul Fatimah Mohd Zaid ◽  
Mohd Faridzuan Majid ◽  
Raihan Mahirah Ramli ◽  
Khairulazhar Jumbri ◽  
...  

The biggest challenge faced in oil refineries is the removal of sulfur compounds in fuel oil. The sulfur compounds which are found in fuel oil such as gasoline and diesel, react with oxygen in the atmosphere to produce sulfur oxide (SOx) gases when combusted. These sulfur compounds produced from the reaction with oxygen in the atmosphere may result in various health problems and environmental effects. Hydrodesulfurization (HDS) is the conventional process used to remove sulfur compounds from fuel oil. However, the high operating conditions required for this process and its inefficiency in removing the organosulfur compounds turn to be the major drawbacks of this system. Researchers have also studied several alternatives to remove sulfur from fuel oil. The use of ionic liquids (ILs) has also drawn the interest of researchers to incorporate them in the desulfurization process. The environmental effects resulting from the use of these ILs can be eliminated using eutectic-based ionic liquids (EILs), which are known as greener solvents. In this research, a combination of extractive desulfurization (EDS) and oxidative desulfurization (ODS) using a photocatalyst and EIL was studied. The photocatalyst used is a pre-reported catalyst, Cu-Fe/TiO2 and the EIL were synthesized by mixing choline chloride (ChCl) with organic acids. The acids used for the EILs were propionic acid (PA) and p-toluenesulfonic acid (TSA). The EILs synthesized were characterized using thermogravimetry analyser (TGA) differential scanning calorimetry (DSC) analysis to determine the physical properties of the EILs. Based on the TGA analysis, ChCl (1): PA (3) obtained the highest thermal stability whereas, as for the DSC analysis, all synthesized EILs have a lower melting point than its pure component. Further evaluation on the best EIL for the desulfurization process was carried out in a photo-reactor under UV light in the presence of Cu-Fe/TiO2 photocatalyst and hydrogen peroxide (H2O2). Once the oxidation and extraction process were completed, the oil phase of the mixture was analyzed using high performance liquid chromatography (HPLC) to measure the sulfur removal efficiency. In terms of the desulfurization efficiency, the EIL of ChCl (1): TSA (2) showed a removal efficiency of about 99.07%.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 27789-27801 ◽  
Author(s):  
Hongxin Xue ◽  
Yanping Bai ◽  
Hongping Hu ◽  
Ting Xu ◽  
Haijian Liang

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