University of Groningen
Computer-aided Ionic Liquids Design for Separation Processes Peng, Daili
DOI:
10.33612/diss.168550903
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Publication date: 2021
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Peng, D. (2021). Computer-aided Ionic Liquids Design for Separation Processes. University of Groningen. https://doi.org/10.33612/diss.168550903
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Chapter 5
A hierarchical hybrid method for screening ionic liquid
solvents for extractions exemplified by the extractive
desulfurization process
Abstract
A hierarchical hybrid method is proposed to screen practically suitable ionic liquid (IL) solvents for different extraction tasks. 47424 infinite dilution activity coefficients (IDAC) data including 12 IL families and 13 organic families are collected from literature and first used to assess the potential extraction performance of IL solvents. Moreover, the melting point, viscosity, and toxicity of the prescreened ILs are evaluated by quantitative structure-property relationships (QSPR) methods. The ILs with potentially high extraction performance and meeting the physical properties criteria are selected to perform liquid-liquid equilibrium (LLE) experiments. Subsequently, process simulation and evaluation using the selected IL solvents are performed by Aspen Plus. To exemplify the proposed method, the extractive desulfurization (EDS) process is taken as a case study, where [EMIM][MESO3] (1-ethyl-3-methylimidazolium methanesulfonate) and [EIM][NO3] (1-ethylimidazolium nitrate) are selected after IDAC database searching and QSPR analysis. Experimental LLE with the two ILs are determined and correspondingly fitted to the NRTL model. Based on the obtained NRTL model, two processes using the screened ILs and sulfolane are developed and compared using Aspen Plus, demonstrating the much higher performances of the ILs regarding process complexity, solvent consumption, and energy usage.
This chapter is based on D. Peng, A. J. Kleiweg, J.G.M. Winkelman, Z. Song, and F. Picchioni, A hierarchical hybrid method for screening ionic liquid solvents for extractions exemplified by the extractive desulfurization process, ACS Sustain. Chem. Eng. 2021, 9, 7, 2705-2716.
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1. Introduction
Due to their favorable thermophysical properties, ionic liquids (ILs) are widely regarded as promising solvents in various separation processes, such as gas capture1-5, extraction6-8, and extractive distillation9,10. Especially in the extraction process, ILs have been applied in many different areas, e.g., separation of aromatic and aliphatic hydrocarbons11-13, purification of drugs and biomolecules14,15, desulfurization and denitrogenation of fuel oils16-19. Compared to the traditional organic solvents, IL solvents have a negligible vapor pressure that makes them unlikely to evaporate to the environment to cause pollution and solvent loss as well as ease the solvent regeneration20. However, there are approximately 1018 anion-cation combinations that could be potentially synthesized21, making the selection of suitable IL solvent very difficult. To avoid the labor-intensive liquid-liquid equilibrium (LLE) experiments for the extraction process, the extraction distribution coefficient (Eq. 1) and selectivity (Eq. 2) at the infinite dilution condition are usually employed as the preliminary performance measure of ILs22:
𝛽 = (1)
𝑆 = (2)
where 𝛾 and 𝛾 represent the infinite dilution activity coefficients of solute 1 and solute 2 in IL, respectively; 𝛽 stands for the solute distribution coefficient of solute 1 and 𝑆 denotes the extractive selectivity.
To acquire the infinite dilution activity coefficients (IDAC) for the solutes in ILs, various methods can be adopted. The classical activity coefficient models, such as the universal functional activity coefficient (UNIFAC)23,24, non-random two-liquid model (NRTL)25, and models from statistical associating fluid theory such as perturbed-chain statistical associating fluid theory (PC-SAFT)26, are often used to predict the IDAC. However, to enable accurate predictions, these models need several molecule-specific
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and mixing parameters from the regression of experimental data. This means that the application domain is essentially confined by the availability of experimental data. In contrast, as a theoretical approach combining principles of quantum chemistry and molecular thermodynamics, the conductor-like screening model (COSMO) based methods namely COSMO-RS27 and COSMO-SAC28 have been demonstrated to be powerful tools for a priori selection of IL solvents in various separation problems. However, for the IL-involved systems, the accuracy of the COSMO-based models for the prediction of IDAC is inferior to other models such as UNIFAC due to its fully predictive character29.
Another avenue to acquire IDAC values is by experiments. Since ILs have negligible vapor pressure, the IDAC of solutes can be measured using the gas-liquid chromatography (GLC) method developed by Everett30 and Cruickshank et al.31. Although the experimental method is more laborious, the results are the most reliable. Moreover, compared to the ILs screened from theoretical databases (e.g., COSMO-RS database) that may be hard to buy or synthesize, the ILs that have been involved in IDAC studies are more practical and easily obtainable. In recent years, a large amount of IDAC data between various organic solutes and ILs have been reported in the literature, covering typical IL families such as imidazolium, pyrrolidinium, pyridinium, piperidinium, morpholinium, quinolinium, ammonium, phosphonium, sulfonium, etc. That is to say, the IDAC database from the literature could already provide a considerable space to screen practical IL solvents for extraction tasks.
In addition to the performance measure from IDAC, pure-component properties of ILs such as melting point and viscosity are also very important to the extraction process32. A suitable IL solvent should possess a low melting point and viscosity to facilitate the practical application. Besides, the potential hazards of ILs for the environment and human being are being gradually recognized33, however, this characteristic was rarely considered in previous IL screening or design studies18,34-36.
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To find out environmentally friendly ILs, an estimation of the potential toxicity of them is also of high value.
Once suitable ILs are selected after preliminarily assessing the IDAC and pure-component properties, it is necessary to perform corresponding LLE experiments to validate their performance under practical extraction conditions. However, most available IL screening and design works based on IDAC have neglected the experimental validation. Meanwhile, the experimental LLE data can be also used to accurately regress thermodynamic models such as NRTL, which subsequently allows for reliable process simulation and evaluation. In previous studies that have involved the process evaluation of IL performances for solvent screening or design, the common practice is to directly use the UNIFAC or COSMO-SAC models as the thermodynamic method in process simulation. Nevertheless, despite the convenience of this approach, the process evaluation results may deviate greatly from the real situation due to high sensitivity to the thermodynamic model accuracy. In this sense, thermodynamic models specifically regressed from experimental LLE data could essentially overcome this problem18.
In this work, as shown in Fig. 1, a hybrid IL screening method that combines the extraction performance estimation, physical property estimation, LLE experimental validation, and process evaluation is developed. In the beginning, a database covering the information of IL and solute name, classification, IDAC, temperature, and corresponding references is built. Then, promising ILs are searched in the database based on the IDAC-derived performance measure. Afterwards, the physical properties namely melting point, viscosity, and toxicity of the IL candidates are evaluated using either experimental data or semi-empirical models. ILs with both potential good extraction performance and favorable physical properties are chosen to perform the LLE experiments. Finally, based on the NRTL model regressed from the LLE data, the continuous extraction process using the selected IL solvents is built and compared with the benchmark process using conventional solvents. To demonstrate the method, the
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extractive desulfurization (EDS) of fuel oils, one of the most extensively studied IL-based extractions in the literature, is taken as a case study.
Fig. 1. Hybrid IL screening method proposed in this work.
2. Method details
2.1. IDAC database collection
The database covering 47424 IDAC data points is built, and the detailed information including the name of IL and solute, classification, IDAC value, temperature, and references are collected37-232. The combinations of involved organic solutes and ILs as well as the number of data points are shown in Fig. 2. As only 11 out of 156 combinations are unavailable in the database, it can be used for screening ILs for many typical extraction tasks such as alcohols/alkanes, aromatics/cycloalkanes, and desulfurization of fuel oils, and so on. The imidazolium family is the most extensively studied since it accounts for a large portion of the data points while other IL families have a quite even distribution. It is worth noticing that the database can be easily expanded in the future when more IDAC data are available.
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Fig. 2. Number of data points among organic solutes and ILs in the database. 2.2. Searching for suitable ILs
Because the distribution coefficient is always inversely related to the selectivity, the overall performance of solvents could be evaluated by the performance index (𝑃𝐼 ), which is the product of the selectivity and the distribution coefficient at infinite dilution (Eq. 3). 𝑃𝐼 has been used in many IL screening problems and proved to be a suitable, combined performance measure20,233-236.
𝑃𝐼 = 𝛽 × 𝑆 (3)
To identify the optimal IL solvents for a specific separation problem from the large database, a program in Matlab (R2019b) is developed to sort the 𝑃𝐼 of ILs in the IDAC database for a given separation task. Because many experiments are not performed at exactly the same temperature, the tolerance for the temperature is set to ±1 K.
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In addition to the thermodynamic performance measure, the desired IL solvents should possess a melting point lower than the operating temperature and possess a relatively low viscosity (e.g. < 150 cP). Moreover, the half-maximal effective concentration (𝐸𝐶50) of the biological endpoints of Leukemia Rat Cell Line (IPC-81), which is frequently considered in cytotoxicity assessments237-239, is employed to estimate the toxicity of ILs in this work. According to the UFT research center (center for environmental research and sustainable technology), log 𝐸𝐶50 < 2 means the ILs have high toxicity, hence the constraint for toxicity is set as log 𝐸𝐶50 > 2. It should be noted that in order to ensure the accuracy of these properties, the experimental data are used whenever possible; otherwise, the QSPR methods240-242 from the literature are applied.
2.3. LLE experiments and NRTL correlation
A mixture containing 5 wt% thiophene, 45 wt% heptane/octane, and 50 wt% IL is weighted and added to a 10 mL round bottom flask with a cap covered in parafilm to avoid chemical loss. The measurements are performed in grams and the total composition is set to 4 g using an analytical balance (Mettler AE200) with the readability of ±0.0001 g. The same procedure is carried for higher concentrations of thiophene (i.e. 10 wt%, 15 wt%, 20 wt%, 25 wt%, and 30 wt%) in the feed while keeping 50 wt% of the ionic liquid. To ensure complete mixing, the samples are put in an incubator shaker and shaken for 6 hours at 200 strokes per minute. For reaching thermodynamic equilibrium at 298.15 K, the vials are left to settle overnight at isothermal conditions in a water bath equipped with Julabo F25-ED refrigerated and heating circulator, which has a stability of ±0.03 K.
Samples are carefully withdrawn with syringes from the hydrocarbon-rich layer and IL-rich layer and then determined by gas chromatography (GC) and nuclear magnetic resonance (NMR), respectively. The samples from the hydrocarbon-rich layer are firstly confirmed to be free of ILs by H-NMR analysis using Varian Mercury Plus
140
400. The GC equipment used is the Agilent Technologies 7890B with a flame ionization detector (FID) and an Agilent Technologies DB-5 (15 m × 0.32 mm × 0.25 μm). The IL-rich layer could not be subjected to GC analysis due to the negligible vapor pressure, therefore, the samples are prepared by dissolving a drop in ±0.7 mL of deuterated methanol placed inside an NMR tube and analyzed by Varian Mercury Plus 300 or Bruker Avance 600 spectrometer. The average uncertainty on the mole fraction of the GC and H-NMR analysis is estimated to be ±0.003. The detailed description of the analysis procedures can be found elsewhere234,243.
The Hand244 and Othmer-Tobias245 correlations are employed to check the consistency of the experimental results
ln = 𝑎 + 𝑏 ln (4)
ln = 𝑐 + 𝑑 ln (5)
where 𝑥 , 𝑥 and 𝑥 stand the molar concentrations of IL, thiophene, and hydrocarbon, respectively. 𝑤 and 𝑤 represent the weight concentrations of IL and hydrocarbon, respectively. The superscripts E and R represent the extract and raffinate phase, respectively. The parameters a, b, c, and d are fitted using the experimental data, and the linearity of the results (i.e. the value of 𝑅 close to 1) indicates the consistency of the ternary liquid-liquid extraction tie lines.
The NRTL model246 is used to correlate the experimental LLE data, where the non-randomness parameters 𝛼 are all set to 0.2. The binary interaction parameter Δg are regressed by minimizing Eq. 6 and the fitting accuracy is estimated by root-mean-square deviation (RMSD):
OF = ∑ ∑ ∑ 𝑥 − 𝑥 (6)
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where 𝑥 and 𝑥 denote the experimental and calculated mole fractions, respectively; the subscripts m, n, and k represent the tie-line, phase, and component, respectively; N is the number of data points.
2.4. Process simulation
From the practical point of view, it is highly desirable that the optimal solvent for an extraction process is consequently finally identified based on the highest performance in a continuous process. As discussed earlier in literature20, the process simulation based on IL solvents is challenging because of two reasons: (1) ILs are not included in the component databanks of commercial chemical process simulator (e.g., Aspen Plus); (2) the parameters of the thermophysical models are not available for the IL-involved systems.
In this work, Aspen Plus V11 is used to simulate the extraction process using IL solvents, and the first problem can be solved by defining ILs as pseudocomponents. To address the second problem, the NRTL model is regressed from the experimental LLE data and introduced into Aspen Plus as the thermodynamic method. Moreover, the parameters of the build-in models in Aspen Plus for physical properties are regressed using experimental data or estimated value by the methods listed in Table 1. This component definition approach has been proved to be reliable for the simulation of IL-containing processes of aromatic/aliphatic hydrocarbons extraction247,248, thiophene/benzene extractive distillation249, and CO
2 absorption250. Table 1. Prediction models for physical properties of ILs
Physical property Data points AARD (%) Ref.
Viscosity 1974 30.2 251 Toxicity 127 4.50 242 Density 1999 0.90 252 Heat capacity 128 5.80 253 Surface tension 162 1.17 254 Thermal conductivity 55 1.70 255 Critical properties 1130 - 256
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3. Case study for extractive desulfurization
3.1. ILs screening results
The proposed method is demonstrated with the case study of the EDS process at 298.15 K, where {thiophene + heptane or octane} is taken as model fuel oils.
Based on the screening method introduced above, the ILs in the IDAC database are sorted according to their 𝑃𝐼 . However, due to the insufficient number of experimental IDAC data at 298.15 K, the screening results under 318 K are taken into account instead, and accordingly, 67 ILs are picked out from the IDAC database. For the thiophene/heptane system, [EMIM][NO3] (1-ethylimidazolium nitrate) and [EMIM][MESO3] (1-ethyl-3-methylimidazolium methanesulfonate) are the top two candidates with 𝑃𝐼 are 240.2 and 153.1, respectively. For the thiophene/octane system, [EMIM][NO3] is still the best with a 𝑃𝐼 of 331.8 and [EMIM][MESO3] also owns a high 𝑃𝐼 (137.4). Therefore, [EMIM][NO3] and [EMIM][MESO3] are first selected as promising IL candidates from the IDAC database.
In terms of pure-component physical properties, the melting point of [EMIM][MESO3] is below 298.15 K257 while that of [EMIM][NO3] is higher than 298.15 K258. Considering its high 𝑃𝐼 , [EIM][NO
3] with melting point below 298.15 K259 is taken as a reasonable substitute of [EMIM][NO
3]. The structure of the selected ILs [EMIM][MESO3] and [EIM][NO3] are shown in Fig. 3 with their physical properties listed in Table 2. Both the two ILs also meet the criteria of a viscosity below 150 cP and llog 𝐸𝐶50 larger than 2. It should be noted that, for [EMIM][MESO3], the viscosity, density, heat capacity, surface tension, and thermal conductivity are experimental values260-263 while other properties in Table 2 are estimated by the methods listed in Table 1.
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Fig. 3. Structure of (a) [EMIM][MESO3] and (b) [EIM][NO3]. Table 2. Physical properties of [EMIM][MESO3] and [EIM][NO3].
Physical properties [EMIM][MESO3] [EIM][NO3]
Viscosity/cP 135.00 116.00
Density/kg m-3 1242.60 1221.28
Heat capacity/J mol-1 K-1 336.41 248.73
Surface tension/N m-1 0.05 0.05 Thermal conductivity/W m-1 K-1 0.21 0.18 Tb/K 667.38 610.18 MW/g mol-1 206.27 159.15 ω 0.33 0.60 Tc/K 1026.03 871.46 Pc/bar 48.13 39.95 Zc 0.33 0.26 Vc/cc mol-1 587.06 467.31 Toxicity/logEC50 4.08 4.34 3.2. Experimental validation
[EMIM][MESO3] (≥ 98%) and [EIM][NO3] (≥ 98%) are purchased from IoLiTec. Heptane (≥ 99%) and octane (≥ 99%) are purchased from BoomLab and Merck KGaA, respectively, and thiophene (≥ 99%) is purchased from Sigma Aldrich. The ILs are dried for 72 h at T = 378 K under vacuum to remove volatile impurities and trace amounts of water. The LLE results for the ternary systems of {ILs + thiophene + alkanes} at 298.15 K are displayed in Fig. 4 and tabulated in Table 3. The molar-based
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distribution coefficient (β) the solvent selectivity (S) are used to assess the performance of the IL solvents for the liquid-liquid extraction process:
𝛽 = (8)
𝑆 = (9)
where 𝑥 and 𝑥 are the concentrations of thiophene and hydrocarbon, respectively.
Fig. 4. Tie-lines of the ternary mixture in mole fraction basis at 298.15 K (black solid line for experimental data, red dashed line for NRTL correlation): (a) [EMIM][MESO3] + thiophene + heptane, (b) [EMIM][MESO3] + thiophene + octane, (c) [EIM][NO3] + thiophene + heptane, and (d) [EIM][NO3] + thiophene + octane.
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Table 3. Molar composition of the tie-lines with the distribution ratio and selectivity for ILs (1) + thiophene (2) + alkanes (3) at 298.15 K.
Hydrocarbon-rich layer Ionic liquid-rich layer
S β
x2 x3 x1 x2 x3
1: [EMIM][MESO3] (1)+thiophene (2)+heptane (3)
0.062 0.938 0.918 0.079 0.003 420.87 1.27 0.135 0.865 0.838 0.160 0.002 451.64 1.18 0.209 0.791 0.790 0.208 0.002 377.43 1.00 0.292 0.708 0.737 0.261 0.002 389.63 0.89 0.374 0.626 0.688 0.309 0.003 131.22 0.83 0.472 0.528 0.630 0.368 0.001 277.22 0.78
2: [EMIM][MESO3] (1)+thiophene (2)+octane (3)
0.081 0.919 0.903 0.090 0.007 151.92 1.11 0.166 0.834 0.852 0.146 0.002 464.34 0.88 0.251 0.749 0.781 0.216 0.003 246.25 0.86 0.329 0.671 0.727 0.270 0.003 191.98 0.82 0.433 0.567 0.681 0.317 0.002 308.87 0.73 0.534 0.466 0.629 0.369 0.002 197.02 0.69
3: [EIM][NO3] (1)+thiophene (2)+heptane (3)
0.068 0.932 0.942 0.055 0.003 281.90 0.82 0.144 0.856 0.904 0.092 0.004 116.88 0.64 0.218 0.782 0.835 0.161 0.003 183.49 0.74 0.309 0.691 0.803 0.193 0.004 107.15 0.62 0.420 0.580 0.754 0.244 0.002 136.57 0.58 0.486 0.514 0.709 0.288 0.003 100.28 0.59
4: [EIM][NO3] (1)+thiophene (2)+octane (3)
0.083 0.917 0.930 0.066 0.004 194.43 0.80 0.162 0.838 0.883 0.114 0.003 175.42 0.70 0.261 0.739 0.817 0.180 0.003 172.70 0.69 0.352 0.648 0.793 0.205 0.002 161.26 0.58 0.458 0.542 0.746 0.252 0.002 118.37 0.55 0.553 0.447 0.696 0.301 0.003 84.31 0.55
For all cases, no ionic liquids are found in the raffinate, which is strongly favorable for the EDS process to avoid the potential contamination of fuel by the nitrogen- and/or sulfur-containing ILs264,265. Moreover, the concentrations of alkanes in all the extract
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phase are at the magnitude of 10-3, which correspond to the high extraction selectivity of the ILs. The results for the Hand and Othmer-Tobias consistency tests are listed in Table S1. It can be seen that the 𝑅 for all correlations are close to 1, indicating the reliability and consistency of experimental LLE data for all the studied systems.
Table S2 lists the NRTL regression results, and the RMSD for the four systems are 0.0067, 0.0104, 0.0076, and 0.0071, respectively. Such low RMSDs demonstrate an excellent correlation of the NRTL model to the experimental LLE data. To assess the consistency of the regressed NRTL parameters, a Matlab toolbox for the topological analysis266 is employed, and the results are shown in the Supporting Information (Figs. S1-S4). According to the miscibility boundary analysis, the binary subsystem thiophene (2)-hydrocarbon (3) can form a homogeneous region while the binary subsystems ILs (1)-thiophene (2) and ILs (1)-hydrocarbon (3) can form a liquid-liquid region. The same conclusion can be concluded from the 𝐺 ⁄𝑅𝑇 function calculated by the correlated NRTL parameters. Moreover, the experimental observations are in line with the topological analysis, the binary subsystem (2)-(3) is completely miscible while the binary subsystems (1)-(2) and (1)-(3) are partially miscible. This good agreement demonstrates that the NRTL parameters regressed for all the systems are reliable and can be trusted for subsequent process design.
3.3. Comparison with other ILs
Fig. 5 presents the maximum selectivity (𝑆 ) of ILs at the lowest concentration of thiophene in the raffinate phase plotted against the corresponding distribution ratio (𝛽 ) in the thiophene/heptane system. The ILs studied in this work are compared with 48 different ILs from the literature16,18,267-284. Compared to other ILs, the ILs studied here own a relatively lower distribution ratio for thiophene. However, as the screening is based on 𝑃𝐼 , the low distribution ratio for thiophene is largely compensated by the high selectivity. To give a more detailed comparison, 𝑆 for each IL are sorted in the ascending order and plotted in Fig. 6. It can be seen that 𝑆
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of ILs is mainly determined by the anions, and the IL with [MESO3]- anion is studied for the first time in this work. The two selected ILs all own a very high 𝑆 , especially for [EMIM][MESO3] which ranks the sixth among all the ILs in the literature. Moreover, comparing to the other ILs with higher selectivity, the two studied ILs have either a notably higher distribution coefficient (1,1][PO2-1,O1] and [IM-2,1][SCN]) or a simpler non-functionalized structure ([PYR-3CN,1][SCN], [Mo1,3CN][TCM], and [PIP-3OH,1][SCN]). That is to say, the ILs studied here achieve a more balanced extraction performance and are more promising in terms of easy preparation and purification. For the system of thiophene/octane, a similar finding can be also found (Table S3), although the available LLE data that can be used for comparison are less than those in the case of thiophene/heptane. To summarize, the comparison with LLE data reported in the literature demonstrates the satisfactory EDS performance of the two selected ILs.
Fig. 5. Comparison of the 𝑆 and 𝛽 of different ILs for the extraction of thiophene from heptane.
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Fig. 6. Comparison of experimental maximum thiophene/heptane selectivity 𝑆 calculated based on LLE data reported in the literature for different ILs.
149 3.4. Process simulation and evaluation
Following the introduced method, the process performance of the two studied ILs are further evaluated and compared with that of sulfolane (a commonly used benchmark conventional solvent). The physical properties of the ILs are estimated by the build-in equations (Eqs. 10-14) in Aspen Plus:
ln 𝜈 = 𝐶 + + 𝐶 ln 𝑇 (10)
𝑉 = 𝐶 + 𝐶 𝑇 (11)
𝐶 = 𝐶 + 𝐶 𝑇 + 𝐶 𝑇 (12)
𝜏 = 𝐶 (1 − 𝑇 ) (13)
λ = 𝐶 + 𝐶 𝑇 + 𝐶 𝑇 + 𝐶 𝑇 (14)
where 𝜈 is the viscosity in cP, 𝑉 is the liquid volume in m3/kmol, 𝐶 is the liquid heat capacity in J mol-1 K-1, 𝜏 is the surface tension in N m-1, λ is the thermal conductivity in W m-1 K-1, 𝑇 = 𝑇 𝑇⁄ , and 𝑇 is the critical temperature of IL in K.
The viscosity260, density263, heat capacity262, and surface tension261 of [EMIM][MESO3] are calculated from the regression of experimental data (Fig. S5). Considering the temperature dependence of thermal conductivity is marginal for [EMIM][MESO3], the mean value between 300 to 375 K is used260. All the regressed parameters are listed in Table S4.
As shown in Fig. 7, the continuous EDS processes using the selected ILs and sulfolane as the extractant are built and compared. A feed stream of 10,000 kg/h of model fuel composed of thiophene and heptane with an initial sulfur content of 100 ppm is fed to the bottom of the extraction column (EC). To reduce the emissions of SOx from burning fossil, the content of total sulfur compounds in the USA285 and European286 gasoline and diesel fuels are confined to a maximum concentration level
150
of 10 ppm. Therefore, the goal of the EDS process is to reduce the sulfur content from 100 ppm in the fuel feed to lower than 10 ppm in the low-sulfur fuel product.
EC DC
S1 Make-up solvents
S2 Model fuel feed
S4
Low-sulfur fuel product
S 5 S u lf u r-lo ad ed I L S7 Recycled IL S6 Residual Liquids S 3 EC DC1 DC2 S1 Make-up solvents S2 Model fuel feed S7 Recycled sulfolane S9 Recycled sulfolane S5 Sulfur-loaded sulfolane S8 Residual Liquids S4 Reffinate S6 Low-sulfur fuel product S 3
(a)
(b)
Fig. 7. Continuous EDS processes using (a) ILs and (b) sulfolane as the solvent. As shown in Fig. 7a, for the IL-based process only one distillation column (DC) is needed because no IL exists in the low-sulfur stream. In contrast, the sulfolane-based process (Fig. 7b) is more complicated with two DCs required, where DC1 is used for
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removing the dissolved sulfolane in the raffinate phase, and DC2 is used for the regeneration of sulfolane.
The key operating conditions of the EC in the IL-based and sulfolane-based processes are optimized using the sensitivity analysis. To fulfill the specification of desulfurization, the mass-based solvent-to-feed ratio (S/F) is determined at the different number of stages for the studied solvents. From Fig. 8, the required amount of solvent continuously decreases with the increase of the stage number, and when the stage number increased to 10, the change of S/F can be neglected. Therefore, the stage number of EC is set to 10 for all the solvents. Compared to sulfolane, the required S/F of the [EMIM]MESO3] and [EIM][NO3] are significantly lower than that of the sulfolane.
Fig. 8. Mass-based solvent-to-feed ratio (S/F) versus the number of stages in the extraction column for meeting the desulfurization requirement.
To avoid using the vacuum pump, all the DCs are operated at atmospheric pressure. For all processes, the design specification is to ensure that the sulfur content in the low-sulfur fuel product stream (S6) is ≤ 10 ppm. The optimized operating conditions and
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the main simulation results of the IL-based and sulfolane-based EDS processes are summarized in Table 4. It can be seen that the low-sulfur fuel products in all the processes satisfy the requirement of the sulfur content ≤ 10 ppm. A very small amount of makeup solvent (at the level of 10-2 kg/h) is needed for the IL-based processes due to the negligible solvent loss in the EC and high solvent recovery ratio in the DC287. In contrast, 7.09 kg/h sulfolane is continuously added to the sulfolane-based EDS process. The overall heat duty of the sulfolane-based process is 7966.31 kW, which is 2.17 and 2.23 times higher than the processes using [EMIM][MESO3] and [EIM][NO3], respectively.
In general, due to the simpler process, less solvent consumption, and lower heat duty, the two studied ILs are promising alternatives to sulfolane for the EDS process of fuel oils. Besides, [EMIM][MESO3] is better than [EIM][NO3] since the EDS process using [EMIM][MESO3] needs a similar amount of heat duty and much less solvent compared to that of [EIM][NO3].
153 T ab le 4 . R es ult s o bta in ed fr om th e s im ula tio n o f t he c on tin uo us E D S p ro ce ss es in A sp en P lu s. E xtr ac tio n c olu m n ( 29 8.1 5 K , 1 b ar, 1 0 s ta ge s) D ist ill ati on c olu m n ( 1 b ar) L ow -s ulf ur fu el pr od uc t S olv en t R ec yc le d so lv en t (k g/h ) S olv en t co ns um pti on (k g/h ) O pe ra tio n c on dit io ns a S ulf ur co nte nt (p pm ) R ec ov er y ra tio (% ) D /F R N S FS H ea t du ty (k W ) [E M IM ][M E S O 3 ] 13 31 0 0.0 1 1.9 × 10 -4 1.2 10 2 36 69 .9 8 10 99 .9 9 [E IM ][N O 3 ] 20 37 4 0.0 5 1.6 × 10 -4 1.4 10 2 35 68 .1 4 10 99 .9 9 S ulf ola ne 39 51 6 7.0 9 0.9 8 b 1.6 b 10 b 9 b 79 66 .3 1 10 99 .8 2 7× 10 -4 c 0.7 c 10 c 2 c a D /F , R , N S , a nd F S d en ote th e m as s-b as ed d ist ill ate to fe ed ra tio , r ef lu x r ati o, nu m be r o f s ta ge s, an d f ee d s ta ge , r es pe cti ve ly . b O pe ra tin g c on dit io ns fo r d ist ill ati on c olu m n D C 1. c O pe ra tin g c on dit io ns fo r d ist ill ati on c olu m n D C 2.
154
4. Conclusions
A reliable method for the screening of practically suitable ILs as extraction solvents is presented and exemplified by the extractive desulfurization process. An experimental IDAC database is collected and used to evaluate the performance measure of ILs towards the specific extraction problem. Meanwhile, the experimental data and QSPR models are applied to assess the physical properties of ILs including toxicity. ILs with good performance as well as desired physical properties are selected as promising candidates. Then, the LLE experiments using these ILs are performed and the corresponding parameters for the thermodynamic models such as NRTL are regressed. Finally, the ILs are introduced to Aspen Plus and their performance in the continuous extraction process is compared. For the extractive desulfurization problem, [EMIM][MESO3] and [EIM][NO3] are selected as the optimal solvents. The processes using these ILs own notably lower solvent consumption and heat duty compared to the benchmark process using sulfolane. These results are encouraging and demonstrate the reliability of the proposed method to screen suitable ILs for practical application.
155
Nomenclature
𝛽 distribution coefficient of component i at infinite dilution
𝛽 distribution coefficient of component i
𝑃𝐼 performance index of extraction at infinite dilution
𝑆 infinite dilution selectivity of component i over j
𝑆 selectivity of component i over j
𝑥 molar composition of component i in liquid
𝑤 mass composition of component i in liquid
𝛾 infinite dilution activity coefficient of component i
[BMMOR] 1-butyl-1-methylmorpholinium [EIM] 1-ethylimidazolium [EMIM] 1-ethyl-3-methylimidazolium [HMMOR] 1-hexyl-1-methylmorpholinium [IM-1,1] 1,3-dimethylimidazolium [IM-1PH,1] 1-benzyl-3-methylimidazolium [IM-2,1] 1-ethyl-3-methylimidazolium [IM-2OH,1] 1-(2-hydroxyethyl)-3-methylimidazolium [IM-4,1] 1-butyl-3-methylimidazolium [IM-5,1] 3-methyl-1-pentylimidazolium [IM-6,1] 3-hexyl-1-methylimidazolium [IM-8,1] 1-octyl-3-methylimidazolium [Mo1,3CN] N-(3-cyanopropyl)-N-methylmorpholinium [Mo1,3OH] N-(3-hydroxypropyl)-N-methylmorpholinium [MO-2O1,1] 4-(2-methoxyethyl)-4-methylmorpholinium [N-2O1,2,1,1] ethyl(2-methoxyethyl)dimethylammonium
[N-2OH,2OH,2OH,1] tris(2-hydroxyethyl)- (methyl)ammonium
[OHOHIM] 1,3-dihydrox1,3-dihydroxyimidazoliumyimidazolium
[P-4,4,4,2] tributyl(ethyl)phosphonium
[Pi4,i4,i4,1] Tri(iso-butyl)methylphosphonium
[PIP-2O1,1] 1-(2-methoxyethyl)-1-methylpiperidinium
156 [PIP-3CN,1] 1-(3-cyanopropyl)-1-methylpiperidinium [PIP-3OH,1] 1- (3-hydroxypropyl)-1-methylpiperidinium [PIP-4,1] 1-Butyl-1-methylpiperidinium [PIP-5,1] 1-methyl-1-pentylpiperidinium [PIP-6,1] 1-hexyl-1-methylpiperidinium
[PO2-O2,O2] diethyl phosphate
[Py2OH] N-(2-hydroxyethyl)pyridinium
[Py3CN] N-(3-cyanopropyl) pyridinium
[Py3OH] N-(3-hydroxypropyl)pyridinium [PY-4,CN[3]] 3-cyano-1-butylpyridinium [PY-4,CN[4]] 4-cyano-1-butylpyridinium [PY-6,CN[4]] 4- cyano-1-hexylpyridinium [PYR-2O1,1] 1-(2- methoxyethyl)-1-methylpyrrolidinium [PYR-3CN,1] 1-(3-cyanopropyl)-1-methylpyrrolidinium [PYR-3OH,1] 1-(3-hydroxypropyl)-1-methylpyrrolidinium [PYR-4,1] 1-butyl-1-methylpyrrolidinium [AC] acetate [CCN3] tricyanomethanide [DCA] dicyanamide [FAP] trifluorotris(pentafluoroethyl)phospha [MESO3] methanesulfonate [NO3] nitrate [Tf2N] ([NTF2]) bis(trifluoromethylsulfonyl)imid [OTF] trifluoromethanesulfonat [PO2-1,O1] methylphosphonate [SCN] thiocyanate
[SO4-1] methyl sulfate
[SO4-2] ethyl sulfate
[TCB] tetracyanoborate
[TCM] tricyanomethanide
157
Supporting Information
Table S1. Parameters for the Hand and the Othmer-Tobias correlations.
System Hand Othmer-Tobias
a b R2 c d R2
1 0.553 1.372 0.992 1.788 1.394 0.993
2 0.784 1.396 0.992 1.828 1.458 0.991
3 1.07 1.3 0.985 1.951 1.345 0.986
4 1.354 1.454 0.989 2.049 1.507 0.99
Table S2. NRTL parameters regressed from the experimental LLE data for the ternary systems ILs (1) + thiophene (2) + alkanes (3).
Components i-j NRTL parameters RMSD
(∆gij /R)/K (∆gji /R)/K αij
[EMIM][MESO3] (1)+thiophene (2)+heptane (3)
1-2 -281.44 3740.6 0.2
0.0067
1-3 2657.55 3481.91 0.2
2-3 608.54 -284.45 0.2
[EMIM][MESO3] (1)+thiophene (2)+octane (3)
1-2 -59.74 4138.24 0.2
0.0104
1-3 2977.69 3423.4 0.2
2-3 854.36 -282.08 0.2
[EIM][NO3] (1)+thiophene (2)+heptane (3)
1-2 40.33 4449.68 0.2
0.0076
1-3 2530.58 3432.59 0.2
2-3 360.76 -98.67 0.2
[EIM][NO3] (1)+thiophene (2)+octane (3)
1-2 -152.98 3356.68 0.2
0.0071
1-3 2688.83 3676.77 0.2
158
Table S3. Comparison of the maximum selectivity among different ILs towards the thiophene/octane system. Name Maximum thiophene's distribution ratio Maximum thiophene/octane selectivity T (K) [Pi4,i4,i4,1][TOS] 1.72 24.2 308.15 [Omim][SCN] 2.34 28.63 298.15 [OHOHIM][ Tf2N] 0.545 66.7 308.15 [Hmim][SCN] 2.11 107.9 298.15 [BMPIP][DCA] 0.022 119 308.15 [BMIM][OTf] 1.88 133 308.15 [EMIM][MESO3] 1.110 151.919 298.15 [EIM][NO3] 0.802 194.429 298.15 [Bmim][SCN] 1.63 403.1 298.15 (c) (d) (a) (b)
Fig. S1. Results of the topological analysis of NRTL correlations for (1) [EMIM][MESO3] + (2) thiophene + (3) heptane.
159
(c) (d)
(a) (b)
Fig. S2. Results of the topological analysis of NRTL correlations for (1) [EMIM][MESO3] + (2) thiophene + (3) octane.
(c) (d)
(a) (b)
Fig. S3. Results of the topological analysis of NRTL correlations for (1) [EIM][NO3] + (2) thiophene + (3) heptane.
160
(c) (d)
(a) (b)
Fig. S4. Results of the topological analysis of NRTL correlations for (1) [EIM][NO3] + (2) thiophene + (3) octane. 280 290 300 310 320 330 340 350 0 100 200 300 400 500 V is co si ty （ cP ） Temperature （K） correlation experimental 290 300 310 320 330 340 350 0.0475 0.0480 0.0485 0.0490 0.0495 0.0500 0.0505 0.0510 Su rf ac e te ns io n （ N m -1） Temperature （K） correlation experimental 290 295 300 305 310 315 320 325 334 336 338 340 342 344 346 348 H ea t ca p ac it y （J m ol -1 K -1 ） Temperature （K） correlation experimental 280 300 320 340 360 1190 1200 1210 1220 1230 1240 1250 1260 D en si ty （ k g m -3） Temperature （K） correlation experimental (c) (d) (a) (b)
Fig. S5. Correlation results of (a) viscosity, (b) density, (c) heat capacity, and (d) surface tension of [EMIM][MESO3] using experimental data.
161
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