Extending the UNIFAC model for ionic liquid–solute systems by combining experimental and computational databases

Hydrocarbon Extraction with Ionic Liquids

Separation and reaction processes are key components employed in the modern chemical industry, and the former accounts for the majority of the energy consumption therein. In particular, hydrocarbon separation and purification processes, such as aromatics extraction, desulfurization, and denitrification, are challenging in petroleum refinement, an industrial cornerstone that provides raw materials for products used in human activities. The major technical shortcomings in solvent extraction are volatile solvent loss, product entrainment leading to secondary pollution, low separation efficiency, and high regeneration energy consumption due to the use of traditional organic solvents with high boiling points as extraction agents. Ionic liquids (ILs), a class of designable functional solvents or materials, have been widely used in chemical separation processes to replace conventional organic solvents after nearly 30 years of rapid development. Herein, we provide a systematic and comprehensive review of the state-of-the-art progress in ILs in the field of extractive hydrocarbon separation (i.e., aromatics extraction, desulfurization, and denitrification) including (i) molecular thermodynamic models of IL systems that enable rapid large-scale screening of IL candidates and phase equilibrium prediction of extraction processes; (ii) structure-property relationships between anionic and cationic structures of ILs and their separation performance (i.e., selectivity and distribution coefficients); (iii) IL-related extractive separation mechanisms (e.g., the magnitude, strength, and sites of intermolecular interactions depending on the separation system and IL structure); and (iv) process simulation and design of IL-related extraction at the industrial scale based on validated thermodynamic models. In short, this Review provides an easy-to-read exhaustive reference on IL-related extractive separation of hydrocarbon mixtures from the multiscale perspective of molecules, thermodynamics, and processes. It also extends to progress in IL analogs, deep eutectic solvents (DESs) in this research area, and discusses the current challenges faced by ILs in related separation fields as well as future directions and opportunities.

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

Condensable Gases Capture with Ionic Liquids

Condensable gases are the sum of condensable and volatile steam or organic compounds, including water vapor, which are discharged into the atmosphere in gaseous form at atmospheric pressure and room temperature. Condensable toxic and harmful gases emitted from petrochemical, chemical, packaging and printing, industrial coatings, and mineral mining activities seriously pollute the atmospheric environment and endanger human health. Meanwhile, these gases are necessary chemical raw materials; therefore, developing green and efficient capture technology is significant for efficiently utilizing condensed gas resources. To overcome the problems of pollution and corrosion existing in traditional organic solvent and alkali absorption methods, ionic liquids (ILs), known as "liquid molecular sieves", have received unprecedented attention thanks to their excellent separation and regeneration performance and have gradually become green solvents used by scholars to replace traditional absorbents. This work reviews the research progress of ILs in separating condensate gas. As the basis of chemical engineering, this review first provides a detailed discussion of the origin of predictive molecular thermodynamics and its broad application in theory and industry. Afterward, this review focuses on the latest research results of ILs in the capture of several important typical condensable gases, including water vapor, aromatic VOCs (i.e., BTEX), chlorinated VOC, fluorinated refrigerant gas, low-carbon alcohols, ketones, ethers, ester vapors, etc. Using pure IL, mixed ILs, and IL + organic solvent mixtures as absorbents also briefly expanded the related reports of porous materials loaded with an IL as adsorbents. Finally, future development and research directions in this exciting field are remarked.

New Model to Predict Infinite Dilution Activity Coefficients Based on (∂p/∂x) T,x → 0

Accurate prediction of infinite dilution activity coefficient (γ^∞) is essential for the calculation of phase equilibria, solubility, and related properties in molecular thermodynamics. Here, we propose a new model to accurately predict the value of γ^∞. It is applicable to calculate γ^∞ for compounds in aqueous solution at different temperatures. The model is based on the relationship of (∂p/∂x) _T,x→0 with γ^∞ and temperature at low pressure. First, we introduce the new idea of using the group contribution method to estimate (∂p/∂x) _T,x→0 and then obtain the activity coefficient of a solute at infinite dilution in water based on the relationship between (∂p/∂x) _T,x→0 and γ^∞. The accuracy of this model is verified using experimental data from 46 systems and more than 450 data points. The result shows that the total average relative deviation of the predicted values from the experimental values for training data is 4.73%. Besides, we test the applicability of the model using solutes that are not part of the training data set. The result shows that the model is satisfactory for the prediction of testing data. Compared with other models, the results prove that the developed model outperforms the UNIFAC model, the modified UNIFAC model, and previous predictive models for aqueous systems. The final equation with only simple arithmetic is more easily applied in engineering practices.

Ionic liquid-based in situ product removal design exemplified for an acetone-butanol-ethanol fermentation

Selecting an appropriate separation technique is essential for the application of in situ product removal (ISPR) technology in biological processes. In this work, a three-stage systematic design method is proposed as a guide to integrate ionic liquid (IL)-based separation techniques into ISPR. This design method combines the selection of a suitable ISPR processing scheme, the optimal design of an IL-based liquid-liquid extraction (LLE) system followed by process simulation and evaluation. As a proof of concept, results for a conventional acetone-butanol-ethanol fermentation are presented (40,000 ton/year butanol production). In this application, ILs tetradecyl(trihexyl)phosphonium tetracyanoborate ([TDPh][TCB]) and tetraoctylammonium 2-methyl-1-naphthoate ([TOA] [MNaph]) are identified as the optimal solvents from computer-aided IL design (CAILD) method and reported experimental data, respectively. The dynamic simulation results for the fermentation process show that, the productivity of IL-based in situ (fed-batch) process and in situ (batch) process is around 2.7 and 1.8fold that of base case. Additionally, the IL-based in situ (fed-batch) process and in situ (batch) process also have significant energy savings (79.6% and 77.6%) when compared to the base case.

Modeling of Technological Processes for a Rectification Plant in Second-Generation Bioethanol Production

The article deals with the recent developments in the fuel industry, considering the permanent increasing requirements for fuel quality and environmental safety. The work aims to study various technological modes at the rectification unit to produce fuel bioethanol from lignocellulosic biomass. The main goals are to solve applied scientific problems of rational designing and technological optimization to obtain boundaries of energy consumption to ensure the quality of bioethanol sufficient for a consumer. Recent approaches for numerical simulation of chemical technological processes were applied to study the operating processes and optimize technological parameters. The plant model was designed from various modules that allow us to simulate technological processes efficiently and accurately for all the primary units of the rectification equipment. The methodology based on the activity coefficient UNIFAC model of phase equilibrium was applied. As a result, a mixture with 74% of bioethanol 9% of impurities was obtained in the brew column. In the epuration column, a mixture of 46% bioethanol and 2.2% of impurities was obtained in bottoms. Finally, in the alcohol column, the mass fraction of distillate of 96.9% and impurities of 2.7% were reached. The numerical simulation results can be applied in recent fuel technologies and designing the corresponding biofuel plants.

A QSPR model for estimating Henry's law constant of H2S in ionic liquids by ELM algorithm

Ionic liquids (ILs) as green solvents have been studied in the application of gas sweetening. However, it is a huge challenge to obtain all the experimental values because of the high costs and generated chemical wastes. This study pioneered a quantitative structure-property relationship (QSPR) model for estimating Henry's law constant (HLC) of H₂S in ILs. A dataset consisting of the HLC data of H₂S for 22 ILs within a wide range of temperature (298.15-363.15 K) were collected from published reports. The electrostatic potential surface area (S_EP) and molecular volume of these ILs were calculated and used as input descriptors together with temperature. The extreme learning machine (ELM) algorithm was employed for nonlinear modelling. Results showed that the determination coefficient (R²) of the training set, test set and total set were 0.9996, 0.9989,0.9994, respectively, while the average absolute relative deviation (AARD%) of them were 1.3383, 2,4820 and 1.5820, respectively. The statistical parameters for the measurement of the model exhibited the great reliability, stability, and predictive power of the ELM model. The Applicability Domain (AD) of the ELM model is also investigated. It proves that the majority of ILs in the training and test sets are located in the model's AD and verifies the reliability of the model. The proposed model is potentially applicable to guide the application of ILs for gas sweetening.

Ion Dissociation in Ionic Liquids and Ionic Liquid Solutions

The extent to which cations and anions in ionic liquids (ILs) and ionic liquid solutions are dissociated is of both fundamental scientific interest and practical importance because ion dissociation has been shown to impact viscosity, density, surface tension, volatility, solubility, chemical reactivity, and many other important chemical and physical properties. When mixed with solvents, ionic liquids provide the unique opportunity to investigate ion dissociation from infinite dilution in the solvent to a completely solvent-free state, even at ambient conditions. The most common way to estimate ion dissociation in ILs and IL solutions is by comparing the molar conductivity determined from ionic conductivity measurements such as electrochemical impedance spectroscopy (EIS) (which measure the movement of only the charged, i.e., dissociated, ions) with the molar conductivity calculated from ion diffusivities measured by pulse field gradient nuclear magnetic resonance spectroscopy (PFG-NMR, which gives movement of all of the ions). Because the NMR measurements are time-consuming, the number of ILs and IL solutions investigated by this method is relatively limited. We have shown that use of the Stokes-Einstein equation with estimates of the effective ion Stokes radii allows ion dissociation to be calculated from easily measured density, viscosity, and ionic conductivity data (ρ, η, λ), which is readily available in the literature for a much larger number of pure ILs and IL solutions. Therefore, in this review, we present values of ion dissociation for ILs and IL solutions (aqueous and nonaqueous) determined by both the traditional molar conductivity/PFG-NMR method and the ρ, η, λ method. We explore the effect of cation and anion alkyl chain length, structure, and interaction motifs of the cation and anion, temperature, and the strength of the solvent in IL solutions.

Prediction of ammonia absorption in ionic liquids based on extreme learning machine modelling and a novel molecular descriptor SEP

The large amounts of ammonia emissions generated from industrial production have caused serious environmental pollution problems, such as soil acidification, eutrophication, the formation of fine particles and changes in the global greenhouse balance, and also greatly endanger human health. At present, effectively reducing ammonia emissions or recovering ammonia is still a huge challenge. Ionic liquids (ILs) as a new class of green solvent have been introduced for ammonia absorption with great potential, but a huge number on combination systems of ILs lead to the difficulty of measuring the ammonia solubility in all ILs by experiments (e.g., danger and cost). Hereby, this study proposed a novel approach for estimating the ammonia solubility in different ILs. A predictive model was developed based on the novel Algorithm - extreme learning machine (ELM) and the molecular descriptors of electrostatic potential surface areas (S_EP) as input parameters. Besides, 502 data points of ammonia solubility in 17 ILs were gathered with a wide range of pressure and temperature. For the total set, the determination coefficient (R²) and the average absolute relative deviation (AARD) of the developed model were 0.9937 and 2.95%, respectively. The regression plots revealed good consistency between predictive and experimental data points. Results show the good performance and reliability of the developed model, indicating that the proposed approach can be potentially applied for screening reasonable ILs to absorb ammonia from chemical industry processes.