Supercritical Carbon Dioxide Separation of Carboxylic Acids and

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Supercritical carbon dioxide separation of carboxylic acids and phenolics from bio-oil of lignocellulosic origin: understanding biooil compositions, compounds solubilities and their fractionation Wahab Maqbool, Philip Hobson, Kameron Dunn, and William O. S. Doherty Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b04111 • Publication Date (Web): 03 Mar 2017 Downloaded from http://pubs.acs.org on March 5, 2017

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Industrial & Engineering Chemistry Research

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Supercritical carbon dioxide separation of carboxylic acids and phenolics from bio-oil of lignocellulosic origin: understanding bio-oil compositions, compounds solubilities and their fractionation Wahab Maqbool, Philip Hobson*, Kameron Dunn, William Doherty, Queensland University of Technology (QUT), 2 George St, Gardens Point 4000 Brisbane, Australia

Abstract Bio-oil produced from the thermochemical treatment of lignocellulosic biomass is increasingly recognised as a potentially abundant source of renewable chemicals and fuels. Single ring phenolics and low molecular weight carboxylic acids are significant constituent compound groups found in bio-oil and are important end product or intermediate commodity chemicals. Fractionation of bio-oil using supercritical fluids (usually with CO2 as a solvent) is a relatively new process being investigated worldwide at both laboratory and pilot scales. Solubility data associated with supercritical carbon dioxide (scCO2) and the many chemical compounds in the complex bio-oil mixture are required to predict the extraction behaviour of different bio-oil compounds. This article starts with a review of the composition of bio-oil in terms of the phenolic and low molecular weight carboxylic acid fractions which are potentially of commercial interest. Binary solubility data of major compounds in these bio-oil fractions with supercritical CO2 are summarized and discussed. Results from previously reported studies in which scCO2 is used as a solvent to recover bio-oil fractions are reviewed and collated. Density and temperature based Chrastil type models are developed using available data for the solubility in scCO2 of some of the major bio-oil compounds. Finally, extraction of compounds from the complex bio-oil mixture is discussed in terms of the trends predicted by the respective individual binary solubility models. Keywords: Bio-oil; Carbon dioxide; Modelling; Solubility data; Supercritical extraction

1. Introduction Thermochemical conversion processes have the potential to provide a highly effective means of biomass valorisation through the production of a range of high value fuels and chemicals. Among these technologies, fast pyrolysis and hydrothermal liquefaction have in recent years attracted significant interest due to the relative simplicity of the associated processes, high value products and the potential to target a range of compounds of special interest through judicious control of process conditions. 1-3 Both technologies produce an intermediate bio-oil product which is a complex mixture of compounds forming a micro-emulsion in which holocellulose (cellulose + hemicellulose) decomposition products are stabilizing the lignin macro-molecules through hydrogen bonding. 4 Bio-oil typically has a high water and

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2 pyrolytic lignin content together with a number of other chemical classes including acids, sugars, esters, aldehydes, ketones, phenol and phenol derivatives. 5-7 Bio-oil in its original state has high acidity (pH 2.02.5), high viscosity, is thermally unstable and largely immiscible with conventional liquid fossil fuels. Fractionation into thermally stable and concentrated compounds is critical if the full potential of bio-oil as a source of fuels and chemicals is to be realised. 8, 9 Bio-oil can be fractionated by standard process separation techniques. Liquid-liquid extraction 10 may require large solvent volume 11 and separation of the solvent itself from the fractionated products as an additional step. Conventional distillation methods like steam distillation 12 and fractional distillation 13 can also be used but they are generally energy intensive processes and can cause thermal degradation of the products. 11 In a review by Kim et al., 14 supercritical fluid extraction (SFE) using CO2 as a solvent and a limited number of other techniques such as switchable hydrophilicity solvents (SHS) and molecular distillation 15 were endorsed as appropriate means of fractionating phenolic rich bio-oils. SHS are solvents which show change in properties (such as polarity) in response to the addition of a trigger component (usually CO2) in the system. 16 With the use of SHS, extract yields may increase because of the enhanced dissolving power of the solvent but the recovered solvent is more contaminated with products 17 when compared with the use of scCO2 alone as a solvent. Molecular distillation can require the use of excessive temperatures (up to 130 ⁰C) 18 and has limited scope for tuning selectivity based on vapour pressure differences of compounds. Supercritical carbon dioxide fractionation (SCF) by contrast permits a high degree of selectivity through control of both density and temperature where the temperatures employed do not cause product degradation. SCF has been the focus of a number of major collaborative research programs to explore its potential in fractionation and stabilization of bio-oil. 19-21 Historically, supercritical extraction and fractionation techniques have been used in the food and nutraceutical industries for the recovery of plant, animal and food extracts. 22, 23 Supercritical solvents are favoured in these and other applications due to their relatively high densities and diffusivities. 24 CO2 is a commonly used supercritical solvent because of its non-toxicity, non-flammability, low cost and abundant availability. 24, 25 In addition CO2 has advantages over other commonly used solvents like ethanol, methanol, acetone, ethane and propane due to its near ambient critical conditions. 26 Although solvents such as ethane and propane have lower critical pressures than carbon dioxide they are highly flammable. 27 Some of the advantages of using SCF for bio-oil separation are the high level of control of solvent density (and therefore solubility) that can be achieved through relatively small variations in temperature and pressures, 28 its suitability for thermally labile natural substances 29 and selective extraction of low polarity compounds (aldehydes, ketones, phenols etc.). 30 scCO2 extraction is not without its disadvantages. For example: a) it is a weakly polar solvent and therefore limited to the selective extraction of non-polar to weakly polar compounds; b) the use of high pressures and densities in this process to enhance total extract yields may result in poor separation of feed mixture components and; c) although the extract yield and selectivity associated with scCO2 can be modified with the use of a polar co-solvent, some of these solvents may be problematic particularly for pharmaceutical and food applications.


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3 The wide spectrum of chemical compound classes present in bio-oil provides significant challenges for extraction using scCO2. For this reason, the number of experimental studies reporting on SFE of actual bio-oil (rather bio-oil synthesised from model compounds) are relatively few. One of the main challenges in the extraction of compounds from such complex systems is the non-availability of appropriate vapour-liquid equilibrium (VLE) data for the design of multistep fractionation processes. In addition to reviewing available data, this work will lay down some simple procedures to estimate the extraction behaviour of bio-oil compounds using simple binary VLE data. Bio-oil is a complex mixture of compounds. The presence of water in bio-oil requires special attention and has been challenging in the past for extensive experimental studies aimed at designing effective fractionation processes for aqueous mixtures. For future more detailed design purposes, complete phase equilibrium data including distribution coefficients of components between the scCO2 and aqueous phases need to be determined. This study explores the use of a simple methodology in which binary solubility data alone is used to understand fractionation of the solutes-rich scCO2 phase typical of that produced by a relatively high temperature and high pressure scCO2 counter flow water stripping column. Components extracted by the scCO2 water stripping stage will have minimal water content and therefore the assumption of negligible solute-solute interactions (in relation to water) may be invoked. In this paper the most prevalent low molecular weight carboxylic acids and single ring phenol (monophenol) components typically found in bio-oil will be identified and the availability and accuracy of the corresponding binary VLE data summarized. Binary solubility data of these major compounds will be discussed and modelled to compare their solubility trends. Experimental bio-oil extraction studies from the literature will be critically reviewed and the reported extraction behaviour will be discussed in the light of binary VLE data.

2. Composition of bio-oil from thermochemical conversion of biomass Composition data for thermochemical conversion of biomass is abundantly available in the literature for a wide range of operational conditions, process and rector designs. Experimental studies have used a range of temperature and pressure conditions for collection and condensation of and subsequent analysis of hydrothermal liquefaction and pyrolysis products. The role played by water both as a reactant and product as well as the way in which bio-oil water content is reported provides further complications in interpreting reported data. Water is produced in large quantities during pyrolysis and is considered a part of pyrolysis oil; in hydrothermal liquefaction water (or a hydrocarbon or a combination of both) is added to the biomass as a reactant. 31-33 In a study by Doassans-Carrere et al. 5 fast pyrolysis and direct liquefaction of identical biomass feedstocks (beech sawdust) is compared in terms of bio-oil compositions. Here, differences in chemical compositions of pyrolysis and liquefaction oils may be explained by different fraction collection and fraction designation procedures. In this study, 5 the removal of water from the liquefaction oil also caused removal of acetic acid, phenol and two other unidentified compounds. Differences in chemical compositions of the pyrolysis and liquefaction oil samples may also be explained through the prevalence of hydrolysis reactions in the liquefaction process in which opening of the levoglucosan ring structure



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4 occurs resulting in the production of sugars. By contrast levoglucosan was reported as a significant component of pyrolysis oil. Acetic acid, acetone, furans, phenols, oxalic acid and levoglucosane were largely present in pyrolysis oil while liquefaction oil contained ketones, phenols (guaiacol, syringol), furans, levulinic acid and etheric compounds. Castellvi Barnes et al. 34 also compared the pyrolysis and liquefaction of pine wood feedstock. Liquefaction studies were carried out with 10 wt % pine wood using a reaction time and temperature of 30 min and 300 ⁰C respectively in three solvents: guaiacol (GL), a guaiacol-water (GWL) mixture and water (WL) separately. Pyrolysis oil was obtained by treating the pine wood at 500 oC for a reaction time of 20-25 min for solid particles and below 2 seconds for the oil. Gel permeation chromatography (GPC) was used to isolate solvents and different fractions based on apparent molecular weight. The apparent molecular weight distribution through GPC showed a significantly greater proportion of heavy molecules in liquefaction compared to pyrolysis oils. In terms of deoxygenation, between 35-45% oxygen is lost in liquefaction while 20% is removed in pyrolysis compared to the original oxygen contents in the wood. In both types of bio-oil, carbohydrates and lignin are believed to be contributing to the production of aromatic and aliphatic compounds the relative proportions of which are dependent on the type of process and in the case of liquefaction oils, the nature of the solvent. Generally, in liquefaction, the yield of aromatics (typically 40% to 60%) was greater than the lignin content of untreated wood (25%) which suggests that carbohydrates are converted to aromatics in bio-oil along with lignin. The aromatic contents of pyrolysis oil were consistent with those present in the original untreated wood. Furans, phenols, acetic acid and other aromatic and aliphatic compounds were usually present in both types of bio-oils. For the three liquefaction solvents and pyrolysis trials the extent of deoxygenation appeared to occur in the order of: WL > GWL > GL > pyrolysis. A qualitative parameter of reaction severity (extent of decrease in residual carbohydrates and oxygen content both in oil and solid residues) was defined to compare the liquefaction bio-oils and it was proposed in the order of: WL > GWL > GL. This indicates that reaction severity in effect increases with increasing water concentration as it will cause a decrease in both residual carbohydrates and oxygen content. Ponomarev et al. 35 reviewed thermochemical methods for biomass conversion including hydrothermal liquefaction, liquefaction in organic solvents and pyrolysis. Use of different liquefaction solvents such as low molecular weight acids, phenols, alcohols or different combinations of these compounds with or without water were reported as the cause of large differences in bio-oil composition. Bio-oil composition from fast pyrolysis is strongly dependent upon operating temperature and residence time and as with liquefaction the resulting bio-oils contained many chemical classes such as acids, phenols, alcohols and other lignin and carbohydrate degradation products. In summary, separation techniques using polarity or any other property related to intermolecular interactions can be used for fractionation of both pyrolysis and liquefaction oils owing to significant similarities in their compositions.


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5

2.1.

Monophenols and low molecular weight acid contents of bio-oil

Phenolics form the largest group of chemical compounds within bio-oil (up to 50 wt%) 36 and are present in the form of monomeric units (monophenols) and oligomers (pyrolytic lignin, weight up to ~ 5000 amu). 37 Monophenols and low molecular weight carboxylic acids are always present in lignocellulosic derived bio-oils. Table S1 (supporting information) summarizes the phenolic and acid contents of bio-oils from pyrolysis and liquefaction of different biomass feedstocks. Major chemical compounds of both biooil fractions are listed in supporting information Table S2 and Table S3 on wt% dry biomass basis. Monophenols are of special interest to the chemical industry as intermediates for a wide range of products such as paints, resins and adhesives. Table S1 (supporting information) shows a collation of bio-oil composition data reported as wt% of dry biomass (where the appropriate mass balance has been reported) and area% of the spectra produced by gas chromatography–mass spectrometry (GC-MS) analysis of the bio-oil to determine monophenols and carboxylic acid contents. Where data is reported on an area% basis amounts are seen to vary over a wide range; when reported on dry biomass basis monophenols and acids yields are in the range of 6-10 wt% each. The yield values calculated in our work using composition data reported in the literature match those quoted more generally (i.e. without reference to specific biomass sources) in the literature where yields of both acids and monophenols are in the range of 5-10 wt% each on dry biomass basis. 38, 39 It is evident from Table S2 (supporting information) that acetic acid is the most abundant of all the low molecular weight acids. Acetic acid derives from the cellulose component of biomass via the production and subsequent decomposition of 2-Furancarboxaldehyde and 5-methyl-2-Furancaboxaldehyde. 32 Acetic and formic acids may also originate from the rupture of lignin aliphatic chains. 40 Phenolics are formed from the lignin in biomass and it is believed that degradation of lignin produces mainly 2-methoxyphenol (guaiacol) 32 and syringol 41 depending upon the nature of the wood (softwood or hardwood) feedstock. Further decomposition of guaiacols at higher temperatures (> 350 oC) produces mainly phenol, catechols, cresols and vanillin. 32, 42 Besides low molecular weight carboxylic acids, significantly greater amounts of longer chain acids (fatty acids) and cyclic acids (containing benzene rings) may also be present in bio-oils. A study by Gao et al. 43 indicated the presence of much greater amounts of n-hexadecanoic acid (7.21 area %) compared to acetic acid (1.61 area %) in liquefaction oil of rice straw. Zhu et al. 44 reported 10.38 % and 30.82 % bio-oil chromatograph areas associated with 3hydroxybenzoic acid (Salicylic acid) and total acids respectively. Acetic acid and the major monophenols identified above are of significant importance to industry. Global acetic acid production capacity exceeds 12 x 106 t/ annum and is used mainly in manufacture of vinyl acetate and acetic anhydride. 45 Acetate esters are produced by reactions of acetic acid with olefins or alcohols. 46 Methanol carbonylation is currently the preferred route for the industrial scale production of acetic acid. 47 All above mentioned monophenols are important building blocks and intermediates for chemical, food, pesticide and pharmaceutical industries. 48, 49 Syringol and guaiacol are naturally occurring phenols used mainly by the food industry for smoke flavouring. 50 Syringols are produced from hardwood that



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6 contains a higher amount of methoxy-substituted lignin while softwoods contain a lignin unit called guaiacylpropane which yield guaiacols upon thermochemical conversion to bio-oil. 50 Guaiacols, at higher temperatures, are eventually converted to catechols and alkyl phenols. 51 Vanillin is a phenolic aldehyde and used primarily in the food, beverage and pharmaceutical industries.

3. Binary system solubility data of bio-oil compounds Earlier sections of this article have identified acetic acid, syringol, phenol, cresol, guaiacol and catechol as major chemical compounds present in bio-oil derived from lignocellulosic biomass. Binary system solubility studies of these bio-oil compounds with scCO2 will be summarized in this section. Binary solubility data of different bio-oil compounds with scCO2 is not only an indication of comparative solubilities and trends but also an indispensable source of data for more rigorous and accurate predictive thermodynamic modelling of multicomponent mixtures. Experimental VLE data of binary systems can be used to derive solute-solvent interaction parameters for use in an equation of state (EOS) along with an appropriate mixing rule to account for degrees of non-idealness in a multicomponent mixture. In a study by Gironi et al. 52 to investigate supercritical carbon dioxide extraction of fish oil ethyl esters, experimental data was modelled to Peng-Robinson (PR) EOS with van der Waals mixing rules and the interaction parameters were determined from binary data. Similarly, in a lemon essential oil deterpenation study 53, PR-EOS was used for modelling the extraction process together with van der Waals mixing rules. In this study 53, binary data was used to calculate three solutesolvent and one solute-solute pair of interaction parameters. In a fish oil extract study 52 only solutesolvent binary interaction parameters were used. Both fish oil and lemon essential oil are multicomponent mixtures and were modelled with a reasonable approximation. Comparison of model predictions and experimental data gave percentage average absolute relative deviation values of 1.63 for fish oil 52 and 11.67 for lemon oil 53 system. Solubility is typically defined as mole fraction or weight fraction of a solute in supercritical fluid. Different configurations of experimental techniques are in use for phase equilibria study and solubility data determination. These techniques together with critical reviews on their applications and limitations are extensively reported in the literature 27, 54, 55. Generally, these experimental techniques are categorized as either static or dynamic (flow) techniques. In the static technique, the solute is immersed in the supercritical fluid for an extended period of time in order to reach equilibrium. This technique has variations which can be described as analytical, synthetic or gravimetric depending upon the method adopted for solubility measurement. In the analytical method a constant volume cell is used from which small samples can be withdrawn for compositional analysis after equilibration is reached. In the synthetic method, a variable volume view cell is used to adjust cell volume and pressure. Known amounts of solute and solvent are brought together in the equilibrium cell and conditions such as pressure and temperature are varied until the cloud point (beginning of precipitation) is observed. There is no need for sampling; solubility is calculated 54 from Eq. (1), 𝑦2 =

𝑛2 𝑛1 +𝑛2

(1)


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7 Where y2 is mole fraction solubility and n1 and n2 are moles of carbon dioxide and solute loaded in the equilibrium cell. Gravimetric methods are least adopted due to low experimental accuracy. In this method 54, solute is placed in a vial and the vial is then placed in a pressure vessel. Perforations between vial and pressure vessel allows the solute to saturate the supercritical fluid present in both the vessel and vial. At the end of the experiment the system is depressurized and the remaining solute in the vial is gravimetrically measured to calculate solubility through solute mass difference and system volume. In dynamic or flow methods 54, supercritical fluid is continuously flowing through the solute packed equilibrium cell and is being analysed at the cell exit by chromatographic, spectroscopic, gravimetric or other techniques. Solubility is calculated as (Eq. (2)): 𝑦2 = 𝑛

𝑛2 1 +𝑛2

=

𝑛2 𝑄1 𝜌1 𝑡+𝑛2

(2)

Where n1 and n2 are moles of carbon dioxide and solute respectively collected over time t; Q1 and ρ1 are the volumetric flow rate and molar density of CO2 respectively at the same conditions. Common limitations of dynamic methods are the possibility of mass transfer restrictions between solute and solvent due to short residence time or larger solvent flow rates, solute clogging of the restrictor and large compositional variations when a multicomponent system is being investigated. Using the static method, a number of solubility data points may be collected at different conditions with a single small loading of solute. However, in the static-synthetic method, care must be taken in order to get accurate and consistent data by visual observations. Table 1 summarizes experimental methods and conditions used in different studies reported in the literature for binary system solubility measurement of different monophenols and acids. Corresponding mole fraction solubility data of these studies can be found in supporting information (Table S4). Temperature, pressure and mole fraction solubility uncertainties associated with the studies reported in Table 1 were ranging from ±0.1 K to ±1 K, ±0.1 bar to ±2 bar and 2% to 5% respectively. Only Skerget et al. 56 study showed uncertainty in mole fraction solubility varying over a wide range of ±0.4 % to ±16.4 %. Table 1. Experimental techniques and conditions used in literature studies for measurement of solute solubilities in supercritical carbon dioxide

Solute Phenol, Catechol Phenol Catechol Guaiacol, P-cresol O-cresol, P-cresol Eugenol

Experimental technique Staticrecirculating Flow Static

Temperature (K)

Pressure (bar)

Equilibration time (min)

Sample volume (mL)

Ref.

333.15 - 363.15

100 - 350

30

0.10

57

309.15 - 333.15 308.15 - 338.15

79.3 – 249.4 122 - 405

45

0.122

58

Flow

323.15 – 423.15

20 - 200

-

-

60

Static

323.15 – 473.15

99 - 348

840

-

61

Flow

313.15 - 333.15

60 - 160

-

-

62


59


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8 Vanillin Vanillin Eugenol Acetic acid 2,5-DMP 3,4-DMP 2,5 and 2,3-DMP

Static Flow Flow

313.2 - 353.2 308.15 - 318.15 308.15 - 328.15

80 - 276.5 83 - 195 14.8 – 125.1

60 -

0.20 -

56

Flow

313.2 - 353.2

11 - 111

-

-

65

Flow Flow

308.15 308.15

74 - 267 82 - 262

-

-

66

Flow

308.0

101 - 280

-

0.120

68

63 64

67

4. Supercritical CO2 extraction and fractionation of bio-oil Experimental studies reporting on the use of supercritical CO2 for the extraction and fractionation of biooil components will be summarized in this section. Experimental schemes, process conditions and yields and recoveries of low molecular weight acids and monophenols will be critically analysed. Elements of a typical experimental process scheme for supercritical extraction 69-71 are shown in Figure 1. Such an experimental setup, besides extraction, can also be used for the dynamic measurement of solubility.

Figure 1. General experimental setup of supercritical CO2 extraction system with optional co-solvent addition. T, P: temperature, pressure measurement and control.

Referring to Figure 1, CO2 is supplied from a reservoir in pressurized liquid form. A high pressure pump and pre-heater deliver CO2 to the extraction vessel at the target temperature and pressure. Where required a co-solvent may be added prior to the pre-heater to fine tune selectivity for particular compounds and fractions. The extraction vessel is a stainless steel container which has either been loaded with the sample from which compounds are to be extracted (semi-continuous operation) or the liquid sample is fed continuously from the middle or top of the vessel (counter-current operation). A heating bath / oven or any other temperature element controls and maintains the extraction vessel


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9 temperature. Solvents and solutes leave the extraction vessel through a restrictor or a micrometering valve to maintain the required pressure in the extraction vessel. Extraction times of between 20 and 90 minutes are typical and vary depending on the nature of the system of solvents and solutes and the extent of extraction required. Solute (extract) precipitates out of the gaseous CO2/ co-solvent mixture after depressurisation across the restrictor; the solute product is then recovered from collection vessel. Solute-free gaseous CO2 is either vented or recycled back to the CO2 reservoir for reuse. Left-over mixture is collected as raffinate from the bottom of the extraction vessel. In a variation on the system shown in Figure 1 Mudraboyina et al. 72 included a rectification column to separate single ring phenolics from bio-oil derived from the microwave pyrolysis of softwood Kraft lignin. Extraction was performed at 35 oC and 80 bar pressure on a 4 g bio-oil sample using a CO2 flow rate of 10 mL/min. Extraction was reported for varying amounts of total CO2 used by varying the extraction time. The extract was found to be selectively enriched with all major single ring phenols except catechol. Catechol has a low partial vapour pressure and therefore low solubility relative to many other single ring phenols at any given temperature and pressure. Single ring phenols which were concentrated in the extract included creosol, guaiacol, cresol, phenol and its derivatives. Figure 2 shows the variation with solvent use in extract yield and concentration where the solvent use is presented here as the ratio (S/B) of the total mass of CO2 to initial mass of pyrolysis bio-oil. A maximum mass transfer rate at a CO2 usage of 49.4 g/g is indicated by the maximum concentration of single ring phenols in the extract at this point.

Figure 2. Extract yields and concentration of single ring phenols in extract from supercritical fluid rectification of softwood Kraft lignin microwave-pyrolysis oil for varying solvent to bio-oil ratio. (inherent and experimental random errors were not reported in the original source) 72



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10 Patel et al. 73 studied scCO2 extraction of cardanol (a type of phenolic lipid) and phenols from pyrolysis oils derived from cashew nut shells and sugarcane bagasse respectively. Pyrolysis oils were mixed with sawdust (1:1 by weight) to provide surface support in the extractor. Table 2 compares extraction results of both oils at different operating pressures and temperatures. Table 2. Effect of pressure and temperature on extract yield and product concentration in extract during supercritical CO2 extraction of sugarcane bagasse and cashew nut shell pyrolysis oils. (inherent and experimental random errors were not reported in the original source) 73

Results

Sugarcane bagasse pyrolysis oil (extraction of phenol)

Cashew nut shells pyrolysis oil (extraction of cardanol) – at 333 (K)

Pressure (bar) Yield %

120

300

200

250

300

9 (at 333 K)

15 (at 333 K)

43

54

63

Concentration %

36.85 (at 300 K)

71.22 (at 300 K)

50.89

64.90

85.50

Conditions associated with high concentration phenol extract (300 bar, 300 K) resulted in relatively higher concentrations of cresols (19.72 %) and 4-ethylphenol (26.79 %) in bagasse pyrolysis oil extract. Similarly, the conditions associated with high cardanol extract concentration (300 bar, 333 K) also caused 4.89 % 2-ethylphenol in cashew nut shells pyrolysis oil extract. Wang et al. 74 studied scCO2 extraction of pyrolysis oil obtained from pulverized corn stalk. Adsorbents in the form of silica gel crystals or a 5Å molecular sieve were used as surface support for bio-oil in order to investigate the effect of intermolecular forces between adsorbent and bio-oil. Equilibration time for extraction was up to 3 hours and 1 hour respectively for the trials with and without the use of adsorbents. Extractions were carried out at 45 ⁰C and 260 bar pressure. A 12 g bio-oil sample was spiked on 30 g adsorbents; 50 normal litre of CO2 was used for 12 g of bio-oil extraction. Figure 3 shows the effect of adsorbents on enrichment (ER) associated with the extraction of phenols and acids; in all trials the extract yield was 20 %. Uncertainties in both temperature and pressure measurements were ±1 K and ±1 bar respectively.


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11

Figure 3. Effect of adsorbent on typical selective enrichment of phenols and acids in scCO2 extraction of corn stalk pyrolysis oil (original pyrolysis oil contained 10.74 % phenols and 28.15% acids). (inherent errors related to extract yields and compositions and experimental random errors were not reported in the original source) 74

Inspection of Figure 3 indicates that the use of scCO2 results in a high level of selective enrichment of phenols relative to acids for all the adsorbent options used. This result is primarily because of the high polarity and resulting hydrogen bonding of acids with water and other polar molecules. Use of support materials (adsorbents) provided a mixed response in which silica gel was slightly more effective than the 5A molecular sieve in the selective enrichment of phenols. Rout et al. 75 and Naik et al. 76 studied scCO2 extraction of pyrolysis oils produced from wheat-wood sawdust and wheat-hemlock respectively. In both studies, bio-oil samples were mixed with 2 mm glass beads before being placed in the extractor. Rout et al. 75 studied fractionation at 45 ⁰C with a CO2 flow rate of 30 g/min. Three fractions were collected at a pressure of 250 bar with an extraction interval of 2 hours each and a fourth fraction was collected at 300 bar. Extract yields and compositions were determined with uncertainties of ± 0.4 % and ± 2.8 % respectively. Naik et al. 76 studied fractionation at 40 oC with CO2 flow rate of 40 g/min. Fractions were collected at 100, 250 and 300 bar with an extraction interval of 2 hours for each collection. Uncertainties of ± 0.8 % and ± 1 % were associated with extract yields and compositions respectively. In both studies the extraction of carboxylic acids and benzenoids (containing phenolics) were reported. There is sufficient similarity in the conditions associated with these two studies to draw some comparisons in terms of the impact of pressures and temperatures on the preferential extraction of benzenoids using scCO2. Figure 4 shows the ratio of total benzenoids extracted to total acids extracted as a function of scCO2 solvent use.



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Figure 4. Ratio of total benzenoids extracted to total acids extracted as a function of different solvent/bio-oil ratios used in scCO2 extraction of wheat-wood sawdust 75 and wheat-hemlock 76 pyrolysis oils

Inspection of Figure 4 indicates that for the wheat-hemlock pyrolysis oil the regime of progressively increasing extraction pressure resulted in an enrichment of benzenoids in the scCO2 solvent stream. 76 By contrast there was little improvement in enrichment of benzenoids in the scCO2 solvent stream beyond a S/B ratio of 100 for the near-constant pressure regime implemented in the wheat-wood pyrolysis oil extractions. 75 Other underlying factors which may be influencing the differences in enrichment reported in these two studies are initial concentration of the target solutes in the bio-oil samples (which were not reported in either study) and extraction temperature. Effect of initial bio-oil water content on extract yield and composition was shown in a supercritical extraction study on beech wood pyrolysis oil. 77 The effects of operating pressure and initial bio-oil water content were investigated in extraction experiments carried out on slow pyrolysis oil (SP), fast pyrolysis oil upper/aqueous phase (FPU), fast pyrolysis oil lower/hydrocarbon phase (FPL) with water contents of 1.13, 43.44 and 18.98 wt % respectively. The experimental setup was similar to previous studies reported in this section. In each case 80 g of the bio-oil sample was adsorbed on silica gel and extraction carried out using a constant flow of scCO2 to achieve a S/B ratio of 45. Experiments were performed at 333.15 K temperature and pressures of 150, 200 and 250 bar. The resulting extract yields and compositions for trials carried out at 150 bar are summarized in Table 3.


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Table 3. Yields and acid-phenol contents of extracts obtained at 333.15 K temperature and 150 bar pressure during scCO2 extraction of beech wood pyrolysis oil (inherent and experimental random errors were not reported in the original source) 77

Parameter Initial bio-oil water content (wt%) Extract yield (wt%) Extract weight (g) Acids (wt%) Phenols (wt%) Extract composition Acids (wt% of initial bio-oil acids) Phenols (wt% of initial bio-oil phenols)

SP 1.13 40.5 32.4 9.5 15.9 58.3 49.2

FPU 43.44 7.4 5.9 23.8 9.1 10.2 25.7

FPL 18.98 8.0 6.4 15.2 13 13.6 27.9

SP: slow pyrolysis oil, FPU: fast pyrolysis oil upper phase, FPL: fast pyrolysis oil lower phase

Although similarly detailed data for the extractions carried out at 200 and 250 bar were not reported, it was noted 77 that the resulting variation in extraction yield between oil samples was large at all extraction pressures. This result was attributed to the large variation in water contents of the three oil samples. In summary, the experimental studies reviewed in this section on the supercritical fractionation of biooil indicate a number of fundamental process trends: i) ii)

iii)

iv) v)

Higher S/B ratios favour higher fractional yields at the expense of a slight decline in wt% concentration of desired products in extract. 72 Increase in solvent density (a function of temperature and pressure) gives higher extract yield as is obvious from Patel et al. 73 although this enhanced solvation power may change the selectivity of products in the extract. 78 Supercritical extractions with pure CO2 solvent show a tendency towards solvating non-polar and slightly polar compounds like phenols, aldehydes and ketones while strong polar compounds like acids, sugars and alcohols will tend to remain in the raffinate. Compounds which initially remain in the raffinate may be extracted with scCO2 when dilution of the preferentially solvated compounds in the raffinate has occurred. 77 Small molecular weight compounds are more likely to be extracted than higher molecular weight compounds. Bio-oil water content has a significant effect on extraction and selectivity of strong polar compounds primarily due to the formation of strong intermolecular forces such as hydrogen bonding. In supercritical extraction of chemically synthesized bio-oil 74 polarity was shown to play a dominant role; propanoic acid showed a higher tendency to be extracted over acetic acid in spite of higher partial vapour pressure of acetic acid.



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5. Discussion 5.1.

Solubility data

The extent of solute solubility in supercritical fluids is mainly dependent on the vapour pressure of the solute and the solute-solvent intermolecular interactions. 79 It has been previously documented in the literature that solute vapour pressure (volatility) or melting point may be directly linked to its solubility. 80, 81 . As a general rule of thumb, the higher the melting point of a solute, the lower its solubility. However, this contribution of solute vapour pressure to solubility should not be over-simplified; Lou et al. 82 showed that an increase in measured solubility can be a result of increased vapours transport from mixture to vapour phase due to increasing vapour pressure rather than solute-solvent interactions. However, in supercritical fluids this phenomenon of increased solubility due to higher vapour pressures is widely accepted as a part of overall increase in solubility. Figure 5 compares solubilities of some bio-oil compounds (see supporting information, Table S4) from the monophenol group at identical temperature and pressure conditions.

Figure 5. Effect of increasing pressure on solubilities of different bio-oil compounds in supercritical carbon dioxide at 333 K temperature. Random or ultimate error were not reported for eugenol in the original source. 62 For vanillin the maximum reported uncertainty of + 16.4% is shown. 56

It is evident from Figure 5 that at 333 K temperature, catechol which has a melting point of 105 oC 83 will show much lower solubility in scCO2 than phenol with a melting point of 40.9 oC 83. In other words, at 333 K temperature, phenol will have much higher vapour pressure than catechol. In Figure 5, melting points of compounds are in the order of: catechol (105 oC) > vanillin (81.5 oC) > phenol (40.9 oC) > eugenol (-9.1 oC), so their solubility in supercritical CO2 at 333 K temperature will show the trend: catechol < vanillin < phenol < eugenol. It can be seen (Figure 5) that isothermal increase in pressure causes increase in solubility of solutes in scCO2. It is a well described fact in literature that isothermal


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15 increase in pressure increases solvent density which results in higher solvation powers. 84-86 Increase in solvent density will not only show enhanced solubility effect but also an opportunity to increase the solubility differences of compounds. Another important phenomenon frequently encountered in supercritical solubility studies is the occurrence of retrograde (crossover) region. 87-89 This is a pressure region where solubility isotherms meet and divide solubility data in to two sections. In the low pressure section (before the crossover region), solubility decreases with increasing temperature due to the corresponding reduction in density of the supercritical fluid solvent. In the higher pressure regions (after the crossover region), solvent density is only slightly affected by temperature so in this region solute solubility increases with increasing temperature as the effect of the corresponding increase in solute vapour pressure starts to dominate. Figure 6 illustrates this crossover region for phenol and vanillin solubility data (see supporting information, Table S4).

Figure 6. Solubility isotherms showing crossover pressure regions for vanillin-CO2 (left) and phenol-CO2 (right) binary systems. The maximum reported uncertainty for vanillin 56 of ±16.4% is shown.

Solubility isotherms of phenol intersect around 280 bar while for the vanillin-CO2 binary system the crossover region occurs near 160 bar. Crossover region is an important phenomenon in design of a separation process for concentration or dilution of a particular compound as it allows increased or decreased solubility of a compound by variation in temperature. 89 For supercritical solubility data generation, it is of utmost importance to correctly calculate fluid densities with a reliable EOS. A number of thermodynamic equations have been proposed for the calculation of solvent densities at different temperature and pressure conditions. Span and Wagner 90 proposed an EOS in the form of Helmholtz energy which can be used up to temperatures of 1100 K and at pressures up to 8000 bar. Equation by Span and Wagner 90 is considered very accurate and reliable and is also featured in REFPROP database by NIST 91 and the ThermoFluids 92 computer program. Tabulated CO2 properties calculated with Span and Wagner 90 equation of state are also available in literature as a published book. 93



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16 An example of the extent of variation between predictions using two different EOSs is shown in Figure 7 which gives CO2 density variation with pressure at 40 oC as calculated by the PR-EOS 94 and the Span and Wagner EOS 90. Inspection of Figure 7 indicates significant variation in predicted CO2 density values at around 110 bar.

Figure 7. CO2 densities calculated at 40 oC with PR-EOS 94 and Span and Wagner EOS 90

Figure 8 gives a complete illustration of solubility isotherms of different monophenols and acids (see supporting information, Table S4) against CO2 densities. Density rather than pressure of CO2 is mostly used to plot solubility data of different compounds as the solvation power (density) of the solvent is directly linked with pressure as well as temperature. Inspection of Figure 8 indicates that there is a general trend of increase in compound solubility with increase in solvent density. It also shows that solubilities of catechol, vanillin and dimethylphenol (DMP) isomers are far less than other compounds in the figure.


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Figure 8. Solubility data (see supporting information, Table S4) plots of different monophenols and acetic acid. CO2 density is calculated here using the Span and Wagner 90 method. The maximum reported uncertainty for vanillin 56 of 16.4% is shown. Random or ultimate error were not reported in the original source for eugenol. 62

However, there is potential opportunity for fractionation of these compounds by exploiting a) crossover phenomena and b) different partial vapour pressures of compounds at different temperatures. This exploitation is possible by changes in temperature and pressure whereby same density values at different temperatures can be achieved as is shown in Figure 9 or different temperature-density combinations may also be produced as per process design requirement.



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Figure 9. CO2 density variation as a function of temperature and pressure, Left: 3-D surface plot of temperaturepressure and CO2 density, Right: 2-D plane plot of CO2 density curves against pressure axis at different temperatures

Another important system parameter affecting extraction yields and product recoveries is pH. 95 In supercritical extraction with CO2, there would be a combined contribution to solution pH from both the crude bio-oil and the induced acidity of CO2 (by the formation of carbonic acid in solution). The acidic compounds will be preferentially extracted relative to basic compounds due to a shift of the ionization equilibrium towards the formation of their associated non-ionic form. Combs et al. 95 studied the effect of pH on supercritical extractions of phenol and 2,4,6-trichlorophenol (TCP) from aqueous matrices. Buffered and non-buffered assays were subjected to supercritical extractions with initial sample pHs of 3.0, 5.0 and 8.0. Extractions were studied at a single temperature and two different pressures of 150 atm and 300 atm. It was found that, for non-buffered solutions, extractions with supercritical CO2 lowered the final pH of the system to a minimum of 3.0 and maximum of 4.2 for an initial pH of 3.0 and 8.0 respectively. At the lower extraction pressure (150 atm) this lowering of final pH caused an increase in the percent recovery associated with both of the individual solute (phenol and TCP) components; at 300 atm pressure the reverse effect on percent recovery was observed. For the buffered solution, the minimum final pH was 3.0 corresponding to an initial pH of 3.0 prior to extraction; the maximum final pH was 5.8 corresponding to an initial pH of 8.0. In the case of the buffered solution an increase in the percent recovery associated with the individual solute components was observed for a decrease of final pH; this was found to occur at both 150 atm and 300 atm extraction pressures. Phenol and TCP are both acidic compounds with pKa values of 9.9 and 6.0 respectively. In solution, ionization occurs, which for the case of phenol is according to, 𝑃ℎ𝑒𝑛𝑜𝑙 ↔ 𝐻 + + 𝑝ℎ𝑒𝑛𝑜𝑙𝑎𝑡𝑒 −

(3)

Lowering of the solution pH by the introduction of CO2 and subsequent formation of carbonic acid will cause the equilibrium of this ionization process to shift to the left in Eq. (3) resulting in increased formation of the more readily extracted neutral form of the compound. As TCP is more acidic than phenol a greater change in its ionization equilibrium shift and therefore a greater improvement in TCP percent recovery relative to phenol percent recovery was observed 95 upon lowering of solution pH (Figure 10).


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19

Figure 10. Effect of CO2 induced acidity (in terms of final solution pH of 3, 3.4 & 4.2 corresponding to initial pH of 3, 5 and 8 respectively) on percent recoveries of phenol and 2,4,6-trichlorophenol solutes from aqueous matrices at 150 atm pressure during supercritical extraction with pure CO2. Inherent and experimental random errors were not reported in the original source. 95

In a multi-component system, solubility of a solute in scCO2 is largely affected by parameters such as temperature, solvent density (dependent upon temperature and pressure) and solute-solvent properties (pH, solute-solute intermolecular forces and solute-solvent intermolecular forces). In a simple binary system, intermolecular forces are homologous due to the presence of only single solute type molecules and this makes predictive modelling of such system relatively reliable and accurate. However, in multi-component systems, such predictive modelling is a challenging task due to presence of different unaccounted solute-solute and solute-solvent intermolecular forces. Although, some binary data (CO2 + bio-oil compound, see supporting information, Table S4) and ternary data (CO2 + two bio-oil compounds) are available in the literature and have been successfully modelled with simple empirical or rigorous thermodynamic models, complete predictive modelling of multicomponent phase behaviour of bio-oil in supercritical CO2 is not yet realized. The reasons behind the scarcity of complete predictive modelling of multi-component systems like bio-oils are:   

the complex nature of the system due to the presence of multi-components; varying compositions (whereby composition is changing over time in a continuous fractionation process) and; non-realization of complete intermolecular interactions due to limitations imposed by nonhomologous feedstock availability and tedious research work requirements

In the absence of multi-component data, the determination of solubility data for binary systems remains of fundamental importance. Such data provides guidance on the degree or extent of separation possible between two or more components at different temperatures and pressures. Binary solubility data also allows us to visualize solubility trends of pure components in supercritical CO2 e.g.; identification of retrograde and non-retrograde regions.



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5.2.

Modelling Binary solubility data

To quantitatively compare and use the binary system solubility data (see supporting information, Table S4) modelling was performed on it with a density and temperature based Chrastil model. 96 It is a semiempirical model and is mathematically described in Eq. (4) and Eq. (5): 𝑎

𝑆 = 𝜌𝑘 exp (𝑇 + 𝑏)

(4)

Eq. (4) can be represented in logarithmic form as: 𝑎

𝑙𝑛𝑆 = 𝑘𝑙𝑛𝜌 + (𝑇 + 𝑏)

(5)

Where S (g/L) is the solute solubility, ρ (g/L) is solvent density, T is temperature in Kelvin and k, a and b are empirically determined constants. Constant k is an association number representing average number of CO2 molecules in formed solvato complex. The constant a depends upon heats of solvation and vaporization of solute and constant b depends upon molecular weights of solute and CO2 and as well as on the value of k. Eq. (5) was correlated with the experimental data (see supporting information, Table S4) giving a set of co-efficient values for each compound summarized in Table 4. Where data for individual compounds were available from multiple sources, this data was combined to produce the parameters in Table 4. Goodness of fit (Table 4) is determined in terms of adjusted co-efficient of determination (R2), sum of squares due to error (SSE) and root mean square error (RMSE). Table 4. Chrastil correlation parameters for the solubility of several bio-oil compounds in supercritical CO2

Compound Phenol Catechol P-cresol O-cresol Guaiacol Eugenol Vanillin 2,5-DMP 1

k 3.999 3.644 3.201 3.075 3.916 4.187 3.916 3.373

Parameter a -3241 -3525 -3240 -2526 -3251 -635.5 -4863 -18630

b -12.81 -12.33 -7.627 -7.862 -11.35 -20.85 -8.747 40.87

R2 0.9489 0.9685 0.9254 0.7893 0.9137 0.9430 0.9715 0.9937

Goodness of fit SSE 4.418 2.033 4.577 2.571 2.717 1.937 2.20 0.008679

RMSE 0.2915 0.1958 0.4561 0.606 0.5495 0.3860 0.2164 0.02809

Obs.1 55 56 25 10 12 16 50 14

number of observations

The correlation fit was good for most of the compounds with the exception of o-cresol, p-cresol and guaiacol. These three compounds did not show good linear relationship between density and solubility on a natural log scale plot. This non-linearity was caused by some of the experimental data points recorded at relatively high temperatures. Upper temperature in solubility measurements of o-cresol and p-cresol was 473.15 K and 393.15 K in the guaiacol study. Parity plots (Figure 11) were generated to show the correlation between predicted solubility (by fitted Chrastil model 96) and experimental solubility of different bio-oil compounds.


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22 Figure 11. Parity plots of experimental vs predicted solubility of different bio-oil compounds on natural log scale. Dots of one colour correspond to one data source.

5.3.

Use of binary data in preliminary assessment and design of fractionation

The Chrastil model 96 correlation of solubility data gives a unique characteristic equation for each compound. These equations provide a means of ready comparison of solubilities for different compounds and also to visualize solubility trends. Figure 12 gives such a comparison of solubilities at 308 K temperature predicted by the fitted model. The model predictions suggest that at 308 K solubilities of different monophenols are in the order: guaiacols > phenols > catechols. This solubility trend of pure compounds was also observed in actual experimental extraction of bio-oil mixtures (Figure 13). According to Figure 12, solubility of guaiacol, phenol and catechol will increase with increasing solvent densities, however selectivity of guaiacol in the extract will drastically improve compared to the phenol and catechol selectivities. To choose optimum conditions for the recovery of guaiacol (which has a relatively high solubility), solvent temperature as well as density should also be considered in the separation design process. Guaiacol exhibits crossover behaviour at around 180 bar which is significantly lower than the crossover pressures of phenol (280 bar) and catechol (270 bar). This would indicate that extracting between 180 and 270 bar at higher temperatures will favour the selectivity of guaiacol over phenol and catechol. At 318 K temperature rather than 308 K, a favourable solvent density of 850 g/L (according to Figure 12) can be achieved with 240 bar pressure. This pressure is in the optimal range for the separation of guaiacol. Inspection of Figure 12 and 13 provides a qualitative insight into the relationship between the trends exhibited by actual solubility and apparent solubility. According to Figure 12, the actual solubility of different phenols is in the order of guaiacols > phenols > catechols. When we compare this with experimental extraction results (Figure 13), we observe a similar trend in apparent solubility.

Figure 12. Solubilities of different monophenols in scCO2 predicted by fitted model at 308 K temperature


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23

Figure 13. Extraction trends of different monophenols with scCO 2 from bio-oil mixtures of softwood Kraft lignin 72 and beech wood 77 pyrolysis oils

5.4.

Solubility data consistency and accuracy

Getting consistent and accurate high pressure experimental solubility data has been a challenging task for experimenters. Discrepancies do exist in data of different phase equilibria and solubility data measurement studies. For example when the Chrastil model 96 was correlated with the experimental solubility data of catechol reported by Garcia-Gonzalez et al. 57 it provided a good fit (R2 = 0.9777); use of the same correlation parameters provided a poor fit (R2 = 0.8495) to a different catechol solubility data set 59 (Figure 14).

Figure 14. Parity plots of experimental versus predicted solubilities using data and parameters based on 57 (plot A) and using the same correlation parameters to predict solubility data presented in 59 (plot B)

Such differences in solubilities and non-consistency of data are generally caused by impurities in solute and/or solvent 97, improper calibrations of pressure, temperature and analytical equipment, use of sampling techniques with different precisions 98, 99 and technical variations in experimental setups 55. Minimization of potential source of errors and standardization of experimental and sampling procedures will greatly help in generating consistent, accurate and reliable solubility and phase equilibrium data.



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6. Conclusion A review of the available literature indicates that there are similarities between pyrolysis oil and biocrude from lignocellulosic biomass in terms of chemical composition. Major and industrially important compounds are highlighted from the monophenol and low molecular weight acid fractions of bio-oil. Few experimental studies are reported in the literature on supercritical extraction of bio-oil. For the experimental studies reported results were encouraging in terms of extract yields and percent recoveries of phenol and acid fractions. Design of an efficient supercritical extraction process for bio-oil necessarily requires extensive solubility data determination of different bio-oil compounds and rigorous thermodynamic modelling of complex bio-oil phase equilibria. Binary system experimental solubility data for some of the compounds present in bio-oil are available in the literature, however there are still many other important compounds for which binary system (bio-oil compound + CO2) solubility data are required. Moreover, discrepancies exist in some of the solubility data available in the literature. Accurate and consistent solubility data generation at relevant supercritical conditions (308-353 K and 80-350 bar) are required for efficient supercritical CO2 extraction process design for bio-oil fractionation. Solubility data is also of utmost importance for getting experimental binary interaction parameters to be used in complex phase behaviour modelling. Semi-empirical or thermodynamic correlation of solubility data are useful tools with which to compare solubility trends of different compounds and to provide a method of estimating temperature and pressure conditions for optimum solubility and selectivity of a compound out of a complex mixture. The measurement of extensive (but currently unavailable) binary, ternary, quaternary and multicomponent phase equilibria data will provide the basis for rigorous thermodynamic modelling, optimisation and process design of practical bio-oil fractionation plants using supercritical CO2.

Supporting Information Tables listing contents of single ring phenolics and low molecular weight carboxylic acids in bio-oils; solubilities of different compounds in supercritical carbon dioxide This material is available free of charge via the Internet at http://pubs.acs.org.

Author Information Corresponding Author *Email: [email protected]

Notes The authors declare no competing financial interest.


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Acknowledgments This work was undertaken with Australian Federal Government and Queensland University of Technology support under the Australia-India Strategic Research Fund program.

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Supercritical Carbon Dioxide Separation of Carboxylic Acids and

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