Systematic Method for Screening Ionic Liquids as Extraction

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A systematic method for screening ionic liquids as extraction solvents exemplified by an extractive desulfurization process Zhen Song, Teng Zhou, Zhiwen Qi, and Kai Sundmacher ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b00024 • Publication Date (Web): 17 Feb 2017 Downloaded from http://pubs.acs.org on February 22, 2017

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A systematic method for screening ionic liquids as extraction solvents exemplified by an extractive desulfurization process Zhen Songa,b, Teng Zhoub,*, Zhiwen Qia,*, Kai Sundmacherb,c a

Max Planck Partner Group at the State Key Laboratory of Chemical Engineering, School of Chemical

Engineering, East China University of Science and Technology, 130 Meilong Road, 200237, Shanghai, China b

Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106, Magdeburg, Germany c

Process Systems Engineering, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, D-39106, Magdeburg, Germany Corresponding author: [email protected] (Teng Zhou); [email protected] (Zhiwen Qi)

ABSTRACT For the selection of industrially suitable ILs as extraction solvents, a systematic method combining phase equilibrium calculation, physical property prediction, and process simulation is presented. The COSMO-RS model is used to predict the liquid-liquid equilibria of the systems composed of the target mixture to be separated and different ILs at the specific global composition of interest, thereby pre-screening ILs with higher mass-based distribution coefficient and selectivity as well as lower solvent loss. Group contribution methods are then employed to estimate the key physical properties of the pre-screened ILs and further suggest candidates meeting certain physical property constraints. Afterwards, the performance of the top IL candidates in a continuous process is analyzed by Aspen Plus to finally identify process-based optimal solvents. The proposed method is illustrated with an extractive desulfurization case study and two most promising ILs for this process are consequently determined. KEYWORDS: ionic liquids screening; liquid-liquid equilibrium; physical property; process simulation; extractive desulfurization

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INTRODUCTION Ionic liquids (ILs), known as molten salts at or around room temperature, possess many attractive physicochemical properties, such as negligible volatility, high thermal stability, broad liquid range, good solvating ability for a wide variety of inorganic and organic compounds, etc. More importantly, the properties of ILs can be fine-tuned by the combinations of different cations, anions and substitution groups, making them ‘designer’ solvents.1,2 Due to these unique characteristics, ILs have received considerable attention as alternatives to traditional organic solvents in various extraction processes, e.g., separation of aromatic and aliphatic hydrocarbons,3,4 extractive desulfurization and denitrogenation of fuel oils,4-9 purification of drugs and biomolecules.10,11 To apply IL for a specific extraction, the primary concern is the selection of suitable solvent since different ILs usually present very distinct properties and separation performance. Currently, most studies with IL solvents are still focusing on the testing of various ILs by simple laboratory experiments.3-8 However, even for the most extensively studied systems, the ILs that have been covered are only the tip of the iceberg in comparison to the huge number of possible cation-anion combinations. To maximize the potential of IL-based extractions, searching the optimal solvents through a larger space is of great significance. For this purpose, the experimental approach is expensive, time-consuming, and even infeasible. Therefore, in this respect, reliable and efficient theoretical approaches for IL screening are highly desired.9-15 Ab initio methods, such as molecular dynamics and quantum chemical calculations have been demonstrated to be able to offer some useful insights into the thermodynamic properties of ILinvolved systems.16-18 However, such methods are generally computationally expensive, even for studying one single IL system, and thus are impractical for the extensive screening of ILs from a large number of potential candidates. The classical activity coefficient models, such as NRTL and UNIQUAC, and equations of state such as PC-SAFT have been successfully applied for modeling and predicting the phase behaviors of IL-involved systems.19-23 However, these methods normally require a number of experimentally fitted parameters, presenting a limited predictive capability for novel systems without experimental data. UNIFAC-IL models extended by Lei et al. and Roughton et al. are very promising methods for IL screening and design.20-22 However, as the group interaction parameters are regressed from experimental data, the current


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UNIFAC-IL interaction parameter matrix is still inadequate to cover the large diversity of ILs and separation systems. Compared to the methods mentioned above, the Conductor-like Screening MOdel for Real Solvents (COSMO-RS) is very promising for predicting thermodynamic properties of purecomponent liquids and liquid mixtures, including IL systems.24 A standard COSMO-RS calculation contains two steps. In the first step, the screening charge density distributions (also known as σ-profiles) of interested compounds are obtained via quantum chemical calculations. In the second step, the component chemical potential is quantified from the statistical thermodynamics treatment of molecular interactions based on the obtained σ-profiles. With the chemical potential information, thermodynamic properties, such as the activity coefficient, solubility, and liquid-liquid equilibrium can then be determined. The current COSMO database already covers the σ-profiles of thousands of conventional solvents and most of the reported cations and anions of ILs. The σ-profiles of new compounds including user-designed ILs can be conveniently obtained from standard quantum chemical DFT calculations. As a fully predictive thermodynamic model, COSMO-RS can make relatively accurate predictions on the activity coefficients of different solutes in ILs and phase equilibria of different IL-involved systems.25-28 For these reasons, COSMO-RS is widely accepted as a fast IL screening tool for various separation problems. So far, several contributions on the screening of ILs as extraction solvents based on COSMO-RS have been reported.10-15 In these works, the extraction distribution coefficient and selectivity at the infinite dilution condition (β∞ and S∞) are generally employed as the screening criteria, defined as follows:

β ∞ =1/γ i∞

(1)

S ∞ = γ ∞j / γ i∞

(2)

∞ where γi and γ∞ j refer to the infinite dilution activity coefficients of solute i and dilute j in the IL

phase, respectively. Although these two parameters can provide a simple and quick evaluation of the separation performance of an IL, they may not lead to the optimal solvent for a practical system due to the neglect of the real extraction concentrations.29 Moreover, the β∞ and S∞ can only indicate the extraction ability of ILs on a molar basis. However, the mass-based solvent



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consumption is more straightforward for evaluating the practical applicability of different ILs.12 Therefore, comparing to β∞ and S∞, the distribution coefficient and selectivity derived from the mass-based liquid-liquid equilibria (LLE) of the system composed of the mixture to be separated and ILs at the interested global composition, are more reasonable for screening ILs. In addition to the thermodynamic criteria, some fundamental physical properties (e.g., melting point and viscosity) also play a significant role in determining the suitability of ILs as extraction solvents. For instance, in a practical extraction process, the melting point of IL should be below the operating temperature, and a low viscosity of IL is normally desired to increase the masstransfer rate and reduce the pumping cost.30,31 However, these key physical properties are rarely considered in the previous IL screening works. Besides, it is always preferred that the optimal solvent for an extraction task is finally identified based on the highest performance in the continuous process; whereas it is unrealistic to evaluate the process performance of a large number of ILs without a preliminary screening by thermodynamic and physical property constraints.32,33 Considering all the aforementioned limitations, a systematic IL screening method which combines the mass-based LLE calculation, physical property prediction, as well as process simulation and evaluation is presented in the present work. An extractive desulfurization (EDS) case study is performed to demonstrate the proposed method, and two most promising ILs with much higher process performance than the benchmark organic solvent are finally identified.

METHODS As illustrated in Figure 1, the whole IL screening method consists of three steps. The first step is the COSMO-RS based LLE prediction of the systems composed of the target mixture and different ILs at the specific global composition of interest. From a large database of cations and anions, the cation-anion combinations that present a higher mass-based distribution coefficient (β) and selectivity (S), and lower solvent loss in the raffinate phase (SL) than the benchmark solvent (usually a widely used organic solvent) are selected. The second step is the estimation of the melting point and the viscosity of the pre-screened candidates by group contribution (GC) methods. Then, ILs with desired physical properties are identified. The third step is the simulation and evaluation of the continuous extraction process with the top IL candidates


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obtained after the first two steps, and optimal ILs with the highest process performance (in terms of solvent and energy consumption, etc.) are finally selected.

COSMO-RS based LLE prediction Since the detailed COSMO-RS theory is given elsewhere,24,34 only the major features for understanding the predictions in the present work are summarized briefly in the following. The capability of COSMO-RS is to calculate the chemical potential of an arbitrary solute in any pure or mixed solvent enables the LLE prediction of binary, ternary or multinary mixtures at any mixture concentration. The principle of the LLE calculation is described here with a two-liquidphase system as an example. All compounds in the system are distributed between the two phases according to their partition equilibrium constants (Ki),

 µiI − µiII  K i = exp    RT 

(3)

where µiI and µiII denote the chemical potential of compound i in phase I and phase II, respectively. Since the chemical potentials of all components depend on the compositions of the phases, the equilibrium distribution has to be solved iteratively. Specifically, with a given set of compositions, the chemical potentials and partition equilibrium constants are calculated. A new set of compositions is then refined from the given compositions and the equilibrium constants. In the LLE calculations, an initial set of composition is firstly guessed and the above procedure is repeated until all the component compositions in the two phases do not change anymore.28,34 A number of previous studies have demonstrated the good qualitative and in many cases acceptable quantitative ability of COSMO-RS for calculating LLE of IL-involved systems.26-28 The software package COSMOthermX (Version C30_1601) based on COSMO-RS allows for easy and fast LLE calculation at any global composition. In this work, the LLE calculations are performed using the BP_TZVP_C30_1601 parameterization. All the σ-profiles of the involved cations and anions of ILs, as well as the conventional compounds are directly taken from the standard database of the software. The LLE of a large number of systems comprising different ILs is automatically performed by an auxiliary batch-processing program in COSMOthermX, i.e., CT_CREATE, which only requires a template input file for LLE calculation and a list of the involved cations and anions.34 After obtaining the LLE results, the mass-based β, S, and SL are determined by



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β = m1E m1R

(4)

m1E m1R

(5)

S=

m2E m2R

SL = m3R

(6)

where m1, m2, m3 are the mass fractions of the solute, dilute, and solvent (in this case IL), and superscripts E and R represent the extract phase and the raffinate phase, respectively. These three parameters are applied as the thermodynamic criteria to evaluate the extraction ability of IL for a specific extraction task.

GC based physical property estimation As mentioned above, the melting point and viscosity of ILs are the most important physical properties that should be considered when applying them as extraction solvents. The GC model developed by Lazzús et al. is used to evaluate the melting point of ILs prescreened by the first step, 31

36

i =1

j =1

Tm (K) = 288.7 + ∑ ni ∆tci +∑ n j ∆taj

(7)

where ni and nj are the numbers of the cation group i and anion group j in IL; ∆tci and ∆taj are the contributions of the cation and anion groups to the melting point, respectively. This model is parameterized from experimental melting point data of 400 different ILs covering 31 cation groups and 36 anion groups. The average deviation between the experimental and predicted melting point is less than 0.07.30 The viscosity of the ILs that satisfy the melting point constraint is evaluated to further screen out suitable solvent candidates. The GC model developed by Lazzús and Pulgar-Villarroel is employed 20 67 67  20  ln η = 6.982 + ∑ ni ai+ + ∑ n j a −j +  ∑ nibi+ + ∑ n j b −j  T i =1 j =1 j =1  i =1 

(8)

where ni and nj are the numbers of the cation group i and anion group j in IL; ai+ / bi+ and a −j / b −j are the contributions of the cation group i and anion group j to the viscosity, respectively. This model is established on the basis of 1445 experimental data points of 326 ILs, consisting of 20 cation groups and 67 anion groups. The average deviation of the model is 0.045.31


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Aspen Plus based process simulation and evaluation Currently, the simulation of IL-involved processes is still a challenging task due to the following two reasons: (1) ILs have not been included in the component databanks of process simulators (e.g., Aspen Plus); (2) parameters of common thermodynamic models are not available for ILinvolved systems.32,35-39 In this work, Aspen Plus V8.8 is employed to simulate the continuous extractive desulfurization (EDS) process using an IL as the solvent. To address the first problem, ILs are defined as pseudo-components by specifying the normal boiling temperature, density and molecular weight. Normal boiling point of ILs is predicted by the extended GC method of Valderrama and Rojas.40 Density and molecular weight of ILs are provided by COSMO-RS calculation.41 The other pseudo-component properties required for process simulation are estimated by the implicit methods and models in Aspen Plus. Except for the ILs, the other compounds (thiophene, noctane, and sulfolane in the case study) are defined as conventional components in the Aspen databank. This component definition approach has been demonstrated to be reliable for the simulation of IL-based processes of aromatic/aliphatic hydrocarbons extraction32,38 and thiophene/benzene extractive distillation.39 With regard to the second problem, the NRTL model is adopted with the binary interaction parameters regressed from COSMO-RS predicted LLE data. The regression is carried out in Aspen Plus Data Regression System (DRS) following a standard procedure as summarized by Sandler.42 The default generalized least-squares method based on maximum likelihood principles is employed and the convergence tolerance is set to 0.0001. Multiple sets of ternary LLE data of the investigated extraction system at various temperatures are predicted by COSMO-RS and then used in the regression to increase the reliability of the obtained binary interaction parameters.42

APPLICATION The proposed method is demonstrated through an application in the EDS process, where thiophene and n-octane are taken as the sulfur compound (solute) and model fuel component (dilute), respectively.5-9 In the case study, sulfolane is selected as the benchmark because it is regarded as one of the most promising conventional solvent for this process.3,5



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Step 1: Pre-screening by thermodynamic criteria In the COSMO-RS prediction, 370 cations and 98 anions resulting in 36260 IL candidates are selected from the COSMOthermX database. The detailed information about the cations and anions are provided in Tables S1 (Supporting Information). Since EDS is typically focused on the removal of a trace amount of sulfur compounds from fuels, the initial sulfur content in the model fuel (thiophene and n-octane mixture) is assumed to be 100 ppm. In order to compare the mass-based EDS performance of different ILs, the mass ratio of IL to the model fuel is fixed as 1:1 in the LLE calculation. To validate the importance of the proposed thermodynamic criteria derived from mass-based LLE, IL screening on the basis of γ∞ (i.e., β∞ and S∞, see Eqs. 1 and 2) and mole-based LLE are also performed by COSMO-RS calculations. For screening ILs as extraction solvents, all the involved thermodynamic calculations are carried out at 298.15 K and atmospheric pressure, and the thermodynamic parameters are correspondingly obtained. The thermodynamic criteria in the mole-based LLE are determined in a similar manner as those in the mass-based LLE (see Eqs. 4 6), where the mole equilibria compositions are used instead. The COSMO-RS predicted results based on these three different sets of criteria are given in Tables S2 - S3, and the ILs prescreened correspondingly are tabulated in Table S4 (Supporting Information). Table 1 compares the IL screening results by the three different sets of thermodynamic criteria. As seen, different numbers of ILs are screened out on the basis of γ∞-based and mole-based LLE criteria. To be specific, a remarkable number of ILs suggested by the mole-based LLE criteria are not included in those suggested by the γ∞-based criteria, and vice versa (see Table S4a, Supporting Information). Moreover, no phase splitting is found for 1217 ILs by the LLE calculation (see Tables S3, Supporting Information). These ILs are certainly not applicable as solvent for the EDS process, whereas they cannot be found out by the γ∞-based criteria. It should also be mentioned that the SL of different ILs in the extraction can be determined by LLE calculation in terms of the IL concentration in the raffinate phase. This criterion also excludes several IL candidates due to their higher SL than sulfolane, which again are not taken into account in the γ∞-based screening. All these facts verify that the screening result based on infinite dilution properties does differ from that suggested by the practical LLE calculation.


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Besides, as also compared in Table 1, only a small proportion of the ILs identified by the molebased LLE criteria possess a higher mass-based β and S, and lower mass-based SL than sulfolane. For instance, the mole-based β and S of the IL [BMPY][TF2N] are 4.758 and 100.921, respectively, which are notably higher than those (2.205 and 71.154) of sulfolane; while the converted mass-based β of the IL is only 1.310, which is much lower than that (2.100) of sulfolane. From the engineering point of view, only the ILs with higher mass-based extraction performance are desirable. The large number of ILs that present only better mole-based, but not mass-based EDS performance than sulfolane clearly demonstrates the significance of mass-based screening criteria. From the above, it can be concluded that the employed thermodynamic criteria derived from the mass-based LLE are more rational and efficient for finding the practically promising ILs from a large number of IL candidates. The 831 ILs pre-screened based on the proposed criteria are listed in Table S4c (Supporting Information).

Step 2: Further screening by physical property constraints In the second step, the melting point and viscosity of the 831 ILs that satisfy the thermodynamic criteria are estimated by the GC methods (Eqs. 7 and 8) to further screen out potential ILs with desired properties for the EDS process. The upper bound of IL melting temperature is set to 298.15 K and 163 of the preselected 831 ILs meet this melting point constraint (see Table S5, Supporting Information). The constraint of viscosity lower than 100 cP at 298.15 K is applied to make sure that the selected ILs have relatively low viscosity. As a result, 15 of the 163 ILs satisfy the viscosity constraint. These 15 ILs are listed in Table 2 together with their molecular weights (MW), mass-based LLE results (β, S and SL), as well as Tm and η. As seen, the MWs of all the 15 ILs are less than 250. These ILs are based on four different kinds of anions, i.e., lactate, dicyanamide, thiocyanate and formate, which are generally of strong hydrogen bond acceptor ability. ILs with such anions are also implied as potential solvent for EDS by some experimental studies.4,43,44 It should be mentioned that the Tm and η of some ILs that satisfy the thermodynamic criteria cannot be evaluated since they contain groups not involved in the employed GC models (see Table S6, Supporting Information). Despite the fact that these ILs may also meet the physical property constraints, they are discarded in this step. Nevertheless, these ILs can be further



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evaluated when the current GC models are extended or their physical properties are available from experiments. Theoretically, regardless of the order of the first two steps, the same set of ILs can be identified if the GC physical property models have covered all IL types involved in the COSMO-RS based LLE calculation. Here, the LLE calculation is set as the first step and the physical property estimation is set as the second step, which is mainly due to the following two considerations: (a) all ILs with satisfying LLE performance can still be suggested whether their properties can be predicted by the GC models or not; (b) by COSMO-RS based LLE calculation, the number of ILs remained in the physical property estimation step is significantly reduced.

Step 3: Final selection by process simulation and evaluation It is well known that both sulfur and nitrogen compounds are undesired impurities in fuels. Since ILs are generally nitrogen- and/or sulfur-containing, ILs with a low solubility in the raffinate phase are strongly favorable for the EDS process to avoid the potential contamination of fuel by ILs.7,8,15,45 Based on this consideration, the top 4 ILs (i.e., IL1, IL2, IL3, and IL4) presenting the lowest mass-based SL are selected from the 15 candidates in Table 2 for further process simulation and evaluation. As described in Section 2.3, the normal boiling temperature, density and molecular weight of ILs need to be specified for simulating IL-involved process in Aspen Plus. The calculated normal boiling point and density of IL1, IL2, IL3, and IL4 are listed in Table S7 (Supporting Information). The NRTL parameters for the ternary systems {n-octane + thiophene + IL/sulfolane} are regressed from the COSMO-RS predicted LLE data at five different temperatures from 288.15 K to 328.15 K. The root mean square deviation (rmsd) between the COSMO-RS predicted and NRTL correlated LLE compositions is determined by 1/2

  exp calc 2 rmsd = ∑∑∑ ( xilm − xilm ) / 6n   i l m 

(9)

where the subscripts i, l, m denote the component, the phase, and the tie-line, respectively; n represents the total number of tie-lines. The regressed binary parameters and the corresponding rmsds of these systems are listed in Table S8 (Supporting Information). The estimated rmsds of the IL1, IL2, IL3, IL4, and sulfolane systems are 0.0159, 0.0056, 0.0059, 0.0049, 0.0037, respectively, which indicate a high model regression quality.


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With the regressed NRTL model, a continuous IL-based EDS process is simulated in Aspen Plus V8.8. For comparison, a corresponding process with sulfolane as the solvent is also evaluated. These two different process flowsheets are depicted in Figure 2, where the extractor, evaporator, and distillation column are modeled by the Extract block, Flash block, and RadFrac block, respectively. In these processes, the model fuel (10,000 kg/h) composed of thiophene and noctane with an initial sulfur content of 100 ppm is fed to the bottom of the extractor. The objective of these separation processes is to reduce the sulfur content down to a level < 10 ppm. As shown in Figure 2a, except the extraction column, only an evaporator is required for IL regeneration. The regenerated IL is then recycled to the top of the extraction column, together with a certain amount of fresh IL. In comparison, two distillation columns are needed in the sulfolane-based process. One is for the removal of the dissolved sulfolane in the raffinate fuel phase and the other is for sulfolane regeneration (see Figure 2b). The simplicity of the IL-based process can be attributed to the negligible vapor pressure of ILs and the extremely low IL solubility in the raffinate stream (at the magnitude of 10-5 - 10-6, see Table S9 in Supporting Information). The key operating conditions of the extraction column and the evaporator in the IL process are optimized by performing a sensitivity analysis. In order to fulfill the desulfurization specification, the required number of stages in the extraction column and the corresponding mass-based solvent-to-feed ratio (S/F) for the studied solvents are determined and plotted in Figure 3. As depicted, when increasing the number of extraction stages from 2 to 8, the required amount of solvent in all the five cases notably decreases whereas after that the S/F remains almost unchanged. Thus, the number of stages is determined to be 8 in all the processes. With the same extraction stage, the required mass ratios of S/F for ILs are notably lower than that of sulfolane, which agree well with the thermodynamic criteria of mass-based LLE. The IL regeneration requirement is to remove at least 98.0 wt% of thiophene in the sulfur-loaded IL stream (S5) into the S6 stream as residual liquids (see Figure 2a). In order to meet this specification, the required operating pressure as a function of the operating temperature in the evaporator is illustrated in Figure 4. As shown, different dependencies of the operating pressure on the temperature are observed for the studied ILs. For instance, when the evaporator is operated at 423.15 K, the required pressures for the four ILs are 0.333 bar, 0.197 bar, 0.108 bar, and 0.051 bar, respectively. Under the same operating pressure, the required temperature for the



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ILs can be ranked as: IL1 < IL2 < IL3 < IL4. To avoid the utilization of vacuum pump, the evaporators in all the IL-based EDS processes are operated at atmospheric pressure. The operating conditions of the two distillation columns in the sulfolane-based process are also roughly optimized. The final operating conditions as well as the main process simulation results of the IL-based and sulfolane-based processes are summarized in Table 3. The detailed simulation results in each case are provided in Table S9 (Supporting Information), where the mass flow, the mass composition, the energy information, as well as the temperature and pressure for all streams are included. As shown, the low-sulfur fuel products obtained from all the processes satisfy the qualification of sulfur content (< 10 ppm). Compared to the sulfolane case, the IL processes have slightly higher fuel recovery ratios. The consumptions of ILs are significantly lower than that of sulfolane in the continuous process. For instance, the makeup of IL1 is 0.05 kg/h, which is only 0.56% of the corresponding sulfolane consumption. The slight IL consumption mainly goes to the residual liquids (S6) at the top of the evaporator. Moreover, the energy consumptions of the IL processes are also much lower than that of the sulfolane process. Specifically, the heat duties of the evaporator in the four IL processes are 290.22 kW, 348.78 kW, 455.98kW and 365.56kW, respectively; while the total energy consumption for the sulfolane process is 2554.84 kW (1810.51 kW for the distillation column B2 and 744.33 kW for the distillation column B3). Besides the very small solvent and energy consumption, it could be expected that the total capital investment of the IL-based process can also be considerably reduced because of the much simplified process. Based on the above discussions, the selected ILs are indeed very promising solvents for the EDS process from the industrial point of view. In view of the same flowsheet of the IL-based processes, the performances of ILs are ranked as IL1 > IL2 > IL4 > IL3 according to their energy and solvent consumptions. Such a ranking is different from those based on the thermodynamic criteria (for example, the ranking of their β is IL4 > IL1 > IL2 > IL3 and the ranking of their S is IL4 > IL2 > IL1 > IL3). This fact verifies the importance of employing process performance as the final evaluation criterion for IL screening. Overall, IL1 and IL2 are identified as the most promising ILs for the EDS process.


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CONCLUSION A systematic method for screening ILs as extraction solvents is presented and illustrated for the extractive desulfurization of the gasoline fuel component n-octane. ILs with higher mass-based distribution coefficient (β), selectivity (S), as well as lower solvent loss in the raffinate (SL) than the conventional solvent sulfolane, are first found by COSMO-RS liquid-liquid equilibrium (LLE) calculation. Through the comparison of the screening results from different sets of COSMO-RS predicted thermodynamic criteria, the β, S, and SL derived from mass-based LLE are demonstrated to be more reasonable and efficient than the commonly used γ∞-based criteria for prescreening potential ILs from a large number of IL candidates. Subsequently, the melting point and viscosity of the prescreened ILs are evaluated by group contribution models, and those unsatisfying the physical property constraints are discarded. Finally, the remaining superior ILs are introduced to Aspen Plus and their performance in the continuous extraction process is compared. Two most promising ILs are finally identified, which possess much smaller solvent and energy consumptions than the benchmark solvent sulfolane in the process. The proposed method could be easily extended to screen practically attractive IL solvents for other extraction applications.

ACKNOWLEDGEMENTS This research is supported by the National Natural Science Foundation of China (NSFC U1462123), Major State Basic Research Development Program of China (973 Program 2012CB720502), PetroChina Innovation Foundation, Fundamental Research Funds for the Central Universities of China, and 111 Project (B08021). Moreover, Zhen Song acknowledges the financial support of China Scholarship Council for his joint PhD program (No. 201506740031) in Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg/Germany.

ASSOCIATED CONTENT Supporting Information associated with this work is available via the internet http://www.***.



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Information of all cations and anions, γ∞ and LLE prediction results by COSMO-RS, IL screening result by different thermodynamic criteria, ILs screening results by physical property constraints, predicted physical properties, correlated NRTL parameters for process simulation and detailed simulation results in each process (xlsx).

References (1) Brennecke, J. F.; Maginn, E. J. Ionic liquids: innovative fluids for chemical processing. AIChE J. 2001, 47, 2384-2389. (2) Zhang, X. P.; Zhang, X. C.; Dong, H. F.; Zhao, Z. J.; Zhang, S. J.; Huang, Y. Carbon capture with ionic liquids: overview and progress. Energy Environ. Sci. 2012, 5, 6668-6681. (3) Meindersma, G. W.; Podt, A. J.; de Haan, A. B. Selection of ionic liquids for the extraction of aromatic hydrocarbons from aromatic/aliphatic mixtures. Fuel Process. Technol. 2005, 87, 59-70. (4) Domańska, U.; Królikowska, M. Measurements of activity coefficients at infinite dilution in solvent mixtures with thiocyanate-based ionic liquids using GLC technique. J. Phy. Chem. B

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(24) Klamt, A.; Eckert. F. COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilibr. 2000, 172, 43-72. (25) Diedenhofen, M.; Eckert, F.; Klamt, A. Prediction of infinite dilution activity coefficients of organic compounds in ionic liquids using COSMO-RS. J. Chem. Eng. Data 2003, 48, 475479. (26) Ferreira. A. R.; Freire. M. G.; Ribeiro. J. C.; Lopes. F.M.; Crespo. J. G.; Coutinho. J. A. P. An overview of the liquid-liquid equilibria of (ionic liquid + hydrocarbon) binary systems and their modeling by the conductor-like screening model for real solvents, Ind. Eng. Chem. Res. 2011, 50, 5279-5294. (27) Ferreira. A. R.; Freire. M. G.; Ribeiro. J. C.; Lopes. F. M.; Crespo. J. G.; Coutinho. J. A. P. Overview of the liquid-liquid equilibria of ternary systems composed of ionic liquid and aromatic and aliphatic hydrocarbons, and their modeling by COSMO-RS, Ind. Eng. Chem. Res. 2012, 51, 3483-3507. (28) Anantharaj, R.; Banerjee, T. Aromatic sulfur-nitrogen extraction using ionic liquids: Experiments and predictions using an a priori model. AIChE J. 2013, 59, 4806-4815. (29) Marciniak, A. Influence of cation and anion structure of the ionic liquid on extraction processes based on activity coefficients at infinite dilution. A review. Fluid Phase Equilibr.

2010, 294, 213-233. (30) Lazzús, J. A. A group contribution method to predict the melting point of ionic liquids. Fluid Phase Equilibr. 2012, 313, 1-6. (31) Lazzús, J. A.; Pulgar-Villarroel, G. A group contribution method to estimate the viscosity of ionic liquids at different temperatures. J. Mol. Liq. 2015, 209, 161-168. (32) Ferro, V. R.; Ruiz, E.; De Riva, J.; Palomar, J. Introducing process simulation in ionic liquids design/selection for separation processes based on operational and economic criteria through the example of their regeneration. Sep. Purif. Technol. 2012, 97, 195-204. (33) Kulajanpeng, K.;Suriyapraphadilok, U.; Gani, R. Systematic screening methodology and energy efficient design of ionic liquid-based separation processes. J. Clean Prod. 2016, 111, 93-107. (34) Eckert. F.; Klamt. A. COSMOtherm, Version C3.0, Release 16.01; COSMOlogic GmbH & Co. KG, Leverkusen, Germany, 2015.


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benzene and thiophene by extractive distillation. AIChE J. 2015, 61, 4470-4480. (40) Valderrama, J. O.; Forero, L. A.; Rojas, R. E. Critical properties and normal boiling temperature of ionic liquids. Update and a new consistency test. Ind. Eng. Chem. Res. 2012, 51, 7838-7844. (41) Palomar, J.; Ferro, V. R.; Torrecilla, J. S.; Rodríguez, F. Density and molar volume predictions using COSMO-RS for ionic liquids. An approach to solvent design. Ind. Eng. Chem. Res. 2007, 46, 6041-6048. (42) Sandler, S. I. Using Aspen Plus in thermodynamics instruction: a step-by-step guide, first ed. John Wiley & Sons, 2015. (43) Lu, X.; Yue, L.; Hu, M.; Cao, Q.; Xu, L.; Guo, Y.; Hu. S.; Fang, W. Piperazinium-based ionic liquids with lactate anion for extractive desulfurization of fuels. Energ. Fuel. 2014, 28, 1774-1780. (44) Asumana, C.; Yu, G.; Li, X.; Zhao, J.; Liu, G.; Chen, X. Extractive desulfurization of fuel

oils with low-viscosity dicyanamide-based ionic liquids. Green Chem. 2010, 12, 2030-2037. (45) Song, Z.; Zeng, Q.; Zhang, J.; Cheng, H., Chen, L.; Qi, Z. Solubility of imidazolium-based ionic liquids in model fuel hydrocarbons: A COSMO-RS and experimental study. J. Mol. Liq. 2016, 224, 544-550.



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Table captions Table 1. IL screening results of step 1 by different sets of thermodynamic criteria (totally 36260 IL candidates). Table 2. 15 ILs selected after the first two steps by the mass-based LLE criteria and physical property constraints (with sulfolane as the benchmark). Table 3. Main results from the simulation of the IL-based and sulfolane-based EDS processes


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Table 1. IL screening results of step 1 by different sets of thermodynamic criteria (totally 36260 IL candidates). γ∞

mole-based LLE

β∞/β

28089

27584

mass-based LLE (criteria proposed in this work) 3144

S∞/S

10349

11293

834

−/SL not considered 11265 831 Note: The numbers of satisfied ILs are determined based on the corresponding thermodynamic parameters of the benchmark solvent sulfolane predicted by COSMO-RS.



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Table 2. 15 ILs selected after the first two steps by the mass-based LLE criteria and physical property constraints (with sulfolane as the benchmark). IL IL1 IL2 IL3 IL4 IL5 IL6 IL7 IL8 IL9 IL10 IL11 IL12 IL13 IL14 IL15

Cation Anion MW (g/mol) Mass-based β Mass-based S 1-(3-methoxypropyl)pyridinium formate 197.23 2.656 104.027 1-ethyl-3-methylpyridinium lactate 211.26 2.305 119.576 1,1-dimethyl-pyrrolidinium lactate 189.25 2.266 77.336 1,2,3-trimethylimidazolium lactate 200.24 3.021 134.940 1-ethyl-3,4-dimethylimidazolium lactate 214.26 2.441 96.059 1-ethyl-2-3-methyl-imidazolium lactate 214.26 2.628 104.160 1,3-diethyl-4-methylimidazolium lactate 228.29 2.500 75.790 1-butyl-2-methylpyridinium lactate 239.31 2.109 89.159 triethylsulfonium lactate 208.32 2.542 79.036 1-propyl-2-3-methyl-imidazolium lactate 228.29 2.334 85.263 1,2-diethyl-3-methylimidazolium lactate 228.29 2.645 84.054 1-butyl-2-3-methyl-imidazolium lactate 242.32 2.160 72.949 1-ethyl-1-methyl-pyrrolidinium thiocyanate 172.29 2.592 135.044 triethylsulfonium thiocyanate 177.33 2.483 205.514 1,2-diethyl-3-methylimidazolium dicyanamide 205.26 2.154 273.529 sulfolane 120.17 2.100 71.154 Note: Mass-based β, S and SL are calculated from LLE at 298.15 K. η is also estimated at 298.15 K.


Mass-based SL Tm (K) η (cP) 2.14×10-6 281.14 88.55 -5 2.56×10 253.01 64.73 -5 286.08 71.31 2.85×10 -5 3.02×10 268.80 53.95 -5 3.45×10 265.04 62.96 -5 5.69×10 265.04 62.96 -5 261.28 73.47 7.08×10 -5 8.28×10 245.50 88.16 -5 8.74×10 218.16 59.27 -5 9.44×10 261.28 73.47 -4 1.12×10 261.28 73.47 -4 1.40×10 257.52 85.75 -4 1.40×10 270.95 81.78 -4 1.46×10 270.74 58.25 -4 2.14×10 288.42 32.23 -3 9.09×10

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Table 3. Main results from the simulation of the IL-based and sulfolane-based EDS processes. Extraction column (1 bar, 298.15 K, Ns = 8)

Solvent

Solvent consumption (kg/h)

Solvent in recycle (kg/h)

IL1

0.05

4089.82

IL2

0.18

4289.74

IL3

1.53

4978.17

IL4

0.48

3669.60

Regeneration unit (1 bar) Unit operation

evaporator

Low-sulfur fuel product Fuel recovery ratio

Operating conditions

Energy consumption (kW)

Sulfurcontent (ppm)

447.15 K

290.22

8.88

98.93%

465.15 K

348.78

8.83

99.31%

478.15 K

455.98

8.89

98.63%

501.15 K

365.56

9.89

99.28%

9.87

98.55%

sulfolane 8.87 5417.35 distillation a 2554.84 a. B2: reflux ratio = 0.35, distillate/feed = 0.98, number of stages = 10, feed stage = 9; B3: reflux ratio = 0.01, distillate/feed = 0.04, number of stages = 5, feed stage = 2.



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Figure captions Figure 1. Schematic diagram of the proposed method for the screening of ILs as extraction solvents.

Figure 2. Continuous EDS processes with ILs (a) and sulfolane (b) as the solvent. EC: extraction column; Evap: evaporator; DC: distillation column

Figure 3. Mass-based solvent-to-feed ratio (S/F) plotted with the number of stages in the extraction column to meet the desulfurization requirement.

Figure 4. Operating pressure plotted with the temperature for the regeneration of IL in the evaporator.


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23

Figure 1. Schematic diagram of the proposed method for the screening of ILs as extraction solvents.

COSMO-RS activity coefficient model (NRTL)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

thermodynamic properties LLE (mass-based β, S, SL) conventional solvent as benchmark

physical properties GC methods

(Tm, η) Tm < 298.15 K η < 100 cP

process performance Aspen plus

(operating conditions, solvent/energy consumption)



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Figure 2. Continuous EDS processes with ILs (a) and sulfolane (b) as the solvent. EC: extraction column; Evap: evaporator; DC: distillation column. (a)

(b)


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25

Figure 3. Mass-based solvent-to-feed ratio (S/F) plotted with the number of stages in the extraction column to meet the desulfurization requirement.

1.8

IL1 IL2 IL3 IL4 Sulfolane

1.6 1.4

Mass ratio of S/F

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1.2 1.0 0.8 0.6 0.4 0.2 2

3

4

5

6

7

Number of stages


8

9

10


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26

Figure 4. Operating pressure plotted with the temperature for the regeneration of IL in the evaporator.

1.0

IL1 IL2 IL3 IL4

0.8

Pressure (bar)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

0.6

0.4

0.2

0.0 380

400

420

440

460

480

Temperature (K)


500

520

540

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1

A systematic method for screening ionic liquids as extraction solvents exemplified by an extractive desulfurization process Zhen Song, Teng Zhou*, Zhiwen Qi*, Kai Sundmacher Corresponding authors: [email protected] (Teng Zhou); [email protected] (Zhiwen Qi)

2

m

Table of Contents (TOC) Graphic

Total Composition

m3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


m1 Liquid-liquid equilibria COSMO-RS

Physical properties GC Methods

Process performances Aspen Plus

Synopsis A systematic method is proposed for screening ionic liquids as extraction solvents using the extractive desulfurization process as an example.


Systematic Method for Screening Ionic Liquids as Extraction

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