Operational Strategies and Comprehensive Evaluation of Menthol

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Operational Strategies and Comprehensive Evaluation of Menthol Based Deep Eutectic Solvent for the Extraction of Lower Alcohols from Aqueous Media Rupesh Verma, Mood Mohan, Vaibhav V. Goud, and Tamal Banerjee ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.8b04255 • Publication Date (Web): 11 Nov 2018 Downloaded from http://pubs.acs.org on November 12, 2018

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ACS Sustainable Chemistry & Engineering

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Operational Strategies and Comprehensive Evaluation of

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Menthol Based Deep Eutectic Solvent for the Extraction of Lower

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Alcohols from Aqueous Media

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Rupesh Verma†,§, Mood Mohan†,§, Vaibhav V Goud†, and Tamal Banerjee†,*

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† Department

of Chemical Engineering, Indian Institute of Technology Guwahati, Amingaon, North Guwahati, Guwahati, Assam- 781039, India

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*Corresponding

author

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E-mail address: [email protected] (Prof. T. Banerjee)

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Tel.: +91-361-2582266; fax: +91-361-2582291

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ABSTRACT

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Deep Eutectic Solvents (DESs) are gaining more interest as low-cost extraction media. Looking

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at its vast opportunity, the current work explores the extraction of alcohols namely ethanol, 1-

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propanol, and 1-butanol from the aqueous phase using two novel hydrophobic DES at 303.15

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K and 1 atm. The preparation of DES posses a common HBA (Hydrogen Bond Acceptor) with

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a HBD (Hydrogen Bond Donor such as organic acids i.e., lauric acid and decanoic acid) at a

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certain proportion. Ratio’s of 2:1 and 1:1 were used for synthesizing menthol: lauric acid (DES-

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1) and menthol: decanoic acid (DES-2), respectively. The highest extraction efficiency of

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alcohols was observed in the presence of DES-1. Thereafter, atomistic molecular dynamics

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(MD) simulations have also been adopted to understand the extraction mechanism of alcohols

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from water using these DES. From MD simulations, the interaction energies, structural

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properties such as radial and special distribution functions and dynamic properties such as self-

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diffusivity are computed. From the results of MD simulations, it was inferred that menthol or

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the HBA was playing a vital role in the extraction of alcohols as compared lauric acid or

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decanoic acid. Furthermore, a process flow sheet was conceptualized for the separation and

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recycling of both DES and alcohol using ASPEN plus. It yielded a 99.7 % and 98.3 % recovery

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of DES and alcohol, respectively.

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KEYWORDS: ASPENPlus, Deep eutectic solvents, Liquid-liquid equilibrium, Lower

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alcohols, Molecular dynamics simulations

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INTRODUCTION

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Over the past few decades, the demand for clean energy is gradually increasing due to depletion

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of fossil resources. In such scenario, an alternative energy source is required to overcome the

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nonrenewable fossil fuels. Lower alcohols are deliberated as a potential substitution for

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traditional fuels. These alcohols are showing comparable properties as gasoline exhibits.1 Thus,

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these lower alcohols could be used as a potential alternative fuel with little modification to the

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current engines. The primary source of lower alcohols is the ABE (acetone-butanol-ethanol)

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fermentation, where the alcohols are present as azeotrope ought to a water-rich phase. ABE

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comprises 60 mass wt% water-vapor and remaining 40 wt% ABE (3:6:1).2,3 Hence, their

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extraction from aqueous solution is an essential step. Distillation is the technique used to

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separate and purify the compounds but requires an expensive unit operation and also a large

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amount of energy. Therefore, the separation of compounds such as alcohols is always

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challenging because of the formation of an azeotrope with its aqueous phase solution.

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Extraction process has been found to be economical and effective among several

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alternatives namely distillation, absorption, pre-vaporization and membrane separation.

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Therefore, Liquid-Liquid Extraction (LLE) is an environmentally friendly technique and an

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alternative to the distillation process. It is based on the non-miscibility of two liquid phases that

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exhibit discriminatory affinity towards one or more components in the feed. It is worthwhile to

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note that the LLE process depends on the efficacy of the appropriate solvent employed, its

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price, recyclability, and ecologically affability. The use of LLE reduces the consumption of

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energy and avoids the discharge of volatile solvent to the environment.

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Recently hydrophobic Ionic Liquids (ILs) gave promising results for the removal of

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alcohols from the aqueous medium. However, the synthesis of hydrophobic ILs are not easy

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and is costly and has controversial environmental acceptability. The new class of solvents such

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as deep eutectic solvents (DES) shows numerous appealing properties like ILs i.e., less vapor

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pressure, a wide range of liquid, and good chemical and thermal stability. Hence, the DES is

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being explored as a substitute for ILs. The DES is prepared by mixing of two or more low-cost

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chemical substances, i.e., hydrogen bond acceptor (HBA) and a hydrogen bond donor (HBD).

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They are mixed at a specific molar ratio to turn them into a liquid state at room temperature.4,5

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Moreover they are economically viable when compared with ILs.5,6 They are also cheap, while

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a further purification step is also not desired.



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Over the years, there has been an extensive research on the extraction of lower alcohols

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from aqueous medium using ionic liquids and organic solvents.7,8 Contrary to ILs and

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conventional solvents, the LLE data for alcohols extraction in DES are limited. In our previous

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communication, we have reported the use of DES consisting of menthol + lauric acid (2:1) for

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recovering the lower alcohols from its azeotrope mixture.1 It was observed that the use of

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hydrophobic DES in the extraction of lower alcohols have displayed larger values of

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distribution coefficients and selectivity as compared to other solvents.

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To understand the molecular level mechanism of an extraction system, researches adopt

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the quantum chemical (QC) and molecular dynamics (MD) simulations which provide

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fundamental molecular insights of the system. These computational techniques would also help

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in exploring new kind of solvents and hence results as an alternative for screening9. However,

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these computational methods are not foreseen to substitute the experiments, but rather to

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complement them and enrich their application. For instance, Stephenson et al. (2006) reported

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the removal of alcohol (ethanol) from its aqueous medium using MD simulations.10 Further,

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Taha and Lee (2013) investigated the extraction of organic solvents from aqueous solution

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using MD simulations. In their study, they used the biological buffer as an extractant.11

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Recently Dehury et al. (2016) attempted to study the separation of 1-butanol from water by

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MD simulations using an ionic liquid as an extractant.12 Perkins et al. (2014) studied the

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computational and experimental insights of choline chloride-based DES.13 In addition, Naik et

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al. (2018) investigated the computational insights for the extraction of polyaromatic

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hydrocarbons from heptane in the presence of phosphonium-based DES.14

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In our earlier communication, it was reported that DES has shown an excellent

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extraction capacity of ethanol, propanol, and butanol from aqueous solutions when compared

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to ILs and common organic solvents.1 However, the earlier study did not evaluate the role of

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HBA and HBD in the extraction process. Moreover, the separation of extract phase components

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is also not attempted. Therefore, the present study is aimed to address the role of DES

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components in the extraction process by applying MD simulations. From the MD simulations,

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interaction energies, radial distribution functions, and other notable properties are measured to

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understand the role of DES. Finally, the MD simulated mole fractions are correlated with the

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experimental tie-line data and also compared with their extraction yield. Furthermore, ASPEN

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Plus calculations have also been adopted to study the separation and recovery of DES and 1-

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butanol from their mixtures.


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MOLECULAR DYNAMICS SIMULATION DETAILS

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The initial structures of the investigated molecules were drawn by Avogadro freeware

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software.15 The geometries of all the molecules are fully optimized by Gaussian09 using the

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B3LYP theory and 6-311G* level of basis set.16,17 Figure 1 shows the optimized molecular

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structures of the explored chemicals along with their atom notations used in the present study.

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The restrained electrostatic potential (RESP) charge derivation method was used to obtain the

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partial charges of all the molecules.18,19 Generalized Amber Force Field (GAFF) functional

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form was employed to generate the force field parameters of the compounds using the

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ANTECHAMBER module.20,21 The generated force field parameters were further validated by

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measuring the densities at 303.15 K. From Table S1, the deviation between experimental and

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MD predicted densities are under 2%.

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For all MD simulations, NAMD version 2.1022 software was used and performed at

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constant temperature (303.15 K) and 1 atm pressure using Langevin thermostat and Nose-

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Hoover Langevin barostat.23,24 To confine the bonds involving hydrogen atoms, the SHAKE

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algorithm was implemented.25 To treat the long-range electrostatic interactions, the Particle

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Mesh Ewald (PME) method was applied at a cut-off distance of 12 Å. The time step was kept

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at 1 fs.26 The initial configurations of different systems consisting of DES, alcohols (ethanol,

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1-propanol, and 1-butanol), and water is prepared by PACKMOL.27 At first, DES and water

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molecules were placed in separate cubical boxes namely DES-rich and a water-rich phase.

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Afterward, the alcohol molecules were dispersed in these two phases. This geometry hence

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ensures the experimental condition. Due to the restrictions, MD simulations were performed

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for just a single tie-line (i.e. the line meeting the extract and raffinate phase in a ternary

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diagram) of each alcohol.

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The number of molecules for MD simulations was taken on the basis of the initial

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experimental feed composition of the investigated systems. Table S2 reports the experimental

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initial feed composition and a corresponding number of molecules used in the MD simulations

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for various tie-lines. As can be seen from Table S2, 100 molecules of DES, 500 molecules of

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alcohols, and 1400 molecules of water are considered for MD simulations. Here, the DES is

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regarded as a solvent with the mixture of two different species at 2:1 (menthol: lauric acid) and

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1:1 molar ratio (menthol: decanoic acid) respectively. They shall be known as DES-1 and DES-

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2, respectively. In the MD simulations, the minimization was run for 1 ns. After the

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minimization step, the system was gradually heated to 303.15 K for 0.5 ns. After reaching the

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desired temperature, the simulation system was equilibrated for 8 ns in isothermal-isobaric 5 ACS Paragon Plus Environment


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(NPT) ensemble. From this constant simulated volume, the MD simulated density is calculated

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and compared with measured density (Table S1). Thereafter, the production run lasted for 40

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ns under NVT ensemble to achieve a clear phase separation data. With the increase in the

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production runtime, the distribution of alcohol molecules was found to be negligible and

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reaches a table point i.e., there was no further movement of alcohol molecules from the water

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phase to DES phase. At every 5 ps, the trajectory data was saved for structural and transport

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analysis using Visual Molecular Dynamics (VMD) package.28

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MATERIAL AND METHODS

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Materials. The lower alcohols ethanol (≥99.9%), 1-propanol (≥99%) and 1-butanol

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(≥99%) were purchased from Merck, India. The chemicals menthol (≥95%), lauric acid (≥99%)

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and decanoic acid (≥98%) were used for the preparation of DES. Table 2 shows the detailed

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description of chemicals used in the present study. To remove the moisture content, all the

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chemicals were kept under vacuum at p = 600 mm Hg and T = 333.15 K for 48 h. The solvent,

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dimethyl sulfoxide-d6 (DMSO-d6) was used in the NMR analysis which was purchased from

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Merck, Germany with a purity of ≥99.8%. All chemicals were of analytical grade and were

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used without further purification.

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Preparation of DES. The hydrophobic DES such as menthol + lauric acid and menthol

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+ decanoic acid were prepared as reported in our previous work.1 To produce the hydrophobic

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DES, menthol + lauric acid and menthol + decanoic acid were mixed in a molar ratio of 2:1

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and 1:1, respectively. The mixture of menthol + organic acids was taken in a 100 mL flat

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bottom flask and placed on a magnetic hot plate (TARSONS SPINOT-magnetic stirrer and hot

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plate-DIGITAL, MC02, India). The mixture was then heated at 323.15 K with a continuous

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stirring until a homogenous colorless clear liquid was obtained. To obtain a clear liquid phase,

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it took 2 h of preparation time. Both DES are prepared at 1 atm pressure and sealed with

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parafilm. Before performing the LLE experiments, the vacuum was applied to the prepared

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DES solvents at p = 600 mm Hg to the DES samples at T = 333.15 K for at least 48 h to remove

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the water content and volatile compounds. After vacuum drying, the water content of both the

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DES was measured with Karl-Fisher Titrator (Metrohm, 787 KF Titrino, Switzerland) and was

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found to be 0.922 and 0.862 wt% for DES-1 and DES-2, respectively. Thereafter, the purity of

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the different constituents of DES was measured by performing the 1H NMR spectroscopy (600

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MHz NMR, Bruker, Germany; Figures S1 and S2). Hydrophobicity of both synthesized DESs

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was checked by washing the samples repeatedly four times. Figures S3 and S4 shows 1H NMR

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spectra of upper phase (solvent rich-phase) which clearly shows an absence of water. Figures 6 ACS Paragon Plus Environment

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S5 and S6 shows the 1H NMR spectra of aqueous rich-phase, which clearly reflect the absence

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of DES in the bottom phase even after repeated washing.

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Further, the LLE experiments were performed to extract the lower alcohols from

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aqueous solutions using DES as an extractant at 303.15 K. The detailed experimental procedure

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for LLE and its compositional analysis is given in our previous work, hence it is not discussed

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here.1 In our earlier work, we have also described the hydrophobic nature of DES which was

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confirmed by 1H NMR spectroscopy.1

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RESULTS AND DISCUSSION

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Correlation between Experimental and MD Simulated Tie-Lines. The study

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initiates with the comprehensive analysis of the molecular interactions of the DES molecules

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with the water and alcohol molecules. The LLE experimental and MD simulated tie-line data

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for the system DES (1)–Alcohol (2)–Water (3) are summarized in Table 2. The distribution

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coefficient (β) and selectivity (S) are calculated by the following Eqs. 1 and 2.

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

E x Alcohol R x Alcohol

E R  Alcohol x Alcohol / x Alcohol S  E R Water xWater / xWater

(1)

(2)

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E R Here, x Alcohol and x Alcohol are the mole fraction of alcohols or butanol in extract and raffinate

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E R phases, respectively. xWater and xWater are mole fractions of water in both the extract and raffinate

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phases. Table 3 shows the comparative LLE results of DES-1 and DES-2 for 1-butanol with

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respect to experimental, ASPEN Plus and MD simulated tie-line points. The latter two shall be

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discussed in detail in the ensuing sections. The values of the distribution coefficient and

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selectivity are higher than one for all three system tie-line data points (see Table 2 and 3). The

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larger values of selectivity implies that DES has higher capability for alcohols extraction as

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compared to water. On the other hand, the values of β were also higher than one, which

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indicates efficient distribution of alcohol molecules from the aqueous phase to the DES phase.

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Figure 2 shows the ternary diagram for the extraction of alcohol from the aqueous phase

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using both DES. The deviation between the experimental and simulated data is less than 6%

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indicating an excellent agreement between them. As can be seen from Table 2 and 3, the

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experimental mole fraction of alcohols in the extract phases is higher than that of MD predicted 7 ACS Paragon Plus Environment


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value. This also results in the difference of values of distribution coefficient and selectivity as

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they are related by equation 1 and 2. The difference in β and S values occurs due to the small

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uncertainty of mole fractions which could lead to a noticeable change in the β and S values. In

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our earlier communication, similar differences are observed for β and S values.14, However, it

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was worthwhile to mention that the reported LLE tie-line data are the average of three diverse

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arbitrary results of experimental and MD simulations. In a similar manner, it was also

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worthwhile to note that the fraction of DES in the raffinate phase is almost negligible which

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signifies that the solvent has less attraction towards the water.

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From the LLE results (Table 1), it is clear that the 1-butanol has higher extraction

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efficiency and is then followed by 1-propanol and ethanol. This ascription was due to the lower

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solubility of 1-butanol in water and also due to higher attraction capacity towards DES phase

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as compared to 1-propanol and ethanol. Therefore, the distribution coefficient and selectivity

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for the 1-butanol based system are higher than the other two alcohols (see Table 1). Further,

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Table 3 reports the effect of two different DESs on the extraction efficiency of 1-butanol from

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the water. DES-2 here gave a higher extraction yield of 1-butanol. Similarly, the distribution

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coefficient and selectivity for DES-2 is higher than DES-1 which signifies that DES-2 has

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higher removal capacity of butanol when compared to DES-1. In order to elucidate the effect

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of alcohols and DES on the extraction efficiencies, the interaction energies, self-diffusion

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coefficients, and structural properties of the ternary system have also been carried out. We shall

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discuss them one by one.

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It was worthwhile to mention that at a similar molar ratio of DESs (1:1 or 2:1), the

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extraction experiments will not be feasible on account of the specific ratio the donor and

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acceptor need to be mixed. Liquid formation takes place only at the eutectic point or the ratio

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which is 2:1 for lauric acid (DES-1) based DES and 1:1 for decanoic acid based DES (DES-2;

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see figure S7 for the formation of the eutectic point). Hence, DES-1 and DES-2 are formed at

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different molar ratios (see figure S7 for the formation of the eutectic point). It should be noted

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that these ratios are obtained from the conductor like screening model for segment activity

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coefficients (COSMO-SAC) predicted model.1 However, molar ratio other than the 1:1 and 2:1

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did not yield a liquid phase on account of the existence of the solid phase.

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Non-bonded Interaction Energies. The interaction energy for ternary system, DES-1

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(1)–alcohol (2)–water (3) have been computed at T = 303.15 K and p = 1 atm from MD

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simulated trajectories and reported in Table 4. The total non-bonded interaction energy is the

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aggregation of electrostatic and van der Waals (vdW) components. From Table 4, it is seen that 8 ACS Paragon Plus Environment

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the interaction energy between DES-1–alcohol is much stronger than DES-1–water, water–

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alcohol and water–DES-1. The van der Waals interactions were seen to be higher than the

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electrostatic interactions, implying that the van der Waals interactions are the governing

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parameter for DES-1–alcohols interaction energy. In DES-1–alcohol interactions, menthol

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gave higher interaction energy with alcohol molecule than lauric acid. Further, the interaction

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between water–alcohols is much lower which implies that the presence of alcohol fraction in

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water-rich phase is less. The decrease in interaction energy also implies that the addition of

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DES to water–alcohol mixtures has a higher impact on their separations; and the alcohol

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molecules are more effectively attracted towards the DES phase owing to the size of the DES

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molecule.

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Among the alcohols, 1-butanol had stronger interaction energies with DES-1 as

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compared to 1-propanol and ethanol. On the other hand, 1-butanol gave lower interactions with

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water. Therefore, the extraction efficiency of 1-butanol is higher than 1-propanol and ethanol

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in the DES-1–alcohol–water ternary systems (Figure 3). Apart from alcohol interactions with

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DES-1 and water, the interaction energy between DES-1–water (i.e., in DES–rich phase) is

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higher than water–DES-1 (i.e., in water–rich phase). The higher interaction of DES-1–water

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indicates that the significant amount of water is present in the DES–rich phase. On the contrary,

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a lower interaction of water–DES-1 indicates a negligible amount of DES-1 in the water-rich

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phase. Thus the interaction of water–DES-1 confirms that the prepared DES-1 is hydrophobic

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in nature. The hydrophobicity of DES-1 mainly results from menthol as the interaction between

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water and menthol is much lower. The degree of interaction energies for alcohols are observed

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to be in the subsequent order: DES-1–1-butanol > DES-1–1-propanol > DES-1–ethanol (Figure

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3). The experimental and MD simulated gave an increasing order of distribution coefficient

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and selectivity which is: 1-butanol >1-propanol > ethanol (Figure 3).

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Table 5 reports the interaction energies of both the DES (1 and 2) with 1-butanol and

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water-based ternary system. Here, the MD simulations were carried out for 1-butanol with both

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DES, in order to know the effect of other DES i.e., DES-2. As can be seen from Table 5, the

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interaction energy between DES-1–butanol is stronger than DES-2–butanol system. However,

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the distribution coefficient, selectivity, and extraction efficiency of 1-butanol are higher in

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DES-2 (Figure 3 and Table 3). We have also attempted to explore this effect by studying the

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spatial distribution functions and mean square displacement. It ought to be noticed that the

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interaction energy between water–DES and DES–water is lower in DES-2 (decanoic acid)



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based system which signifies that DES-2 has higher hydrophobicity as compared to DES-1

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(lauric acid).

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Radial and Spatial Distribution Functions. The explicit interactions of DES–alcohols

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and water–alcohols were studied through radial distribution functions (RDF) and depicted in

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Figure 4. The RDF plot provides information about the structural packing and interactions

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between different chemical molecules of the system.14,17,29 The atom notations OM1 atom of

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menthol, HBO of 1-butanol, HPO of 1-propanol, HEO atom of ethanol, and OW1 atom of

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water were used for RDF plots (see Figure 4). The RDF peak between the oxygen atom of

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menthol and hydroxyl proton of alcohols are obtained at a distance of 1.95 Å, which implies

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the formation of a strong hydrogen bond between menthol and alcohol molecules. As in the

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case of water–alcohol systems, the RDF peak was also obtained at a similar distance of 1.95

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Å. However, the peak height (g(r)) changes with different interactions of menthol and alcohols.

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In the menthol–alcohol system, the height of the peak was higher than water–alcohol systems.

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Thus, the interaction energy between menthol-alcohols was stronger than water-alcohol.

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The TRAVIS package was used to generate the spatial distribution functions (SDFs) data for

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the chemical neighborhood of alcohol and water in DES phase. These are shown in Figure 5.30

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For comparison purpose of SDFs, the radius of solvation (i.e., cutoff) was fixed to 7 Å. For

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SDF plots, the system composed of DES–alcohol and 1-butanol–DES where the isovalues are

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3.2 and 4 particle nm–3, while the isovalues for the water–DES/1-butanol constituting species

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is 2 particle nm–3. As can be seen from Figure 5a, menthol is surrounded by three different

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alcohols namely 1-butanol, 1-propanol, and ethanol. The more active site of menthol (hydroxyl

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group) is seen to be surrounded by 1-butanol and 1-propanol whereas ethanol is attracted to the

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entire menthol moiety. The surface of 1-butanol is found to be highly surrounded around the

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non-active site of menthol. Thus, the contribution of van der Waals interaction energy is higher

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in menthol–alcohol system (Table 4 and 5).

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Figure 5b shows the fact that menthol is highly surrounded with 1-butanol as compared

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to water within the active site of menthol. Therefore, the contribution of electrostatic energy

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and van der Waal energy is higher for 1-butanol with menthol/DES as compared to 1-propanol,

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ethanol, and water. Figure 5c depicts the fact that water is surrounded by 1-butanol and menthol

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from DES-1–butanol–water system. It is clearly seen that DES and 1-butanol are distributed

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very far away from the water molecule which hinders the interaction between water–menthol

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(DES) and water–1-butanol. Further, Figure 5d also depicts the effect of butanol on two

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different DESs in their ternary systems. The surfaces of 1-butanol from DES-2 are found to be 10 ACS Paragon Plus Environment

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firmly dispersed around the more active site (-OH) of menthol than butanol from DES-1.

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Therefore, the extraction of butanol is higher in DES-2 based ternary system.

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Self-Diffusion Coefficient. The self-diffusion coefficient (D) provides the overall

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movement of the molecule in the ternary system which is calculated by the Einstein equation

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(Eq. 3).25,31-33 The D values were reported at different simulated time step trajectories by

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selecting the individual molecules present in the bulk phase (i.e., ternary and binary system).

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This is given by:

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1 d D  lim 6 t  dt

N

 r t   r  0 i 1

i

2

i

(3)

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Here, ri(0) and ri(t) are the positions of the ith atom at time 0 and t, respectively. The expression

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within bracket implies the mean square displacement (MSD) of the species. The presence of

303

factor 1/6 is an account to the three-dimensionality of the system. Diffusion coefficients of

304

corresponding molecules are determined from the gradient of the MSD curve (Figure 6) and

305

all the curves are linear. Here, it is worthwhile to specify that the D values for different

306

molecules are accounted for on the averaging over various time inceptions. These are reported

307

in Table 6 against various time intervals with a time frame of 15 ns. The self-diffusion

308

coefficient of DES and alcohol molecules were found to be similar while for water, the value

309

was higher in the ternary system at 45 ns. At the initial stage of the production run (0-15 ns),

310

the D values of all alcohols have a higher deviation with DES-1. With the increase in the

311

simulation runtime, 1-butanol molecules move towards the DES phase where the self-diffusion

312

values are now found to be closer to each other.

313

1-propanol and ethanol gave slightly higher deviation with DES-1 as compared to 1-

314

butanol which results in lower extraction. On contrary, the diffusion values for water are higher

315

which implies that the water molecules move further away from DES-1 and alcohols. Thus, the

316

water molecule does not interact favorably with DES-1 and alcohol in the ternary system. From

317

these observations, it is concluded that: (i) the transfer of alcohols from the water phase to

318

DES-1 phase proceeds at about 30 ns (ii) the closer the D value, higher the solubility and more

319

interaction between the molecules and vice versa. Therefore, the D for 1-butanol is close to

320

DES molecules which results in higher recovery of 1-butanol.

321

Furthermore, the self-diffusivity coefficient values for DES-1 and DES-2 with 1-

322

butanol-water mixture were also explored and shown in Table 7. From Table 7, it is seen that

323

the self-diffusion coefficient of 1-butanol from DES-2 is two times higher than DES-1 self11 ACS Paragon Plus Environment


324

diffusion coefficient. Table 7 also reports the self-diffusivity of 1-butanol which was three and

325

two times higher than the D value of DES-1 and DES-2 respectively. Therefore, the DES-2 has

326

shown higher extraction capacity of 1-butanol than DES-1. It should also be worthwhile to

327

mention that the D value of water is higher in DES-2 based ternary system which results in

328

higher hydrophobicity of DES-2. With this, we now move towards the process flow sheeting

329

of butanol and solvent recovery so that a comprehensive economic evaluation of these novel

330

solvents is justified.

331

Recovery of DES and Lower Alcohols using Hybrid Extraction-Distillation Unit.

332

As discussed earlier the separation of lower chain alcohols from their aqueous phase is

333

considered difficult because of azeotrope formation.34-36 Simple distillation is not effective and

334

economical for such cases.37 LLE is used when hydrophobic solvents retain greater tendency

335

for 1-butanol and lower solubility with an aqueous medium. A hybrid extraction process was

336

designed to carry out an optimized flowsheet relating to the scale-up the process as well as

337

recovery and recycle of solvent using low-density DES. It was reported that hybrid extraction

338

process was found to be a more feasible tool for the separation process as it reduces the energy

339

demanding step of distillation.38 To make the separation process economical and efficient, the

340

hybrid extractive process includes the distillation column connected with LLE set-up.37,39

341

Various researchers (Groot et al.40, Qureshi et al.41 and Liu et al.42) have studied the separation

342

of different components using hybrid downstream process. The detailed flow sheet of process

343

optimization for DES and 1-butanol recovery is depicted in Figures 7 and 8.

344

Another goal of this present study is to minimize the process cost and enhance the

345

solvent recovery process in order to recover the extracting solvent at 303.15 K and 1 atm

346

pressure. Further, the recovered solvent was also reused in the extraction flowsheet.43-45 Figure

347

8 shows the solution strategy for the extraction of 1-butanol using ASPEN Plus. The

348

operational conditions for the hybrid extractor column will be used as p = 1 atm and T = 303.15

349

K. The fresh feed composition in terms of mass fraction namely: water 0.8 (w/w) and 1-butanol

350

0.2 (w/w) is used in all the simulations. Here DES as the solvent is added to a separate stream.

351

Non-random two-liquid model (NRTL) and COSMO-ASPEN thermodynamic model have

352

been used for thermodynamic modeling.45-48 Here in this work, we have displayed the results

353

of DES-1 as a solvent. We have then compared our results with the conventional solvent

354

namely mesitylene as well as DES-2.49

355

Initially, the extractor column is designed for a recovery of 99.99% 1-butanol by setting

356

solvent flow rate as the adjustable parameter. In the extractor, both Design Spec as well as 12 ACS Paragon Plus Environment

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357

Sensitivity Analysis tools of ASPEN have been utilized to elevate the solvent flow rate for a

358

99.9 (w/w) % recovery of 1-butanol. Within the extractor column, for a particular number of

359

stages (i.e., 5, 6, 7, 8 etc.) the solvent flow rate is varied by applying Design Spec with 99.9%.

360

Thereafter for every stage, the freight on board cost (F.O.B) purchase cost is computed and

361

plotted. The stage number corresponding to the lowest F.O.B was then chosen as the number

362

of stages for the extractor. Once the number of stages is fixed, the process adopts the Sensitivity

363

Analysis tool to obtain the variation of solvent flow rate with butanol flow rate in the extract

364

stream. With this, we get an optimized flow rate of solvent (DES).

365

In the distillation column, the same extract feed rate from extractor is used as a feed

366

stream by increasing its pressure through a pump. In this column, the manipulated variables

367

used are reflux ratio and distillate rate. Design Spec is then invoked by keeping a maximum

368

purity of butanol in the distillate stream. Here, both the reflux ratio and the butanol flow rate

369

in the distillate stream is varied from 0.01 to 100 and 1000 kg/h to 6000 kg/h, respectively

370

while applying Design Spec. The simulation is made to converge by varying the feed stage or

371

the pump pressure. On similar lines, the TAC (Total Annual Cost) of the distillation column,

372

which take account of both capital (shell and heat exchange cost) and energy cost is also

373

calculated by varying the number of stages in the distillation column. Optimum solutions

374

obtained when the overall TAC (combined TAC of extractor and distillation column) is

375

minimum.

376

COSMO Model for Defining DES in ASPEN Plus. COSMO model was used for

377

defining the pseudo-components i.e. DES in ASPEN Plus.50 The PSUEDOCOMPONENT

378

concept was introduced by Riva et al.50 and Larriba et al.51 during their study on CO2 absorption

379

and aromatic compounds extraction using ionic liquids. Pseudo-components means, the

380

component which does not have initial configuration or properties in ASPEN Plus data bank.

381

Therefore, the PSUEDOCOMPONENT model has been defined by incorporating molecular

382

properties computed through the COSMO model. First, the geometry of pseudo-component

383

was optimized with Gaussian09 at B3LYP/6-311G*.52

384

After optimization of molecular geometries, the next step is to generate the COSMO

385

file at BVP86 level of theory and SVP basis set along with the combination of density fitting

386

basis set DGA1.8,53,54 Once, the COSMO files were generated, the final step is to obtain the

387

sigma profiles of the molecules, which will now be used as input for ASPEN particularly for

388

DES.55-58 The sigma profile is given in Table S2. The sigma profile divides the entire screening

389

charge in 60 histograms ranging from -0.03 e/Å2 to +0.03 e/Å2 with each histogram having a 13 ACS Paragon Plus Environment


Page 14 of 43

390

width of 0.001 e/Å2. The 60 histograms are then divided into five sets of 12 histograms and

391

then inserted in the ASPEN database as user input. Once this part is done ASPEN performs the

392

simulation using DES as a single component. For DES, the sigma profile of HBA and HBD

393

are multiplied with their respective mole fraction (Eq. 4) to get the sigma profile of DES.

394 395 396

pDES ( )  pHBA ( )  pHBD ( )  f HBA pHBA ( )  f HBD pHBD ( )

(4)

Here, pHBA ( ) and pHBD ( ) are the sigma profile of the components of DES, namely the HBA and HBD, respectively. f HBA and f HBD are the mole ratios of HBA and HBD.

397

Hybrid Extraction Distillation with Lauric Acid based DES. For starting the

398

simulation, the boiling point of DES is also required. The boiling point of the DES can be

399

calculated by the method given by Lydersen−Joback−Reid (LJR).59 The following equation

400

(Eq. 5) was used to calculate the normal boiling point of DES and is given as:

401

T  198.2   n T b i bMi

(5)

402

Here, Tb is the boiling point temperature of the prepared DES in (K), ni is the number of times

403

appearance of the ith group in the molecule, and ΔTbMi is their contribution to the normal boiling

404

point temperature (K). The calculated normal boiling point of DES-1 was found to be 566.44

405

K and for DES-2 is 561.56 K (Table 1).

406

Figure 9 depicts the hybrid downstream process for the production of 1-butanol at the

407

flow rate of 5000 kg/h (4.83×104 ton/yr.). As per the optimization scheme outlined, the

408

extraction column here consists of seven equilibrium stages and the distillation column possess

409

fifty-four stages. It was seen that the mass loss of solvent was negligible, however, as per

410

standard protocol and mass balance in ASPEN, one needs to add the required quantity for

411

successful convergence. In the Extractor column, mass loss is 7.857 kg/h (xsolvent = 0.0004 of

412

19255.4 kg of mass flow) while in distillation column mass loss is 2.14×10-59 kg/h. Hence for

413

the material balance, the make-up solvent was added to the DES-1. The total number of stage

414

in extractor has been taken as seven while the operational conditions used for the extractor

415

column was p = 1 atm and T = 303.15 K. As defined earlier, the sensitivity analysis tool was

416

adopted to reveal the information about the recovery of 1-butanol and flow rate of solvent

417

(DES) for a fixed number of stages in extractor. The results of sensitivity analysis were depicted

418

in Figure 10(1-butanol recovery vs solvent flow rate). It has been observed that for a 100 %

419

recovery of 1-butanol, an amount of 6,850 kg/h of DES-1 is required. Further, the optimal

420

solvent (DES) was found to be 2,500 kg/h for the recovery of 0.999 w/w 1-butanol from the 14 ACS Paragon Plus Environment

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421

extractor. In this case, also DESIGN SPEC has been used for maintaining the mass fraction of

422

1-butanol in the distillate at 0.86. A minimum number of stages (NDistillation= 54) have been

423

obtained for a reflux ratio of 2.26 after the rigorous optimization of the distillation column. The

424

diameter of the column (DDistillation) thereafter was found to be 1.7 m.

425

For the recovery and reuse of solvent in the extraction process, a distillation column

426

(RadFrac) has been utilized.43,44 The required height of the distillation column was calculated

427

by equation 6.60,61 It should be mentioned that the total height of distillation column is 20%

428

higher than the actual height(equation 6), whereas the gap between the plates is kept as 0.61

429

m.

H  1.2  0.61  ( NT  2)

430

(6)

431

Here NT represents the total number of stages. It should be noted that for a lesser number of

432

stages requires a higher reboiler heat duty. This shall lead to an increase in the segment

433

diameter and heat exchanger zone.49 Two DESIGN SPEC were used in these simulations. These

434

were used for optimizing the distillate rate and the reflux ration by fixing the mass fraction of

435

butanol as 0.86.60,62

436

Table 8 shows the results of 1-butanol recovery for the different streams where it was

437

observed that ~99.99% of 1-butanol was recovered using DES-2 as a solvent from the extractor.

438

The extract stream was then sent to the distillation column where butanol was separated as the

439

top product and solvent as the bottom product. This gave a weight fraction at ~0.86 w/w of

440

butanol in the distillate or the top product. The bottom product (solvent) is then fed to a cooler

441

to reduce its temperature to 303.15 K. Thereafter the solvent was recycled back to the extractor

442

after addition of makeup solvent. As observed with DESs, the final concentrations of the

443

raffinate phase (in extractor) and extract phase are in-line with our experimental outcome

444

(Table 2 and 3).

445

Comparison of DES and Conventional Solvent. Table 9 shows the comparison of

446

solvent DES-1 and conventional solvent (i.e., mesitylene), for the same feed 25000 kg/h (water

447

= 0.8 w/w and butanol = 0.2 w/w) and the same number of extractor stages which is seven. It

448

has been observed that the solvent DES-1 requires a flow of 2500 kg/h with a reflux ratio at

449

2.26 and a reboiler duty of 5135.79 KW. This is almost one-tenth as compared to mesitylene

450

(i.e., in terms of flow rate). Further, the number of stages in the distillation column is also the

451

least at 54. Hence, economically DES-1 is the preferred solvent for the extraction of the lower



452

alcohols as compared to the conventional solvent i.e. mesitylene and DES-2. In terms of TAC

453

also it can be termed that DES-1 is more efficient than mesitylene or DES-2.

454

CONCLUSIONS

455

In the current study, MD simulations have been employed to evaluate the experimental phase

456

equilibria of DES-alcohols-water by means of their structural and dynamic properties. The

457

deviation between the experimental and MD simulated phase equilibrium data was less than

458

6%. MD simulated non-bonded interaction energies were calculated for the ternary system and

459

it was found that the interaction energy between DES-1–alcohol is much stronger than DES-

460

1–water, water–alcohol, and water–DES-1. The higher DES-1–alcohol interaction energy

461

signified the fact that the alcohols are more attracted towards the DES phase. In DES-1–

462

alcohols system, 1-butanol had higher extraction efficiency than 1-propanol and ethanol due to

463

lower solubility of 1-butanol in water and higher interactions with DES-1. Among the DESs,

464

DES-2 have shown a higher extraction capacity for 1-butanol. The results of SDF revealed that

465

the surfaces of 1-butanol from DES-2 are closely distributed around the super active site of

466

menthol than DES-1–1-butanol, thereby giving a higher extraction when compared to DES-2.

467

Moreover, the easier recovery of 1-butanol from the aqueous medium was due to the higher

468

values of selectivity for the solvents. To study the process economics, a hybrid extraction-

469

distillation framework was proposed in order to economically isolate 1-butanol utilizing DES-1

470

and DES-2. 86% of 1-butanol was recovered at an optimized conditions which includes a

471

solvent/feed ratio of 0.54. Therefore, the present work gave a comprehensive integration from

472

MD simulations to experiments and finally its process economics. Such type of studies will

473

help the scientific community in scaling up the experimental data generated in the laboratory.

474


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475

ASSOCIATED CONTENT

476

SUPPORTING INFORMATION

477

The chemical description, MD simulated densities and prepared DES 1H NMR plots are

478

provided in supporting information along with this manuscript.

479

AUTHORS INFORMATION

480

Authors E-mail and Corresponding Author (*)

481

E-mail: [email protected] (R. Verma)

482

E-mail: [email protected] (M. Mohan)

483

E-mail: [email protected] (V. V. Goud)

484

* E-mail:

485

AUTHOR CONTRIBUTIONS

486

§ R.

487

DISCLOSURE STATEMENT

488

No potential conflict of interest was reported by the authors

489

ORCID

490

Mr. Rupesh Verma

https://orcid.org/0000-0003-4296-8995

491

Dr. Mood Mohan

https://orcid.org/0000-0001-5937-9746

492

Dr. Vaibhav V Goud

https://orcid.org/0000-0001-7755-6451

493

Prof. Tamal Banerjee

https://orcid.org/0000-0001-8624-6586

494

ACKNOWLEDGMENTS

495

The authors acknowledge Centre for Instrument Facility (CIF), IIT Guwahati for providing

496

necessary experimental facilities. Computational time from Param-Ishan Supercomputer

497

facility of IIT Guwahati is also highly acknowledged.

[email protected], Tel.: +91-361-2582266; fax: +91-361-2582291 (T. Banerjee)

Verma and M. Mohan contributed equally to this work



498

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499

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fermentation economics. Food Bioprod. Process. 2005, 83 (1), 43-52.

603

(42) Liu, G.; Gan, L.; Liu, S.; Zhou, H.; Wei, W.; Jin, W. PDMS/ceramic composite

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membrane for pervaporation separation of acetone–butanol–ethanol (ABE) aqueous solutions

605

and its application in intensification of ABE fermentation process. Chem. Eng. Process. 2014,

606

86, 162-172.

607

(43) Chen, Y. C.; Li, K. L.; Chen, C. L.; Chien, I. L. Design and Control of a Hybrid

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Extraction–Distillation System for the Separation of Pyridine and Water. Ind. Eng. Chem. Res.

609

2015, 54 (31), 7715-7727.

610 611

(44) Seider, W. D.; Seader, J.; Lewin, D. R.; Widagdo, S. Product and Process Design Principles: Synthesis, Analysis and Evaluation. Wiley: 2010.

612

(45) Díaz, I.; Palomar, J.; Rodríguez, M.; de Riva, J.; Ferro, V.; González, E. J. Ionic liquids

613

as entrainers for the separation of aromatic–aliphatic hydrocarbon mixtures by extractive

614

distillation. Chem. Eng. Res. Des. 2016, 115, 382-393.

615

(46) Ferro, V.; Ruiz, E.; De Riva, J.; Palomar, J. Introducing process simulation in ionic

616

liquids design/selection for separation processes based on operational and economic criteria

617

through the example of their regeneration. Sep. Purif. Technol. 2012, 97, 195-204.

618

(47) Ferro, V.; De Riva, J.; Sanchez, D.; Ruiz, E.; Palomar, J. Conceptual design of unit

619

operations to separate aromatic hydrocarbons from naphtha using ionic liquids. COSMO-based

620

process simulations with multi-component “real” mixture feed. Chem. Eng. Res. Des. 2015,

621

94, 632-647.

622

(48) De Riva, J.; Ferro, V.; Moreno, D.; Diaz, I.; Palomar, J. Aspen Plus supported conceptual

623

design of the aromatic–aliphatic separation from low aromatic content naphtha using 4-methyl-

624

N-butylpyridinium tetrafluoroborate ionic liquid. Fuel Process. Technol. 2016, 146, 29-38.

625

(49) Verma, R.; Dehury, P.; Bharti, A.; Banerjee, T. Liquid-liquid extraction, COSMO-SAC

626

predictions and process flow sheeting of 1-butanol enhancement using mesitylene and oleyl

627

alcohol. J. Mol. Liq. 2018, 265, 824-839.

628

(50) de Riva, J.; Suarez-Reyes, J.; Moreno, D.; Díaz, I.; Ferro, V.; Palomar, J. Ionic liquids

629

for post-combustion CO2 capture by physical absorption: Thermodynamic, kinetic and process

630

analysis. Int. J. Greenh. Gas Con. 2017, 61, 61-70.



631

(51) Larriba, M.; de Riva, J.; Navarro, P.; Moreno, D.; Delgado-Mellado, N.; García, J.; Ferro,

632

V. R.; Rodríguez, F.; Palomar, J. COSMO-based/Aspen Plus process simulation of the

633

aromatic extraction from pyrolysis gasoline using the {[4empy][NTf2]+[emim][DCA]} ionic

634

liquid mixture. Sep. Purif. Technol. 2018, 190, 211-227.

635

(52) Frisch, M.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb M. A.; Cheeseman, J.

636

R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li,

637

X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, G.; Sonnenberg, J. L.; Hada, M.;

638

Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao,

639

O.; Nakai, H.; Vreven, M. T.; A, J. J.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; E.

640

Brothers; Kudin, K. N.; Staroverov, V. N.; Keith, T.; Kobayashi, R.; J. Normand;

641

Raghavachari, K.; Rendell, A.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Rega, N.;

642

Millam, J. M.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.;

643

Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski,

644

J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg,

645

J. J.; Dapprich, S.; Daniels, A. D.; Farkas, O.; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox

646

, D. J. Gaussian 09 Revision B. 01, Gaussian, Inc, Wallingford, CT, DOI. 2010.

647 648

(53) Schäfer, A.; Horn, H.; Ahlrichs, R. Fully optimized contracted Gaussian basis sets for atoms Li to Kr. J. Chem. Phys. 1992, 97 (4), 2571-2577.

649

(54) Sosa, C.; Andzelm, J.; Elkin, B. C.; Wimmer, E.; Dobbs, K. D.; Dixon, D. A. A local

650

density functional study of the structure and vibrational frequencies of molecular transition-

651

metal compounds. J. Phys. Chem. 1992, 96 (16), 6630-6636.

652 653 654 655 656 657 658 659 660 661 662 663

(55) Kundu, D.; Banerjee, T. Multicomponent vapor–liquid–liquid equilibrium prediction using an a priori segment based model. Ind. Eng. Chem. Res. 2011, 50 (24), 14090-14096. (56) Bharti, A.; Kundu, D.; Rabari, D.; Banerjee, T. Phase Equilibria in Ionic Liquid Facilitated Liquid–Liquid Extractions. CRC Press: 2017. (57) Klamt, A. Conductor-like Screening Model for Real Solvents: A New Approach to the Quantitative Calculation of Solvation Phenomena. J. Phys. Chem. 1995, 99 (7), 2224-2235. (58) Lin, S.-T.; Sandler, S. I. A priori phase equilibrium prediction from a segment contribution solvation model. Ind. Eng. Chem. Res. 2002, 41 (5), 899-913. (59) Joback, K. G.; Reid, R. C. Estimation of pure-component properties from groupcontributions. Chem. Eng. Commun. 1987, 57 (1-6), 233-243. (60) Luyben, W. L. Distillation design and control using Aspen simulation. John Wiley & Sons: 2013.


Page 22 of 43

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664 665 666 667


(61) Luyben, W. L.; Chien, I.-L. Design and control of distillation systems for separating azeotropes. John Wiley & Sons: 2011. (62) Luyben, W. L. Comparison of extractive distillation and pressure-swing distillation for acetone/chloroform separation. Comput. Chem. Eng. 2013, 50, 1-7.

668

(63) Pathak, A. S.; Agarwal, S.; Gera, V.; Kaistha, N. Design and control of a vapor-phase

669

conventional process and reactive distillation process for cumene production. Ind. Eng. Chem.

670

Res. 2011, 50 (6), 3312-3326.


ACS Sustainable Chemistry & Engineering 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

671

Table 1. Compound Name, Solubility, Boiling Point (B. P.), Purities and Source of the Chemicals Used in this Work Sl. no.

Compound name

Solubility in water (g/L)

B.P. (K)

Densitya (g/cm3)

Purity

Source of chemical Sigma Aldrich, Germany

1.

Menthol

0.42

487.75

0.890

≥95%

2.

Decanoic acid

0.062

541.85

0.893

≥98%

3.

Lauric acid

0.059

572.05

0.883

≥99%

4.

Ethanol

infinite

351.39

0.789

≥99.9%

Merck, India

5.

1-propanol

infinite

371.15

0.803

≥99%

Merck, India

6.

1-butanol

75

390.85

0.810

≥99%

Merck, India

7.

DES-1 (Menthol: lauric acid)

NMb

566.44c

0.894

≥98%

Prepared in present work

NMb

561.56c

0.896

≥99%

Prepared in present work

8. 672

Page 24 of 43

a Density

DES-2 (Menthol: decanoic acid)

Tokyo Chemical Industry, Japan Merck, Germany

measured at T = 298.15 K; b Not measured; c Calculated from Joback-Method (Joback and Reid, (1987)59


Page 25 of 43 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


673

Table 2. Experimental, MD, and ASPEN Plus Simulated Liquid-Liquid Equilibrium Data for DES-1 (1)–Alcohol (2)–Water (3) Ternary System

674

at T= 303.15 K and p = 1 atma,b

Alcohol type

1-butanol

1-propanol

Ethanol

675 676

a RMSD b RMSD

Type of data

DES–rich phase

Water–rich phase

xDES

xalcohol

xwater

xDES

xalcohol

xwater

Exp.

0.223

0.584

0.193

0.022

0.011

MD

0.284

0.542

0.174

0.007

ASPEN

0.197

0.476

0.327

Exp.

0.14

0.452

MD

0.282

ASPEN

Distribution coefficient (β)

Selectivity (S)

0.967

53.09

266.00

0.096

0.897

5.65

28.94

0.000

0.006

0.994

81.95

249.15

0.408

0

0.049

0.951

9.22

21.50

0.491

0.227

0.014

0.127

0.859

3.87

14.63

0.138

0.471

0.391

0.000

0.0238

0.976

19.79

49.36

Exp.

0.243

0.419

0.338

0

0.143

0.857

2.93

7.43

MD

0.281

0.481

0.238

0.016

0.135

0.849

3.56

12.71

ASPEN

0.214

0.458

0.329

0.000

0.046

0.954

10.02

29.10

from MD:1-butanol= 5.51%; 1-propanol=10.74%; Ethanol= 5.11% from ASPEN Plus: 1-butanol= 7.26%; 1-propanol=1.78%; Ethanol= 5.98%


ACS Sustainable Chemistry & Engineering 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

Page 26 of 43

677 678

Table 3. Experimental, MD, and ASPEN Plus Simulated Liquid-Liquid Equilibrium Data for DES (1)–1-butanol (2)–Water (3) Ternary Systems

679

at T= 303.15 K and p = 1 atma,b

DES type

Type of data

DES–rich phase xDES

Water–rich phase

xalcohol

xwater

xDES

xalcohol

xwater

Distribution coefficient (β)

Selectivity (S)

DES-1 (1)– 1-butanol (2)– Water (3) DES-1

Exp.

0.223

0.584

0.193

0.022

0.011

0.967

53.09

266.00

MD

0.284

0.542

0.174

0.007

0.096

0.897

5.65

28.94

ASPEN

0.197

0.476

0.327

0.000

0.006

0.994

81.95

249.15

DES-2 (1) – 1-butanol (2)– Water (3) DES-2

680 681

a RMSD b RMSD

Exp.

0.069

0.662

0.269

0.010

0.010

0.980

66.20

241.17

MD

0.143

0.622

0.235

0.003

0.062

0.935

10.03

39.92

ASPEN

0.067

0.565

0.368

0.000

0.008

0.992

71.31

192.6

from MD:DES-1 = 5.51%; DES-2 = 4.66% from ASPEN Plus:DES-1 = 7.26%; DES-2 = 5.66%


Page 27 of 43 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


682

Table 4. MD Simulated Interaction Energies (kJ/mole) between the Different Ionic Pairs of

683

DES-1–Alcohols–Water for Ternary Systems Calculated at T = 303.15 K and p = 1 atm Ionic pair

Electrostatic interactions (Eelec)

van der Waals interactions (EvdW)

Total non-bonded interactions (Etotala)

DES (1)– 1-butanol (2)– water (3) Lauric acid– 1-butanol

-32.64

-50.31

-82.96

Menthol – 1-butanol

-57.89

-72.79

-130.69

Lauric Acid-water

-33.55

-5.19

-38.74

Menthol-water

-19.52

-1.79

-21.31

Water – 1-butanol

-9.10

-0.68

-9.78

DES – 1-butanol

-90.54

-123.11

-213.65

DES-water

-53.07

-6.98

-60.05

Water-DES

-3.79

-0.50

-4.29

DES (1)– 1-propanol (2)– water (3) Lauric acid– 1-propanol

-28.65

-40.52

-69.17

Menthol – 1-propanol

-46.90

-58.54

-105.43

Lauric acid-water

-36.00

-4.47

-40.47

Menthol-water

-31.07

-3.04

-34.11

Water – 1-propanol

-10.04

-1.02

-11.06

DES – 1-propanol

-75.55

-99.06

-174.60

DES-water

-67.07

-7.52

-74.59

Water-DES

-4.97

-0.55

-5.52

DES (1)– ethanol (2)– water (3) Lauric acid-ethanol

-27.66

-34.17

-61.83

Menthol-ethanol

-42.41

-47.38

-89.79

Lauric acid-water

-40.19

-5.20

-45.39

Menthol-water

-31.12

-3.33

-34.45

Water-ethanol

-10.53

-0.99

-11.52

DES-ethanol

-70.07

-81.56

-151.62

DES-water

-71.31

-8.54

-79.84

Water-DES

-5.09

-0.61

-5.70

684



Page 28 of 43

685

Table 5. Comparison of MD Simulated Interaction Energies (kJ/mole) between the Different

686

Ionic Pairs of DES (1 and 2)–1-butanol–Water Calculated at T = 303.15 K and p = 1 atm Ionic pair

Electrostatic interactions (Eelec)

van der Waals interactions (EvdW)

Total non-bonded interactions (Etotala)

DES (1)– 1-butanol–water Lauric acid– 1-butanol

-32.64

-50.31

-82.96


-57.89

-72.79

-130.69

Lauric acid-water

-33.55

-5.19

-38.74

Menthol-water

-19.52

-1.79

-21.31

Water – 1-butanol

-9.10

-0.68

-9.78

DES – 1-butanol

-90.54

-123.11

-213.65

DES-water

-53.07

-6.98

-60.05

Water-DES

-3.79

-0.50

-4.29

DES (2)– 1-butanol–water Decanoic acid- 1-butanol

-26.25

-50.71

-76.97


-25.13

-44.92

-70.05

Decanoic acid-water

-21.58

-1.56

-23.14

Menthol-water

-14.79

-2.59

-17.38

Water – 1-butanol

-8.25

-0.68

-8.93

DES – 1-butanol

-51.39

-95.63

-147.02

DES-water

-36.38

-4.15

-40.53

Water-DES

-2.60

-0.30

-2.90

687


Page 29 of 43 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


688

Table 6. Self-Diffusivity Coefficient (D) of Different Molecular Species in Different Ternary

689

Systems at 303.15 K Sl. No.

Molecule species

D  10−10 (m2/s) 0-15 ns

15-30 ns

30-45 ns

DES (1)–1-butanol–water 1.

Lauric Acid

0.22

0.148

0.273

2.

Menthol

0.259

0.232

0.284

3.

1-Butanol

1.053

1.202

0.919

4.

Water

15.813

11.069

11.462

DES (1)–1-propanol–water 5.

Lauric Acid

0.268

0.39

0.499

6.

Menthol

0.303

0.43

0.419

7.

1-Propanol

2.087

1.9

2.397

8.

Water

9.432

10.032

11.700

DES (1)–ethanol–water 9.

Lauric Acid

0.453

0.303

0.396

10.

Menthol

0.337

0.252

0.389

11.

Ethanol

3.033

3.655

3.220

12.

Water

11.837

15.112

14.124

690



Page 30 of 43

691

Table 7. Self-Diffusivity Coefficient (D) Comparison between the Different Molecular Species

692

of DES (1 and 2)–1-butanol–Water in Ternary Systems at 303.15 K Sl. No.

Molecular species

D  10−10 (m2/s) DES(1)–1-butanol–water

DES(2)–1-butanol–water

2.

Lauric acid/ decanoic acid Menthol

3.

1-butanol

0.919

2.018

4.

Water

11.462

13.816

1.

0.273

1.054

0.284

1.010

693


Page 31 of 43 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

694


Table 8: Stream Results for 1-butanol Recovery Using DES-1 as a Solvent Stream Name

Feed

DES-Solvent

Extract

Raffinate

D

B

Make-Up

Component Mass Flow (kg/h) DES

0

2509.734

2501.877

7.8572

0.00

2501.877

7.857

1-butanol

5000

206.5202

5124.868

81.65179

4918.352

206.5176

0.00

Water

20000

0.00

834.1046

19165.8954

834.1046

0

0

Component Mass Fraction DES

695 696 697

0

0.9239

0.2957

0.0004

0

0.9237

1

1-butanol

0.20

0.0760

0.6057

0.0042

0.8550

0.0762

0

Water

0.80

0

0.0986

0.9953

0.1449

0

0

Mass flow (kg/h)

25000

2716.254

8460.85

19255.4

5752.457

2708.388

7.857

Volume flow (lpm)

437.61

53.83

178.17

325.64

124.05

63.67

0.15

T (°C)

30.00

30.00

32.40

32.50

94.10

202.10

30.00

P (bar)

1.01

1.01

1.01

1.01

1.01

1.38

1.01

Molar enthalpy (cal/mol)

-68681.90

-55259.50

-71357.50

-68135.10

-71352.70

-40841.30

-50866.10

Molar entropy (cal/mol-K)

-43.80

-214.03

-108.24

-38.61

-85.16

-177.92

-230.59

Optimal Results: Extractor Column: P = 1 atm, T = 30 °C, NExtractor= 7, Distillation Column: NDistillation=54, Nfeed=23, Distillate rate: 5752.46 kg/h, Reflux ratio: 2.26, DDistillation: 1.7 m



698

699 700

Page 32 of 43

Table 9: Overall Comparison of DESs as well as Mesitylene for the Extraction of 1-butanol Solvent name

Mesitylene

DES-1

DES-2

Feed flow [kg/h] [W= 0.8, Bt=0.2 w/w]

25000

25000

25000

Solvent required [kg/h]

30,000

2500

2498.92

RR

2.46

2.26

2.01

NT extractor

7

7

7

NT Dist. Col.

48

54

44

NF Dist. Col.

47

23

26

D(m) Dist. Col.

2.05

1.7

1.68

Recovered BuOH Dist Col..[kg/h]

4866.0

4918.35

4916.91

Solvent lossDist.Col. [kg/h]

14.71

0

0

Reboiler duty (kw)

5673.23

5135.79

4733.21

Energy (103 $/year)

760.25

688.23

634.28

Capital (103 $/year)

1380.56

1115.90

987.57

TACDist-Col a (106 $/year)

1.220

1.0600

0.963

TACExt-Col b (103 $/year)

13.746

8.752

8.768

Pump capital cost c (103 $/year)

8.585

8.585

9.577

Pump energy cost c (103 $/year)

2.692

1.008

0.525

Cooling water cost c (103 $/year)

11.715

1.475

1.518

TAC overall d (106 $/year) 1.257 1.08 0.984 a 60 b 44 Based on the methodology given by Luyben (2013) ; Seider et al. (2010) ; c Pathak et al. (2011)63; d Chen et al. (2015)43

701


Page 33 of 43 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


OL1 1:2 DES -1 HLO

Lauric acid

HDO

HMO OM1

1 1: -2 S DE

OD1

Menthol

Decanoic acid OB1

OP1

OW1 OE1

HBO

HPO HEO

702

1-butanol

1-propanol

Ethanol

HW1

Water

703

Figure 1. Optimized molecular geometries of different structures of the investigated

704

compounds along with their atom notations

705



706 707

Figure 2. Correlation between experimental and MD simulated tie lines data points for the

708

ternary system composed of DES-1 – 1-butanol - water and DES-2 – 1-butanol -water at 303.15

709

K and 1 atm


Page 34 of 43

Page 35 of 43

Ternary system

28.94

30

(b)

DES 1-Butanol-Water DES 1-Propanol-Water DES 1-Ethanol-Water 0 -9.8

-30

-4.3

-11.1

-5.5

-11.5

25

-5.7

Distribution coeffcient Selectivity

-60

-60.1 -74.6

-90

-79.8

 and S

20

Interaction energy (kJ/mole)

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


-120

-180

-240

12.71

10

-150

-210

14.63

15

-174.6

-213.6

5.65

-151.6

5

DES-Alcohol DES-Water Water-Alcohol Water-DES (a)

0

3.87

3.56

DES 1-Butanol-Water DES 1-Propanol-Water DES 1-Ethanol-Water

Ternary system

710

Figure 3. Correlation between MD simulated (a) non-bonded interaction energy and (b) distribution coefficient and selectivity of different DES-

711

1-alcohol-water systems at 303.15 K at 1 atm pressure

712



12.0

(a)

10.5

4.0

Menthol1-butanol Menthol1-propanol MentholEthanol

9.0

g(r)

8 6

4.5

2

2.4

2.0

1.2

1.8 0.6 0.0 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6

0.5

0.0

0.0 0

713

2.5

1.0

0 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6

1.5

3.0

1.5

4

3.0

3.6

3.0

10

6.0

Water1-butanol Water1-propanol WaterEthanol

(b)

3.5

12

7.5

g(r)

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

Page 36 of 43

1

2

3

4

5

6

7

8

9

10

0

r (Å)

1

2

3

4

5

6

7

8

9

10

r(Å)

714

Figure 4. Atom–atom radial distribution function (RDF) plots between the different molecules present in the DES-1 – alcohols-water ternary

715

system (a) Oxygen (OM1) atom of menthol with hydroxyl proton (HBO, HPO, and HEO) of alcohol (b) Oxygen (OW1) atom of water with

716

hydroxyl proton (HBO, HPO, and HEO) of alcohol


Page 37 of 43 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


(a)

(b)

(c)

(d)

717

Figure 5. Spatial Distribution function (SDF) of (a) 1-butanol, 1-propanol and ethanol around

718

menthol at an isovelue of 3.2 particle nm–3 in DES-1– alcohols–water system (b) 1-butanol and

719

water around menthol at an isovelue of 2 particle nm–3 in DES-1–1-butanol–water (c) 1-butanol

720

and menthol are around water at an isovelue of 2 particle nm–3 for menthol and 0.4 particle

721

nm–3 for 1-butanol in DES (1)–1-butanol–water and (d) 1-butanol (green is DES-1 and violet

722

is DES-2) around menthol molecule an isovelue of 4 particle nm–3 in in DES (1 and 2)–1-

723

butanol–water. Green surface refers to 1-butanol, blue surface refers to 1-propanol, orange

724

surface refers to ethanol, purple surface refers to water, sky blue surface refers to menthol of

725

DES, and violet surface refers to 1-butanol of DES (2)–1-butanol–water system



1050

600 (a)

(b)

900

500

750

1-butanol 1-propanol Ethanol

2

600 450 300

300 200

150

100

0

0

0.5

1.0

1.5

2.0

1-butanol in DES 1 1-butanol in DES 2

400

MSD (Å )

2

MSD (Å )

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

Page 38 of 43

2.5

3.0

3.5

4.0

4.5

5.0

0.5

Time step (ns)

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Time step (ns)

726 727

Figure 6. MSD plot of (a) 1-butanol, 1-propanol, and ethanol in DES–alcohols–water ternary system (b) comparison of 1-butanol MSD’s in DES

728

(1 and2) –1-butanol–water ternary system


Page 39 of 43 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


Feed-aqueous phase

1-butanol

Liquid-Liquid Extraction (LLE)

729 730

Extract

Solvent recovery and 1-butanol separation via distillation

Solvent recycled Figure 7. Sequential flow chart for optimization37,41

731



732 733

Figure 8. Solution strategy for 1-butanol extraction (EXT: extractor; NT= number of stages)49


Page 40 of 43

Page 41 of 43 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


734 735

Figure 9. Hybrid extraction distillation process flow sheet for the separation of 1-butanol from

736

the aqueous mixture using DES-1 as a solvent



5000

1-butanol (kg/h) in extract phase

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

Page 42 of 43

4900

4800

4700

4600

4500 0

500

1000

1500

2000

2500

3000

3500

Solvent DES (kg/h)

737 738

Figure 10. LLE of 1-butanol using sensitivity analysis for obtaining the optimum DES-1

739

(solvent) flow rate


Page 43 of 43

Table of Content

740 ASPEN Plus

Molecular Dynamics

DIST-COL

FEED Feed

PUMP Pump EXTRACT Extract

Feed RadFrac

FEED-DIS

RadFrac

DIST RadFrac-T EXT-COL

Ext

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


RAFFINAT Raffinate RAFINATE BOT RadFrac-B

Mixer MIXER

COOLER Cooler SOLVENT Solvent

MIX-IN Mix-In MAKE-UP Makeup

DES

Alcohol

Water

741 742

The current work depicts the multiscale modeling strategies involving Experimental LLE of

743

DES-alcohol-water along with MD simulations and ASPEN simulated flowsheet.


Operational Strategies and Comprehensive Evaluation of Menthol

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