Multiphase equilibria modeling of fast pyrolysis bio-oils. GCA-EOS

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Thermodynamics, Transport, and Fluid Mechanics

Multiphase equilibria modeling of fast pyrolysis bio-oils. GCA-EOS extension to lignin monomers and derivatives Yannik Ille, Francisco Adrián Sánchez, Nicolaus Dahmen, and Selva Pereda Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.9b00227 • Publication Date (Web): 28 Mar 2019 Downloaded from http://pubs.acs.org on April 1, 2019

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Industrial & Engineering Chemistry Research

Multiphase equilibria modeling of fast pyrolysis biooils. GCA-EOS extension to lignin monomers and derivatives Yannik Ille,1 Francisco A. Sánchez,2 Nicolaus Dahmen,1 Selva Pereda2,3* 1

Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

Planta Piloto de Ingeniería Química (PLAPIQUI), Chemical Engineering Department, Universidad Nacional del Sur (UNS) - CONICET, Camino La Carrindanga Km7, 8000B Bahía Blanca, Argentina 2

3 Thermodynamics

Research Unit, School of Engineering, University of KwaZulu-Natal, Howard College Campus, King George V Avenue, Durban 4041, South Africa *[email protected]

Abstract Fast pyrolysis is a promising route to use biomass as a source of renewable energy and chemicals. For economic feasibility, this process has to be optimized in regard of product yield and handling. One of the big challenges in detailed process design is the complexity of biomass derived liquid mixtures, since they comprise hundreds of different organooxygenated chemicals, such as alcohols, ketones, aldehydes, furans, sugar derivatives and also aromatic components if lignocellulosic biomass is processed. To model such a system, and predict its phase behavior, an advanced thermodynamic model is required. In this work, we extend the GCA-EOS to lignin monomers and their aromatic derivatives. Results show that GCA-EOS is able to handle this new family of organic compounds, not only their vapor-liquid equilibrium with other molecules typically found in the fast pyrolysis bio-oils, but also the liquid-liquid and solid-liquid equilibria. Keywords: lignocellulosic biomass, fast pyrolysis, bio-oil, GCA-EOS 1. Introduction In the last years, bio-oils from fast pyrolysis have become an attractive alternative to produce liquid intermediate energy carriers, which can be further process to biofuels and chemicals by appropriate conditioning and upgrading. In absence of oxygen, fast pyrolysis transforms solid organic feedstock into a mixture of liquid, gaseous and solid products at high temperatures, usually around 500 °C. One of the key features of fast pyrolysis is that 1 ACS Paragon Plus Environment

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it allows transforming a low density feedstock (biomass) into a liquid of higher density, easier to transport to upgrading plants for further conversion or usage.1 On the other hand, the solid char and non-condensable gas are usually used as energy source. By a rapid heating-up, short reactor residence times, and an instant product cooling, numerous organic compounds and intermediates are produced in the liquid condensate, also called fast pyrolysis bio-oil (FPBO). FPBO is a dark viscous fluid composed by several hundreds of compounds obtained from the degradation of lignocellulosic materials.2,3 The complexity of the bio-oils is such that they may contain emulsified phases, comprising aqueous droplets, char particules, and waxy materials.4 At present, most of the ongoing design efforts aim to maximize the liquid fraction in the product during the reaction stage.5–7 However, in order to achieve this objective, a fundamental understanding of all the stages of the process is required. Even though fast pyrolysis has been investigated since decades, most of the process operating units still are, to a certain extent, black boxes. The main reasons that cause this are the variable and not fully known feedstooks, and the complex reactions network, also not known in detail. On the other side of the process, FPBO are extremely difficult to characterize, and only the more volatile fractions are known. Ille et al.

8

proposed to deal with this problem by fitting a surrogate mixture to

experimental phase equilbirum data of FPBOs. From an engineering perspective, the simultaneously formed and interacting phases complicate the problem. For example, besides the already mentioned phases, a substantial aerosol formation is observed during the condensation of fast pyrolysis vapors.9 This, together with the intrinsic complexity of the bio-oil mixture, complicate the modeling of the process, which is esencial for its design and optimization. It is worth highlighting that similar challenges exist in other lignocellulosic biomass conversion processes like hydrothermal liquefaction10 or hydrogenolysis.11

The components comprising the bio-oil are diverse and depict a wide volatility range. Among them, organic acids, esters, alcohols, ethers, furans, sugars, water are found, as well 2 ACS Paragon Plus Environment

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as monomeric and oligomeric lignin derivatives. Lignin is an amorphous polymer consisting of three primarily monomers, known as monolingols (see Figure 1a), which are methoxylated phenylpropene structures: p-coumaryl, coniferyl, and sinapyl alcohols. On the other hand, in bio-oils, lignin derivatives are also aromatic compounds but with shorter pending oxygenated chains (see Figure 1b). To a small extent, aromatic compounds can also be formed and found in cellulose conversion.12

(a) Monolignols

(b) Lignin monomer derivatives

HO

OH p-courmaryl alcohol

O OH guaiacol

O OH 4-ethylguaiacol

OH

O

OH

4-methylguaiacol

O

O

isoeugenol

O OH syringol

HO O

O

OH

coniferyl alcohol

OH

O

4-vinyl guaiacol

O

OH

vanillin

O

OH

O

methyl syringol

O

OH

O

4-ethyl syringol

HO

O OH sinapyl alcohol

OH

O

vinyl syringol

O O

O

O

O

O OH 4-propenyl syringol

O

O OH acetosyringon

O

O OH syringaldehyde O

O OH syringyl acetone O

Figure 1. (a) three primary monomers involved in the biosynthesis of lignin;13 (b) fast pyrolysis lignin monomers derivatives.14

All the compounds shown in Figure 1 are complex polyfunctional molecules and strongly polar, capable of self- and cross-associate with many other molecules within the bio-oil mixture. In consequence, it is important to select a thermodynamic model that handles properly these characteristics. Most of the common thermodynamic models rely on the correlation of binary molecular interaction parameters, which is a cumbersome task, given the high number of compounds comprising the bio-oil. To our knowledge, there is no

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binary phase equilibrium experimental data of most of the compounds listed in Figure 1. Very often not even pure compound data is available. Nonetheless, Figure 1 points out that all lignin monomers are composed by the same functional groups, namely, aliphatic, olefinic and aromatic carbons (CHx, CHx=CHy, ACH and AC, and ACCHx, respectively), aliphatic and aromatic alcohols (CHxOH and ACOH, respectively), ketones (CHxCO) and methoxy (ACOCH3) groups. Therefore, by means of a limited number of functional groups, it is possible to construct many complex molecules that are present in mixtures of pyrolytic lignin. In this context, a group-contribution model is a well suited alternative, in order to predict the properties of a complex mixture with a low number of parameters.

Between the available group-contribution models, the Modified UNIFAC model15,16 is the most well-known, having already a large table of interaction parameters. Nonetheless, being UNIFAC an excess Gibbs energy model, it still needs the vapor pressure of pure compounds, which does not exist for several of those comprising the bio-oil and, in case it does, data is usually reported in a limited range of temperature and pressure. Moreover, UNIFAC does not specifically consider the presence of hydrogen-bond formation within the mixture. In that sense, the GCA-EOS17 is a group contribution equation of state, which takes into account specific association interations. Moreover, in the last years, several other group contribution thermodynamic models have also been developed, namely, GCPPC-SAFT,18,19 GC-SAFT-VR,20,21 and SAFT-γ22,23 equations of state (EOS). It is clear that all these models belong to the SAFT family, therefore, they can handle the phase equilibria of mixtures comprising size-asymmetric and polar compounds, as well as the GCA-EOS does.24 Nonetheless, there are some distinctive features among them, like the number of adjustable parameters, the inability to simultaneously represent critical data and volumetric properties, and the intrinsic complexity of the model. The latter is crucial in the selection of the thermodynamic model, since typical model mixtures for biomass derivatives require ten or more key components to describe the mixture distillation profile. Therefore, even for simple process modeling, the calculation effort can get 4 ACS Paragon Plus Environment

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unreasonably high. Another important aspect for selection of the group contribution thermodynamic model is its capability to represent the phase equilibria of mixtures comprising polyfunctional compounds.

Within the aforementioned models, the GCA-EOS fulfills these premises as it is a simple yet roboust model. In particular, a special effort is being done to extend the GCA-EOS to biorefineries, i.e to deal with mixtures from renewable materials of different sources, biofuels, and oxygenated chemicals, besides hydrocarbons. During the last decade, the GCA-EOS has been applied to first and second generation biofuels, their blending with hydrocarbons, and in the processing of several natural products derived from vegetable oil, fruits and herbs. Consequently, the GCA-EOS table of parameters has been already extended to mixtures of aliphatic and aromatic hydrocarbons with ketones,25 alcohols and water,26 furans,27 and phenols.28 Moreover, previous works show its roboustness to deal with

polyfunctional

molecules

such

as

alkanolamines,24

furfuryl

alcohol

and

hydroxymethyl furfural,27 and more recently, glycol and its acetates.29

In this work, we extend the GCA-EOS table of parameters to lignin monomers and their derivatives in pyrolytic bio-oil. Based on the correlation of phase behavior of monooxyganted compounds, the model is challenged to predict the phase behavior of the polyoxygenated aromatic compounds under study.

2. Thermodynamic modeling The GCA-EOS17 is an extension to associating systems of the original GC-EOS,25 which is based on the Generalized van der Waals theory. In the GCA-EOS model, there are three contributions to the residual Helmholtz energy (AR) free volume (Afv), attractive (Aatt) and association (Aassoc):



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𝐴R = 𝐴fv + 𝐴att + 𝐴assoc

(1)

The free volume contribution is the extended Carnahan-Starling30 equation for mixtures of hard spheres developed by Mansoori et al.,31 which is characterized by one purecompound parameter: the critical diameter (dc). The attractive contribution to the residual Helmholtz energy, Aatt, accounts for dispersive forces between functional groups. It is a van der Waals expression combined with a density-dependent, local-composition mixing rule based on a group contribution version of the NRTL model.32 This term is characterized by the number of surface segments of each group (q), and the surface energy (g), which is temperature dependent. Furthermore, each binary group interaction is characterized by one symmetric interaction parameter (kij = kji), which can additionally be temperature dependent, and two binary non-randomness parameters (αij ≠ αji). Finally, the association term, Aassoc, is a group contribution version of the SAFT equation developed by Chapman et al.33 This term is characterized by two parameters: the energy (ε) and volume (κ) of association and is only required to describe components showing specific association interaction (hydrogen-bonding or solvation). A detailed list of GCA-EOS parameters is shown in Table 1. A more detailed explanation of the model can be found elsewhere.34

Table 1. GCA-EOS parameters. Contribution Free-volumea (Afv)

Parameter Pure compound

Attribute

Hard sphere diameter Critical temperature

dc Tc

Fixedc Fixed

Pure group

Reference temperature Surface area Energy

Ti* qi gii*,gii’,gii’’

Fixed Fixedd Adjustable

Binary

Energy interactione Non-randomness

kij*, kij’ αij αji

Adjustable Adjustable

Pure group

Self-association energy Self-association volume

εik-ik κik-ik

Fixed Fixed

Binary

Cross association energy Cross association volume

εik-lj κik-lj

Comb.rule/Adjustable Comb.rule/Adjustable

Attractiveb (Aatt)

Associatingb (Aassoc)

a Molecular

term. b Group contribution term. c Calculated from critical point conditions for molecular compounds, density or vapor pressure data for compounds described by GC. d Value taken from Bondi.35 In certain cases, this


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parameter can be adjustable if it is not available.

2.1. Parameterization procedure GCA-EOS parameterization is performed through the optimization of the following objective function: 𝑁𝐸𝑞

O.F. =

∑ 𝑒2eq, 𝑖

(2)

𝑖=1

where NEq is the number of binary equilibrium points fitted, and and eeq,i is the error between experimental and calculated data as follows:

𝑒2eq, 𝑖

= IFL𝑖

(

𝑃exp, 𝑖

2

)

𝑃exp, 𝑖 ― 𝑃calc, 𝑖

+ (1 ― IFL𝑖)

(

2

)

𝑥exp, 𝑖 ― 𝑥calc, 𝑖 𝑥exp, 𝑖

+

𝑤2𝑦

(

𝑦exp, 𝑖

2

)

𝑦exp, 𝑖 ― 𝑦calc, 𝑖

(3)

where P is the pressure, x and y the molar fraction in the solid and vapor phase, respectively, and wy is the weighting factor in y, which is set to 0.2.36 IFL is an auxiliary variable that sets the type of flash calculations: 0 for a TP flash and 1 for bubble point calculations. In this work, all binary vapor-liquid equilibria (VLE) systems are evaluated through bubble pressure calculation, while a TP flash37,38 is used for solid-liquid equilibria (SLE) data. In the case of VLE data, the objective function assesses both phases in equilibrium, while for SLE the solid solubility in the liquid phase is optimized. The objective function (Eq. (2)) is minimized drawing upon the Levenberg–Marquardt algorithm of finite difference coded in Fortran77. It is worth mentioning that when the equilibrium involves a pure solid phase, the fugacity of the solid solute can be calculated from its fugacity in the fluid phase in equilibrium. At SLE conditions, the fugacity of pure solute i in the solid phase can be related to the fugacity of the subcooled liquid by the following expression:39

ln𝑓S𝑖 (𝑇,𝑃) = ln𝑓L𝑖 (𝑇,𝑃) ―

Δ𝑣f,𝑖(𝑃 ― 𝑃t,𝑖) RT

―

Δ𝐻f,𝑖 1 1 ― 𝑅 𝑇 𝑇t,𝑖

(

)


(4)


where 𝑓S𝑖 and 𝑓L𝑖 are the fugacity of the solute i as pure solid and liquid, Pt,i and Tt,i are the triple-point temperature and pressure of solute i, while ∆Hf,i and ∆vf,i are the corresponding enthalpy and volume change of fusion. In practice, the triple-point temperature Tt,i is replaced by the normal melting point temperature Tf,i, because for most substances there is little difference between these properties. Moreover, the term corresponding to ∆vf,i (Poynting correction) can be neglected if the pressure difference is not extremely large. Since all SLE calculations are carried out at atmospheric pressure, the Poynting correction is disregarded in this work. Table 2 reports the melting temperature and heat of fusion of pure compounds used in the SLE calculations with the GCA-EOS equation.

Table 2. Heats of fusion and melting temperatures used in solid-fluid equilibrium calculations. Compound ∆Hf (J/mol) Tf (K) Source 40 anisol 11660 237.0 41 1,2-dimethoxybenzene (veratrole) 12600 295.7 42 phenol 11281 314.1 40 3-methylphenol (m-cresol) 8670 284.0 40 4-methylphenol (p-cresol) 9140 307.6 41 2-methoxyphenol (guaiacol) 12000 301.2 43 4-methoxyphenol (mequinol) 18300 328.3 43 1,2-benzenediol (catechol) 22540 377.7 41 1,4-butanediol 18700 292.9

For the optimization of the objective function (Eq. (2)), experimental phase equilibria data taken from the literature is used. The database contains approximately 1050 binary VLE, liquid-liquid equilibria (LLE), and SLE data points from which only a fraction is used in the model parameterization. All the groups needed to assemble lignin monomers were assessed in previous works. In this regard, Skjold-Jørgensen25 reports parameters for the non-associating groups, while Sánchez et al.28,34,44 those for phenol and anisol derivatives. In addition, Soria et al.26 provides parameters to model the aliphatic alcohol group and water. Therefore, this work only involves fitting binary interaction parameters of the


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attractive and association contributions (see Table 1). The parameterization procedure comprises the selection of key datasets to correlate the GCA-EOS parameters, avoiding an overfitted result. The parameter estimation is done separately for each family of functional groups, including, as few datasets as possible, to achieve a predictive model. Then, the model predictive capacity is challenged against the remaining datasets. If a dataset shows atypical deviations, it is included in the correlation database and the optimization is run again. If the new correlated dataset does not improve substantially the overall regression, the data set is moved back to the prediction database, and the parameters are restored to their original value. These two steps are repeated up to achieving a satisfactory result.

It is worth highlighting that there is a lack of data for several of the binary systems of interest, and in some cases there are only few VLE datasets. Therefore, the approach followed in this work is to use as few parameters as possible.

The association contribution takes into account the hydrogen-bond formation and solvation interactions between electron donors and acceptors. Since many of the functional groups involved in this work are polar, their cross-association parameters are needed. Typical examples are the alkoxy and ketone groups (electron donors) in mixtures with water, phenols or alcohols (electron donors and acceptors). In this work, all of the self-association and some cross-association parameters have been taken from literature,26,28,34,44 including those for water (H2O), alcohols (OH), and aromatic hydroxyl groups (AOH and o-AOH, where o-AOH is exclusive for ortho-substituted phenols). Figure 2 illustrates the group assembly of some of the molecules of interest, together with their corresponding association sites.

To assess each binary interaction, we first only fit the cross-association parameters (εki,lj and κki,lj), while those of the attractive term remained in their default values (kij* = 1, kij’ = αij = αji = 0). In general, fitting these two parameters the model is able to accurately



describe the phase behavior of the data in the correlation databank. As it is shown in the next section, in few cases we also correlate the temperature independent binary interaction parameter (kij*) to improve the model accuracy. In the case of orthosubstituted phenols, due to its hysteric hindrance to associate, we have assumed that the o-AOH group depicts an association volume slightly smaller than that of a regular phenol group AOH, while keeping the same association energy parameters. In GCA-EOS, this approach has been already successfully applied for alkyl-derivatives of phenol in mixtures with aromatic and aliphatic hydrocarbons and water28 and to mixtures involving secondary alcohols.29 Moreover, an analogous approach has been used for mixtures of phenol derivatives by Nguyen-Huynh et al.19 using the GC-PPC-SAFT EOS, though these authors differentiated between ortho, meta and para substitutions.

Figure 2. Group assembly of some lignin derivatives. Atractive groups circled with dotted lines and association sites (electron donors and acceptors) marked as (−) or (+). (a) Anisole: 5×ACH + ACOCH3, with two electron donor sites (b) Phenol: 5×ACH + ACOH, with 2 electron donors and 1 electron acceptor. (c) 2-Methoxyphenol (guaiacol): 4×ACH + ACOCH3 + ACOH, with 3 electron donors and 1 electron acceptor. Last, the free-volume contribution requires to assess the critical hard sphere diameter (dc), which is characteristic for the pure-compound molecular size and has no binary or higher-order parameters. There are three different ways to calculate dc of each component: (i) direct calculation with the values of critical temperature and pressure so that the model fulfills the critical point and its conditions (first and second derivatives of pressure with regard to volume equal to zero);25 (ii) fit dc to an experimental pure-


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component vapor pressure data point (Tsat, Psat);25 and (iii) computation with the correlation proposed by Espinosa et al.45 for non-volatile compounds. In this work, method (ii) has been used for all components but syringol for which, to our knowledge, there is no experimental data; therefore, we apply the fully predictive method (iii) in this case.

3. Results and discussions As mentioned in the previous section, to model the phase equilibria of lignin monomers by group contribution, several groups and their binary interactions are needed. Up to this work, all the groups and most of the binary interaction parameters of the GCA-EOS have been already defined elsewhere.25,26,28,34,44,46 Nonetheless, in this work, the binary interaction between aromatic hydroxyl and alkoxy groups are fitted, as well as that of these two groups with the alcohol and ketone groups. Figure 3 shows the groups needed to model lignin derived monomers together with the source of GCA-EOS parameters, those from previous works and the ones fitted in this work.

Figure 3. Group and binary interaction parameters of GCA-EOS needed to model lignin derived monomers. References in the matrix diaganol point to the source of pure group 11 ACS Paragon Plus Environment


parameters, while those in the rest of the matrix to the source of binary interaction parameters. TW: parameters fitted in this work.

In the following sections, we first show the results of the correlation of mono-functional oxygenated aromatic compounds, including binary VLE and SLE data. Also, we show the model robustness to predict other mono-functional oxygenated aromatic compounds not included in the parameterization of the model. Finally, we challenge the GCA-EOS to predict phase equilibria of poly-oxygenated aromatic compounds also with data not used during the parameterizaion.

3.1. Modeling of mono-oxygenated aromatics compounds Since mono-oxygenated aromatic compounds were modeled previously, we first validate the model accuracy to predict vapor pressure of these compounds, which in fact is the simplest challenge for the binary group interaction parameters with the aromatic ring. Figure 4 depicts the GCA-EoS description of vapor pressure of phenol, anisole and their alkyl-substituted derivatives.


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Figure 4. Vapor pressure of phenol derivatives. (a) Phenol and alkylphenols:47 () phenol, () 3-methylphenol, and () 3,5-dimethylphenol. (b) Phenol ethers48–56 () anisole, () ethoxybenzene, () 4-methylanisol, () 2,4-dimethylanisol. Solid lines: calculations with GCA-EOS with parameters from Sánchez et al.28,44



The parameterization of the model is performed using only 15% of the equilibrium data available, mainly involving binary mixtures with phenol and anisole, except for the binary mixture ethanol + veratrole (1,2-dimethoxybenzene), which is the only di-oxygenated compound included in the correlation. We avoid fitting the only data available for anisol + ethanol,57 because its correlation distorts the trend showed by the binary interaction parameters between the methoxy and the alcohol groups (methanol and higher alcohols). Tables Table 3 and Table 4 list the parameters fitted in this work. From these tables, it is clear that many of the associating binaries are described solely by fitting the cross association parameters, except for anisol + alcohols and phenol + methanol. Furthermore, the phase equilibrium of anisol with ketones is completely predicted, i.e. no interaction parameter is regressed to obtain a good prediction.

Table 3. Binary energy interaction parameters between attractive groups (𝑘′𝑖𝑗 = αij=αji= 0) Group

𝑘𝑖𝑗∗

Fitted experimental data

i

j

ACOCHx

CH3OH

0.8015

VLE anisole + methanol58

C2H5OH

0.90

VLE ethanol + veratrole59

CH2OH

0.7149

ACOH

1

VLE anisole + 1-propanol60 and 1pentanol61,62 not correlated

CHxCO

1

not correlated

CH3OH

0.9452

C2H5OH

1

not correlated

CH2OH

1

not correlated

CHxCO

1

not correlated

ACOH

VLE methanol + phenol63

Table 4. Self and cross-associating parameters of GCA-EOS correlated in this work. Site k

Group i

Site l

Group j

εki,lj R–1 /K

κki,lj /(cm3 mol–1)

(–)

AOCH3

(+)

OH

2012

3.0513

(+)

AOH

2400

2.009

VLE anisole + 1-propanol60 and 1pentanol61,62 VLE & SLE anisole + phenol64

(+)

o-AOH

2400

1.800

VLE anisole + o-cresol64

(+/–)

OH

2985

0.805

(–)

>CO

3104

1.6252

(+/–)

OH

2985

0.50

VLE phenol + methanol63 and 1propanol65 VLE phenol + acetone66,67 and 2octanone68 VLE methanol + o-cresol69

(–)

>CO

3104

1.00

VLE o-cresol + 2-octanone68

(–/+)

(–/+)

AOH

o-AOH


Fitted experimental data

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Table Table 5 shows the GCA-EOS deviations in the correlation of binary mixtures, together with the data temperature and pressure range, number of points of each binary system, and the source of experimental data. Analogously, Table 6 lists all the deviations with respect to the experimental data not included in the parameterization database. As can be seen, the model performs well on the correlation database, showing average errors of 2.8% and 2.5% in bubble pressure and vapor composition, respectively. For the prediction database, the average error is slightly higher. However, the larger deviations corresponds to binary VLE data at pressures lower than 10-2 bar, for instance part of the data of ethanol and acetone, which depict higher relative errors. If we only consider the equilibrium data above 10-2 bar, the average prediction errors are 3.3% and 4.1% in bubble pressures and vapor composition data, respectively. Finally, GCA-EOS predictions are in disagreement with the data of ethanol + anisole at atmospheric pressure;57 this dataset distorts the correlation procedure, therefore, we only predict its behavior, as explained in Section 2.1.

Table 5. GCA-EOS phase behavior correlation of binary systems. Compounds 1 anisol

2 phenol o-cresol methanol

1,2-dimethoxy benzene phenol

1propanol 1pentanol 1pentanol ethanol methanol

Vapor-liquid equilibria T /K P /bar AARD P 3650.13 2.1 393 3660.13 1.3 399 3381.01 6.9 402 358 0.10-0.64 1.4 410427 410423 290

ethanol 1propanol acetone acetone

338455 288 341439 288 323

2octanone

446459

1.01

1.3

1.01

0.71

0.02-0.04

2.0

1

4.2

0.01-0.04 0.26-0.93

3.1 2.7

0.01-0.19 0.0040.77 1.01

7.2 2.4 1.6


NP

Reference

y1 -

10

64

-

10

64

2. 3 1. 4 -

14

58

19

60

12

61

1. 8 -

28

62

5

59

1. 6 -

21

63

7 33

66

1. 4 9. 2

9 12

66

17

68

65

67


o-cresol

methanol 2octanone

Compounds 1 anisole

2 phenol

288 0.02-0.10 10 8 4461.01 1.6 5. 16 465 3 Solid-liquid equilibria AAD(AARD%) T/K P / bar x1 in 2 x2 in 1 2291.01 1.3×10-2 1.3×10-2 (4.0) 314 (1.6)

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69 68

NP

Referenc e

6/1 0

42

Table 6. GCA-EOS phase behavior prediction of binary systems. Vapor-liquid equilibria T /K P / bar

ARD% NP Reference P y2 64 363-412 0.13 4.0 10 57 351-427 1.01 9.6 11 60 368 0.10-0.93 1.8 1.4 21 60 353 0.09-1.01 1.1 1.2 22 62 373-418 0.26-0.80 1.3 48 66 288 0.07-0.15 7.6 7 60 333, 353 0.03-1.02 1,4 4.1 40 70 361-427 0.26-1.01 0.9 40 66,71 phenol 288, 294 0.01-0.13 6.5 14 71,72 293-390 0.008-1.0 9.4 59 73 376-451 0.27-0.95 3.5 39 73 371-448 0.27-0.95 4.1 35 a 67 329-454 0.02-1.01 12 1.4 41 a 74,75 373-433 0.03-0.53 2.5 14 45 72,76 o-cresol 291-394 0.004-1.0 a 15 55 76 291 0.003-0.16 a 16 12 68 464-478 1.01 3.2 3.8 13 69 m-cresol 288 0.02-0.98 1.1 8 a 76 291 0.008-0.13 18 9 76 411-428 0.13 0.92 8.8 17 69 p-cresol 288 0.02-0.98 a 1.1 8 76 291 0.005-0.13 a 18 9 Solid-liquid equilibria 1 2 T/K P / bar AAD(AARD%) NP Ref. x1 in 2 x2 in 1 40 anisol m-cresol 226-284 1.01 1.3×10-2 (1.7) 3.9×10-2 (8.4) 8/15 -2 -2 40 p-cresol 229-308 1.01 1.8×10 (2.2) 1.3×10 (2.9) 8/11 a VLE data with buble pressures bellow 0.03 bars, limit for which an average 15% deviation means a maximum absolute deviation of 0.0045 1 anisole

Compounds 2 p-cresol ethanol 1-propanol 1-butanol 1-pentanol acetone 2-butanone 2-hexanone methanol ethanol 1-pentanol 2-methyl-1-butanol acetone acetophenone ethanol acetone acetophenone methanol ethanol p-methylacetophenone methanol ethanol

Figures Figure 5 to Figure 7 ilustrate the results achieved for mono-oxygenated aromatic compounds. Specifically, Figure Figure 5 depicts the correlation and prediction of VLE and SLE of anisol + phenol derivative mixtures. Accurate representation of phenol and anisol is key to this work since their oxygenated aromatic groups (ACOH and ACOCHx) are present in all the lignin monomers considered (see Figure 1). On the other hand, Figure 6 illustrates GCA-EOS correlation and prediction of the phase behavior of aliphatic alkanols


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with anisole or phenol. In general, mixtures of alkanols + phenol derivatives depict a slight negative deviation from ideal behaivor, while anisole + alkanol mixtures show higher negative deviations, similar to that of aromatic hydrocarbons + alkanols.34 In addition, Figure 6a depicts the phase behavior of the mixture anisole + ethanol, which is one of the datasets with higher deviation in Table 6. Nontheless, the GCA-EOS still predicts qualitatively the data of Piatti.57 Finally, Figure 6 shows GCA-EOS results of ketones + mono-oxygenated aromatic compounds binary mixtures. Ketones + anisole mixtures depict an ideal solution behavior (see Figure Figure 7a) and, as we mentioned before, the model fully predicts its behavior (no need of any binary interaction parameter). On the other hand, binary mixtures comprising ketone + phenol derivatives show a prominent negative deviation from ideal solution behavior (Figure Figure 7b), as a consequence of the strong hydrogen-bond formation. In summary, each oxygen-bearing aromatic group affects the phase behaivor of mixtures in a different manner. Therefore, in order to predict the phase equilibria of poly-oxygenated aromatic compounds, the model should be able to predict the net effect of the presence of multiple polar substituents, a topic that is discussed in following section.


Industrial & Engineering Chemistry Research 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

Figure 5. Isobaric phase equilibria of anisole + phenol derivaties. (a) Vapor-liquid equilibrium data64 at 0.13 bar anisol + () phenol, () o-cresol, and () p-cresol. (b) Solid-liquid equilibrium data40,42 of anisol + () phenol, () m-cresol, and () p-cresol. Dashed and solid lines: GCA-EOS correlation and prediction, respectively.


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Figure 6. Isobaric vapor-liquid equilibria of aliphatic alcohols with: (a) Anisol. Experimental data57,58,77,78 with () methanol, () ethanol, and () 1pentanol at 1.01 bar. (b) Phenol. Experimental data65,72,73 with () ethanol, () 1-propanol, () isopentanol and () 1-pentanol at 0.94±0.02 bar. Dashed and solid lines: GCA-EOS correlation and prediction, respectively.



Figure 7. Isobaric vapor-liquid equilibria of ketones + polar aromatic compounds. (a) 2-Butanone + anisole data70 at () 0.26, () 0.53, () 0.79, and () 1.01 bar. (b) O-cresol + ketones at atmospheric pressure. Symbols: experimental data68 for () 2-octanone and () acetophenone. Dashed and solid lines: GCA-EOS correlation and prediction, respectively.


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3.2. Phase equilibria prediction of poly-oxygenated aromatic compounds One of the aims of this work is to develop a thermodynamic model able to predict phase equilibria of typical mixtures in the conversion of lignocellulosic biomass to produce FPBO. As stated before, one of the characteristics of these mixtures is the high number of complex multi-functional molecules that comprises the bio-oil. In general, for simplicity purposes, group contribution models are parameterized based on mono-functional polar compounds, but only in few cases they are contrasted against poly-functional polar compounds.24,27 In this section, it is shown that the GCA-EOS is able to predict the phase equilibria involving this type of compounds, in some cases with a good precision, while in others the results are qualitative.

Table 7 lists GCA-EOS deviations in the prediction of vapor pressure of selected polyoxygenated aromatic compounds, while Figure 8 illustrates the results graphically. In addition, Table 7 also reports the values of Tc and dc required for each of the polyfunctional compound assessed in this work. As can be seen, the model behaves well for guaiacol and 4-methoxyphenol above 10-2 bar.

Table 7. Pure poly-oxygenated aromatic critical temperatures and diameters for the freevolume contribution of GCA-EOS. Model accuracy to predict vapor pressure in the reduce temperature range (ΔTr ). Compound

Tc /Ka

dc /(cm mol-1/3)b

∆Tr

ARD(P)%

Reference

7.7

No. points 12

1,2-dimethoxybenzene (veratrole)

693.1

5.2947

0.54-0.69

1,2-benzenediol (catechol)

800

4.5015

0.49-0.94

4.9

35

79

1,3-benzenediol (resorcinol)

836

4.4661

0.51-0.97

7.2

26

79

698.7

5.1117

0.52-0.69

3.6

16

55,56,80,81

2-methoxyphenol (guaiacol) 4-methoxyphenol (mequinol)

764.3

4.8835

0.52-0.68

6.1

19

56,81–83

2,6-dimethoxyphenol (syringol) c

817.8

5.262d

-

-

-

-

Critical temperatures predicted by the GCA-EOS. Critical diameter set to match a saturation point, except for syringol. c Up to our knowledge, no VLE data is available for pure syringol d Estimated with Espinosa et al.45 correlation based on the van der Waals reduced volume a

b


51,54–56


Figure 8. Vapor pressure prediction of poly-oxygenated aromatic compounds. Symbols: experimental data for (a) () 1,2-dimethoxybenzene,79 () 1,2-benzenediol, () 1,3-benzenediol;79 (b) () 2-methoxyphenol,55,56,80,81 and () 4-methoxyphenol.56,81–83 Lines: GCA-EOS predictions.


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Regarding binary mixtures, Table 8 lists the deviations of the GCA-EOS with respect to experimental data involving multipolar aromatic compounds. As can be seen, the performance of the model is good with results comparable to those presented in Table 6, with average errors of 6.5% and 2.9% in bubble pressures and vapor composition, respectively. It is worth mentioning that most of the errors are below 3% in bubble pressure except those which are low pressures, together with the VLE of cresol and 2,5hexanedione. Figure Figure 9 show the phase behavior of simple compounds with different multipolar oxygenated aromatics. Furthermore, those figures depict also the largest deviations listed in Table 8. Nonetheless, it can be seen that the model captures well the phase equilibria depending on the substituent, being able to differentiate between ortho- and meta-benzenediol in mixtures with ethanol. On the other hand, Figure Figure 10 shows the prediction of VLE for mixtures of 4-methoxyphenol + 1-octanol or catechol, showing that the GCA-EOS is able to assess the low relative volatility of these mixtures.



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Table 8. Prediction errors for aromatic compounds Compounds 1 2 veratrole guaiacol 1-octanol acetone m-cresol methanol 2,5-hexanedione p-cresol 2,5-hexanedione guaiacol anisol ethanol 1-octanol acetone 4-methoxyphenol p-cresol catechol catechol ethanol acetone resorcinol ethanol acetone Compounds 1 guaiacol 4-methoxyphenol syringol veratrole p-cresol

2 water

guaiacol p-guaiacol catechol

4-methoxyphenol 1,4-butanediol

Vapor-liquid equilibria T /K P / bar

ARD P 2.9 2.3 2.2 1.1 7.6 7.8 2.8 15 2.8 7.3 2.8 2.9 12 4.2 23 22

y2 6.0 3.4 6.1 6.3 1.0 2.1 2.3 1.8 -

433-463 0.24-0.66 433-463 0.24-0.87 290 0.04-0.17 288 0.02-0.98 465-482 1.01 465-482 1.01 429-469 1.01 290-409 0.01-0.98 433-463 0.26-0.88 290 0.02-0.22 423-453 0.04-0.55 438, 453 0.08-0.15 290 0.02-0.05 290 0.04-0.22 290 0.02-0.08 290, 293 0.03-0.25 Liquid-liquid equilibria T/K P /bar AAD(AARD%) x1 in 2 x2 in 1 320-360 1.01 4.4×10-3 (90) 0.09 (13) 300-360 1.01 1.5×10-2 (73) 9.7×10-2 (15) 313-323 1.01 2.5×10-3 (57) 0.15 (21) Solid-liquid equilibria 265-301 1.01 4.1×10-2 (6.6) 3.2×10-2 (5.6) 280-328 1.01 4.8×10-2 (7.0) 1.9×10-2 (4.4) 298-378 1.01 1.9×10-2 (1.8) 2.0×10-2 (15) 309-373 1.01 1.7×10-2 (2.3) 3.4×10-2 (7.8) 270-318 1.01 9.3×10-2 (21) 0.23 (34)


NP

Refere nce

31 36 7 8 13 13 11 69 36 7 33 22 6 6 8 13

55 84 59 69 85 85 86 59,72 55 59 83 83 59 59 59 59

NP

Ref.

10 4 3

72,87

14/9 5/9 3/20 6/7 10/5

41

87 72

88 88 43 89

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Figure 9. Vapor-liquid equilibria of (a) ethanol and (b) acetone with different ligning derivatives. Symbols: experimental data59,76 at 290 K for, () 1,2-dimethoxybenzene, () guaiacol () 1,2-benzenediol, and () 1,3-benzenediol. Dashed and solid lines: GCA-EOS correlation and prediction, respectively.



Figure 10. Vapor-liquid equilibria of some polyfunctional aromatic compounds (a) 4-Methoxyphenol + 1,2-benzenediol. Symbols: experimental data83 at () 438 K and () 453 K. (b) 1-Octanol + guaiacol. Symbols: experimental data83 at () 433, () 448, and () 463 K. Lines: GCA-EOS prediction.


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The available experimental SLE data is predicted with an average error of 5% excluding the butanediol + 4-methoxybenzene binary, for which the deviation is atypically large (see Figure 11). Regarding the latter, Figure 11b also shows, as a matter of comparison, the solubility of 1,4-butanediol considering the liquid as an ideal solution. As can be seen, this binary would depict experimentally a negative deviation from ideal solution, while VLE mixtures involving alkanols and aromatic methoxy compounds depict positive deviations (Figures Figure 6a and Figure 9a) or at least almost ideal solution (Figure 10a).

On the other hand, likewise for mono-functional compounds, GCA-EOS predictions of LLE of poly-functional compounds with water is qualitatively correct. Figure 12a illustrates the prediction of water + guaiacol and 4-methoxyphenol binaries, and GCA-EOS is able to correctly predict the difference between the mutual solubility of these two isomers. Moreover, Figure 12b depicts the prediction of water + syringol binary, taking into account that this is a complete predictive calculation, based on a simplified parameterization of the model, we believed is a good result. In order to achieve quantitative predictions more LLE data is needed to fit the corresponding nonrandomness parameters (αij and αij), which are set to a null value in this work. It is worth noting the importance to avoid fitting the high number of binary parameters of such a complex system with the limited amount of experimental data available. In this regard, we can trust that the model prediction of multicomponent systems, in the framework of process design, will be qualitatively correct. In any case, future work requires a fine tuning of the model to either new equilibrium data or actual process data acquired from bench scale experiments.



Figure 11. Solid-liquid equilibria of different polar aromatic compound binaries. (a) P-cresol binaries with40,88 () 1,2-benzenediol, () 4methoxyphenol, and () anisol. (b) 4-methoxyphenol systems with () catechol and (+) 1,4-butanediol. Solid lines: GCA-EOS prediction. Dotted lines: ideal solution behavior for 4-methoxyphenol + 1,4-butanediol system.


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Figure 12. Liquid-liquid equilibria of methoxy phenols with water. (a) Symbols: experimental data of guaiacol (squares) and 4-methoxyphenol (triangles). (b) Experimental data for syringol (circles). Lines: GCA-EOS prediction. Source of experimental data: (,) Jasperson et al.;87 (,) Cesari et al.72



4. Conclusions In this work, the GCA-EOS has been extended to monoaromatic oxygenated compounds in mixtures of interest for lignocellulosic biomass conversion processes. The families of compounds evaluated comprise anisole and phenol derivatives and their mixtures with linear and cyclic alkanes, alkenes, aromatic hydrocarbons, ketones, alcohols and water. A database with ca. 1050 binary equilibrium data has been modeled. The model parameters have been correlated with only 15% of the data available, involving mainly monooxygenated aromatic compounds, except for few dataset of veratrole with water and ethanol. The model average deviations are 3% in the pure compound vapor pressure data and, in the case of binary systems, 4.0% and 3.0% in bubble pressure and vapor phase composition, respectively. Moreover, the GCA-EOS also predicts the solid-liquid equilibrium experimental data, in most of the cases, for which the average error is about 4.7% in the solid solubility in the liquid phase.

Finally, the GCA-EOS succeeds to fully predict experimental data comprising pure and binary mixture of poly-oxygenated monoaromatic compounds, namely, guaiacol isomers, catechol, resorcinol, syringol, and also veratrole, as representative of lignin monomers. In this case, the average errors are about 4.0% for pure vapor pressure data, and 6.5% and 2.9% in bubble pressure and vapor phase composition, respectively. The deviations are slightly higher than those for mono-oxygenated aromatic compounds. However, it is worth noting that the main source of errors is the very low bubble pressure point of non-volatile compounds, for which still good predictions are obtained. Furthermore, the model predictions of solid-liquid equilibria of these systems are in good agreement with experimental data, except that for the binary system 1,4-butanediol + 4-methoxyphenol. Remarkable, the model is also able to predict the changes in the mutual solubility of water with 2-methoxyphenol and 4-methoxyphenol isomers, as well as that of syringol.


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The parameterization carried out in this work allows predicting vapor pressure of lignin derived dimers, compounds for which there is scarce or even no experimental information. Having the vapor pressures of these compounds, is the first step towards the simulation of the vapor-liquid equilibrium of condensation units in future works.

List of symbols A

Helmholtz free energy

AAD(Z)

Average absolute deviation in variable Z: 𝑁∑𝑖 |𝑍exp 𝑖 ― 𝑍calc 𝑖|

1

𝑁

AARD(Z)% Average absolute relative deviation in variable Z:

100 𝑁 ∑ | | 𝑁 𝑖 𝑒𝑖

dci

Effective hard sphere diameter of component i evaluated at Tc

ei

Relative error with respect to experimental data:

FPBO fi gjj kij LLE N NC NG NGA NEq Mi P qj R SLE T Tci VLE v xi yi Z

Fast pyrolysis bio-oil Fugacity of compound i Group energy per surface segment of group j Binary interaction parameter Liquid-liquid equilibria Number of experimental points of each data set Number of components in the mixture Number of attractive groups in the mixture Number of associating groups in the mixture Number of binary equilibrium data points Total number of associating sites in group i Pressure Number of surface segments of group j Universal gas constant Solid-liquid equilibria Temperature Critical temperature of component i Vapor-liquid equilibria Molar volume Molar composition in liquid phase of component i Molar composition in vapor phase of component i Dummy variable

𝑍exp 𝑖 ― 𝑍calc 𝑖 𝑍exp 𝑖

Greek symbols αij εki,lj κki,lj

Non-randomness parameter between groups i and j Energy of association between site k of group i and site l of group j Volume of association between site k of group i and site l of group j



Acknowledgments The authors acknowledge the financial support granted by Federal Ministry of Education and Research (BMBF, grant number 01DN15017), the Consejo Nacional de Investigaciones Científicas y Técnicas (PIP 112 2015 010 856), the Ministerio de Ciencia, Tecnología e Innovación Productiva (PICT 2016-0907), and Universidad Nacional del Sur (PGI 24/M133).

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For Table of Contents only

GCA-EOS predictions of VLE of lignin monomers

380

OH

0,2

OH

340

O O

P /bar

0,15 OH

300

T /K

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

260

O

220

OH

OH

0,05

O

O

OH

0,1

OH

OH

0 0

0,2

0,4 0,6 x (p-cresol)

0,8

1

0

0,2

0,4 0,6 0,8 x, y (acetone)

1


Page 38 of 38

Multiphase equilibria modeling of fast pyrolysis bio-oils. GCA-EOS

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