Guaiacol and its mixtures: New data and predictive models. Part 2: Gibbs energy of solvation
Introduction
Lignocellulosic biomass (LCB) can be a potential resource for the production of different chemical reagents and fuels. LCB usage will help to protect the environment and to reduce the dependence on fossil fuels, which is important for sustainable development of economics as well as for creation of new workplaces at the regional level and development of rural areas [1]. There is now a commitment from the chemical industry to develop new green chemistry-based processes [[2], [3], [4], [5], [6]], and LCB is called to play a major role as an alternative raw material. It has several advantages in comparison with fossil raw materials: renewable, widely available and better distributed throughout the world. As a matter of fact, lignocelluloses can be used to synthesize target molecules for many applications, which are consistent with the principles of green chemistry [6]. However, the development of effective processes to produce chemicals from LCB is limited by the availability of design tools that allow the prediction of physical-chemical properties of molecules when one only knows their structure.
It is expected that bioresources will be processed in plants called biorefineries. As in classic refineries, biorefineries consist of unit operations where separations are governed by chemical thermodynamics. The design of green and innovative processes for the valorisation of biomass requires the understanding of the thermodynamic behavior of species associated to LCB feedstocks. These mixtures are particularly complex due to the wide variety of oxygenated compounds they might contain. In particular, the decomposition of the LCB raw material leads to the formation of a large variety of multifunctional oxygen-bearing compounds. This leads to strong intra- and intermolecular interactions that make these mixtures highly non-ideal (from a thermodynamic point of view). The models used for hydrocarbons (models typically found in most process simulators) fail to reproduce these non-idealities. Therefore, the challenge for the design and optimization of biorefinery units is to have appropriate tools that can adequately reproduce the phase equilibrium and properties of these mixtures, in the same way as fossil mixtures are described in current industrial applications.
Another application that requires the development of thermodynamic models is the European regulatory context. The European legislation (REACH) pushes the chemical industry to provide adequate predictive estimations of the possible effects of molecules on humans and the environment [7].
A good reproduction of phase equilibria is of special importance for the design of separation technologies in biorefineries. Predicting the affinity of oxygen-bearing molecules with respect to a given solvent is a relevant step for designing separation processes, and thus predictive models that are able to take into account the molecular diversity and the complex interactions taking place in biomass-based mixtures are required.
Guaiacol and guaiacol derivatives are products of the breakdown of lignin when processing LCB. Guaiacols are phenolic compounds that have a methoxy group and a hydroxyl group. Guaiacols are used as model molecules to understand the breakdown of LCB [8,9]. Due to the presence of two oxygen-bearing groups and an aromatic ring, inter- and intramolecular interactions play a major role in phase equilibrium and phase properties of pure guaiacol and its mixtures with polar and/or associating solvents. When inventorying the available phase equilibrium data for binary mixtures of guaiacol with different solvents [10], it appears that:
- •
The most frequent data in mixture with hydrocarbons (normal and iso-alkanes from C6 to C16) and some aromatics (benzene, toluene) are infinite dilution activity coefficients (IDAC) at two temperatures: 321 and 331 K. All these IDAC data are provided by the same author [10]. Notice that, due to the technological limitations explained later in this paper, it is the IDAC of the solvent infinitely diluted in guaiacol that is provided.
- •
A few VLE/LLE data are provided for guaiacol with other solvents: water, some alcohols (methanol, ethanol and octanol) and benzene.
In the first part of this work devoted to guaiacol [11], the GC-PPC-SAFT EoS was used to reproduce the thermodynamic behavior of pure guaiacol and its mixtures with methane, carbon dioxide, ethanol, octanol, water, acetone, butyl acetate, n-hexadecane, hydrogen, carbon monoxide, hydrogen sulfide, ammonia. In particular, the choice of an appropriate association scheme for guaiacol was discussed. The importance of using binary mixtures data instead of pure compound data only was shown to be of major importance when dealing with such a choice.
In this work, we measured activity coefficients of binary mixtures of guaiacol with organic solvents at different composition of components and temperatures. The interest is to study the behavior of solvents having different characteristics in terms of hydrogen bonding and polarity. Guaiacol is a high added value molecule that can be obtained during biomass processing. Knowing its behavior in different solvents can be of major help to study extraction processes for this molecule and other aromatic oxygen-bearing molecules. The measurements were carried out using the headspace method [12]. Headspace analysis became popular over recent years and has now gained worldwide acceptance for analysis of, for example, alcohols in blood and residual solvents in pharmaceutical products. It allows obtaining activity coefficients of solutes in a large range of concentration. Moreover, activity coefficients can be calculated for several solutes in multi-component mixture simultaneously. However, there are some restrictions and limitations for this method. The most important for us is that the solute should have detectable saturated vapor pressure. Thus, activity coefficients of large molecules are analyzed with high uncertainties. Guaiacol has extremely low saturated vapor pressure as its vapor is barely appreciable by the detector of a gas chromatograph. Therefore, activity coefficients of guaiacol are not measured in this work. They were obtained from the measured activity coefficients of solvents diluted in guaiacol, which were then used to fit the NRTL Gibbs energy model. The NRTL model was used to determine the guaiacol activity coefficient. This approach is consistent by virtue of the Gibbs-Duhem equation. The development and use of reliable predictive methods can resolve this restriction. The solvents used in this work were ethanol (EtOH), tetrahydrofuran (THF) and acetonitrile (ACN). From the measured activity coefficients, we deduced the infinite dilution activity coefficients (IDAC) by extrapolating the correlated NRTL model to infinite dilution conditions. The IDAC values were then converted into Gibbs energy of solvation and compared to the values predicted with different approaches, namely:
- •
Monte Carlo molecular simulation using the AUA force field and the thermodynamic integration technique [13].
- •
Group-contribution PPC-SAFT equation of state developed by Tamouza [14].
- •
COSMO-SAC activity coefficient model [15,16].
- •
UNIFAC PSRK model [17].
Section snippets
Materials
All samples used in this work were of commercial origin (see Table 1). Pure guaiacol (2-methoxyphenol) was purified by repeated vacuum fractional distillation under a nitrogen atmosphere. Acetonitrile, ethanol and tetrahydrofuran were dried and purified before usage by standard methods [12] up to minimal mass fraction 0.995. Sample purities were determined by using the Agilent 7890 B gas chromatograph equipped with the flame ionization detector. The water content was determined by titration
GC-PPC-SAFT EoS
The GC-PPC-SAFT (Group Contribution-Polar Perturbed-Chain Statistical Associating Fluid Theory) Equation of State is a predictive model based on the polar PC-SAFT equation developed by Gross and Sadowski [19,20], coupled to a group contribution method (GC). It is defined as a sum of Helmholtz energy contributions:where the first four terms relate to the non-polar interactions and follow the theory developed by Gross and Sadowski [19,20], the last
Activity coefficients
Fig. 4, Fig. 5, Fig. 6 show activity coefficients of ethanol, acetonitrile and tetrahydrofuranin mixture with guaiacol at different composition, as measured and computed in this work. Experimental activity coefficients are provided in Table 2, Table 3, Table 4. These data are compared to the predictions provided by UNIFAC DMD, COSMO-SAC and GC-PPC-SAFT. The analysis is based on the observation that both self-association and polarity in one of the compounds leads to positive deviations from
Conclusions
The activity coefficients of different solvents (tetrahydrofuran, acetonitrile and ethanol) in guaiacol have been measured by the head space technique. The detection limits of this technique depend on the volatility of the molecules being analyzed, and thus only the activity coefficients of volatile solvents in guaiacol could be measured. The infinite dilution activity coefficients (IDAC) of guaiacol in the solvent and that of the solvent in guaiacol could be obtained by fitting the NRTL model
Acknowledgements
This work has been partly performed according to the Russian Government Program of Competitive Growth of Kazan Federal University. R.N gratefully acknowledges the financial support by the research grant of Kazan Federal University and the financial support by the Russian Ministry of Education and Science No. 4.5289.2017/9.10. It has also partly been funded by the Tuck foundation chair for Biofuels Thermodynamics.
References (39)
- et al.
Overview of fuel properties of biomass fast pyrolysis oils
Energy Convers. Manag.
(2009) - et al.
Head-space analysis in modern gas chromatography
Trends Anal. Chem.
(2002) - et al.
Group contribution method with SAFT EOS applied to vapor liquid equilibria of various hydrocarbon series
Fluid Phase Equil.
(2004) - et al.
A group contribution equation of state based on UNIFAC
Fluid Phase Equil.
(1991) - et al.
Application of perturbation theory to a hard-chain reference fluid: an equation of state for square-well chains
Fluid Phase Equil.
(2000) - et al.
Application of GC-SAFT EOS to polar systems using a segment approach
Fluid Phase Equil.
(2008) - et al.
Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling
J. Comput. Phys.
(1977) - et al.
Avoiding singularities and numerical instabilities in free energy calculations based on molecular simulations
Chem. Phys. Lett.
(1994) - et al.
Molecular simulation of the hydration gibbs energy of barbiturates
Fluid Phase Equil.
(2010) Les débouchés non alimentaires des produits agricoles : un enjeu pour la France et l'Union Européenne
Rapport du Conseil Economique et Social
(2004)
Top value added chemicals from biomass. Volume I – results of screening for potential candidates from sugars and synthesis gas
PNNL (DOE)
Top-value added chemicals from biomass. Volume II–Results of screening for potential candidates from biorefinery lignin
PNNL(DOE)
Situation et perspectives de developpement des productions agricoles a usage non alimentaire
Rapport Ministère de l'Agriculture et de la Pêche
Chemicals from biomass
Science
Chapter 3: process options for the catalytic conversion of renewables into bioproducts
A general guidebook for the theoretical prediction of physicochemical properties of chemicals for regulatory purposes
Chem. Rev.
Solvent fractionation method with brix for rapid characterization of wood fast pyrolysis liquids
Energy Fuels
Thermophysical Properties of Pure Substances and Mixtures, Version 2012
Guaiacol and its mixtures: new data and predictive models Part 1:Phase equilibrium
Fluid Phase Equil.
Cited by (4)
Group-contribution SAFT equations of state: A review
2023, Fluid Phase EquilibriaInteraction mechanism of collagen peptides with four phenolic compounds in the ethanol-water solution
2021, Journal of Leather Science and EngineeringCan we safely predict solvation Gibbs energies of pure and mixed solutes with a cubic equation of state?
2019, Pure and Applied Chemistry