In recent decades, the rapid growth of the world population has led to fast industrial development and higher usage of chemical materials. Among the chemical compounds, the chemical solvents, especially the volatile organic compounds (VOCs) are of great importance in numerous industrial processes and applications. The main benefit of using VOCs is their ease of removal and evaporation in several applications such as separation and extraction processes; but their critical disadvantages are their adverse health effects such as allergic skin reactions, dyspnea, nose and throat discomfort, and their environmental pollutant role destroying the ozone layer through free radical air oxidation processes [1].
Recently, there are lots of demands among several countries to move toward “green”
industries to eliminate the environmental pollutions, especially air pollution. In this regard, one of the basic and fundamental steps is to substitute the VOCs from different industrial chemical processes with a “green” alternative. Recent research has revealed that the ionic liquids (ILs) are typically the best possible environmental friendly alternative to the conventional solvents. In general, they show unusual but interesting properties such as extremely low saturation vapor pressure and negligible volatility, wide liquid range, nonflammability, high thermal conductivity, and high physical and chemical stability [2].
Ionic liquids are defined as molten salts which are generally liquid at or near room temperature (typically below 100 °C) due to the poor coordination of ions [3]. ILs are typically composed of a cation and an anion; so their thermophysical properties are strongly dependent on the type and chemical structure of the ions. As a result, they can be designed for specific applications with desired properties by choosing the proper pair of ions. This feature has made them the “tunable” and “designable” materials [4]. Consequently, ionic liquids are potential to be applied in numerous industrial applications, such as extraction and separation processes [5, 6], battery industry [7, 8], fuel cells [9, 10], solar panels [11, 12], polymer and biopolymer processing [13-15], electroplating [16, 17], lubricants [18-21], waste recycling [22-25], gas separation and CO2 capturing [26-33], catalysis [34-36], and many others [37-40].
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The existence of a large number of combinations of organic cations and anions leads to the generation of various groups of ionic liquids. The most popularly researched groups are imidazolium, ammonium, phosphonium, pyrrolidinium, pyridinium, guanidinium, isoquinolinium, piperidinium, morpholinium, and sulphonium. As a result, thousands of ionic liquids can be synthesized in theory by different types of cations and anions. In addition, it is possible to design the ILs for specific applications before synthesis. In this regard, predictive models can play an important role to relate the physico-chemical properties of ILs to their constituent cation and anion combinations or other properties of ILs.
The models used for property estimations of ionic liquids as well as other chemical compounds are classified into three main types:
1. Models based on chemical structure and functions groups of the compounds.
2. Models based on other physico-chemical properties.
3. Models based on both chemical structure and other physico-chemical properties.
Usually the model which uses the other thermophysical properties of the compounds has better accuracy and prediction ability, especially for nonlinear properties; but it strongly depends on the availability of the experimental data for all parameters of the model for desired compound. If one of the parameters does not exist in the literature, the model becomes useless if the missing parameter cannot be estimated by another model.
The first ionic liquid was discovered in 1888 [41]. Since then, more than a thousand ionic liquids have been reported to date. Despite the great interest of introducing the new ionic liquids, less effort have been done to measure the physico-chemical properties of ionic liquids uniformly. For example, experimental density data is available for more than 500 ionic liquids, but surface tension has been measured for less than 150 ILs. At present, the results of experimental investigations for ionic liquids as well as other compounds are reported comprehensively in the form of large databases by Dortmund Data Bank [42], and NIST ThermoData Engine [43]. For non-ionic liquid compounds, there are few more databases such as DIPPR [44].
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Due to lack of enough experimental data for the thermophysical properties of ILs, use of other thermophysical properties of ILs as the parameters of a new model is not applicable and/or straightforward in most cases. Consequently, a chemical structure-based model can overcome such limitations and can be applied to develop more comprehensive and general models.
The aim of this study is to develop the best new accurate and predictive models for prediction of the thermophysical properties of ionic liquids which are more comprehensive but have less limitation. For this purpose, the largest possible databases are collected by the use of the NIST standard reference database and searching the literature for data published recently; so the more comprehensive and predictive models can be developed with the use of various types of ionic liquids and more experimental data points, compared with the available models in the literature.
Furthermore, it is targeted to develop some models for the prediction of infinite dilution activity coefficient (γ∞) of organic solutes in various ionic liquids. This property has not been modeled by Group contribution (GC) or Quantitative Structure-Property Relationship (QSPR) to date. These novel models make the calculation/prediction of γ∞ much easier compared with the UNIFAC model.
Another aspect of this study is to assess the use of the QSPR method for development of new models for ionic liquids. The common approach in the model development for ionic liquids is the GC method and consequently, there is an opportunity to develop some new and predictive QSPR models for the first time for some thermophysical properties of ionic liquids.
The main approach of the model development in this thesis is the use of chemical structure- based parameters to develop accurate and predictive property estimation models for ionic liquids. The number of these parameters is more than a thousand for ionic liquids and when a temperature dependent property is studied, it may goes up to 40,000 parameters (all parameters are multiplied by each other or by a function of the temperature; e.g. , , ). As a result, novel and different mathematical approaches and feature selection methods should be applied to choose the most effective parameters on the target property.
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To achieve this goal, there are several steps that have to be undertaken to produce a predictive model. In this thesis, the procedure is as follows:
1- Each property was selected carefully. The selected property should have reasonable number of data points and the previous models in the literature should have at least one drawback such as complexity, low accuracy, high number of parameters, limited supported compounds, etc.
2- It was tried to collect all available data into a dataset using the NIST standard reference database and searching the literature for recent published data.
3- The dataset was examined carefully to screen the duplicated and erroneous data.
4- A mathematical method was selected and the model was developed. If the model did not have good accuracy, the mathematical method was changed.
5- The model was validated through different statistical methods and its prediction power was evaluated.
6- Finally, if it is applicable, the model developed was compared with previous models in term of simplicity, accuracy, prediction power, and comprehensiveness.
All the steps will be explained in detail in other chapters of the thesis. Chapter 2 covers the literature review of the properties studied. In chapter 3, the data management procedure is described. Afterward, the methods used for modeling are explained in chapter 4. Thereafter, chapter 5 discusses about the model development procedure. Chapter 6 includes the result of modeling for physico-chemical properties studied. The conclusion of this thesis is presented in Chapter 7. Finally, Chapter 8 offers some recommendations for future works.
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