High-pressure phase equilibrium in a carbon dioxide-acetone isopropanol ternary system: obtaining and analysing new datasets

BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS MSc in Chemical Engineering THESIS PROJECT “High-pressure phase equilibrium in a carbon dioxide-acetone- isopropanol ternary system: obtaining and analysing new datasets” Written by Pablo Miranda Rey Supervised by Márton Kőrösi, Department of Chemical and Environmental Process Engineering Budapest, JANUARY 2022 Statement of the supervisor I, Márton Kőrösi as supervisor, hereby declare that the thesis written by Pablo Miranda Rey, (Neptune code: PDQ13B) titled “High-pressure phase equilibrium in a carbon dioxide-acetone-isopropanol ternary system: obtaining and analysing new datasets” is his own writing prepared under my supervision. I also declare that the thesis meets the formal and professional requirements of the Budapest University of Technology and Economics and those of the Faculty of Chemical Technology and Biotechnology, thus I support its submission. Budapest, January 2022 Márton Kőrösi Statement of the student I, Pablo Miranda Rey (Neptun code: PDQ13B) as author of the thesis hereby declare that my thesis titled “High-pressure phase equilibrium in a carbon dioxide-acetone- isopropanol ternary system: obtaining and analysing new datasets” is my original writing and I have not plagiarised any other work. All third party materials including published and unpublished sources were referenced. I acknowledge that the intellectual property rights of the methods used and the results of any research or development described in the thesis belong to the participating researchers and institutions/companies, thus their utilization or publication must not be initiated before the approval of all parties. I also declare that during the preparation and writing the thesis I did not mislead my supervisor(s) and thesis advisor. Budapest, January 2022 Pablo Miranda Rey 2 Contents Acknowledgment .......................................................................................................... 3 Abstract ........................................................................................................................ 4 List of Figures ............................................................................................................... 5 List of Tables ................................................................................................................ 6 List of Acronyms .......................................................................................................... 7 List of Symbols ............................................................................................................. 7 1. Introduction ........................................................................................................... 8 2. Literature summary ............................................................................................. 10 2.1. Supercritical fluids: importance of phase equilibrium measurements ............. 10 2.2. Supercritical carbon dioxide (scCO2) ............................................................ 14 2.2.1. Properties and interest ............................................................................ 15 2.2.2. Processes and applications. .................................................................... 16 2.3. Equilibrium measurement methods (modelling) ............................................ 17 2.4. Short review on the ternary mixtures available .............................................. 20 3. Materials and methods ......................................................................................... 23 3.1. Materials ....................................................................................................... 23 3.2. Experimental method .................................................................................... 23 3.3. Calculation methods...................................................................................... 27 4. Results ................................................................................................................ 29 4.1. Measured data ............................................................................................... 29 4.2. Thermodynamic modelling ........................................................................... 47 5. Summary ............................................................................................................. 60 6. Literature bibliography ........................................................................................ 62 7. Appendix ............................................................................................................ 68 7.1. Molar density of CO2 in the applied temperature range. ................................ 68 7.2. Laboratory measurements. Opacity Points..................................................... 68 7.3. Laboratory measurements. Redissolution Points............................................ 73 3 Acknowledgment Project no. 2019-1.3.1-KK-2019-00004 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the 2019-1.3.1-KK funding scheme. I would like to thank the Budapest University of Technology and Economics (BME) and the University of Valladolid (UVa) for offering me the possibility to do this project within the 2021-2027 Erasmus+ programme funded by the European Union. I would also like to thank all the people who work in the BME Department of Chemical and Environmental Process Engineering for their welcome, and especially, my supervisor Márton Kőrösi for his total availability and help during this work. Finally, to my colleagues and friends, for these unforgettable months in Budapest. 4 Abstract This paper describes the work carried out to obtain and analyse the thermodynamic equilibrium dataset for the ternary mixture of carbon dioxide, acetone and isopropanol in a range of temperatures between 308.15 and 328.15 K and pressures between approximately 5 MPa and 14 MPa. The composition (molar fraction) of each compound was varied approximately in the ranges 𝑥𝐶𝑂2∶(0.7;0.99); 𝑥𝑎𝑐𝑒𝑡𝑜𝑛𝑒 ∶(0.003;0.24);𝑥𝑖𝑃𝑟𝑂𝐻 ∶ (0.001;0.14) maintaining 3 different volumetric ratios of acetone/isopropanol: 1:1, 3:1 and 5:1. The measurements are based on the use of a static synthetic method in which the opacity and redissolution points are visually observed in a high-pressure, variable volume view-cell. The system has not been previously studied and therefore represents a novel contribution to high-pressure phase equilibria investigation. The text can be divided into three main parts: (i) where the characteristics of supercritical fluids, especially supercritical CO2 and the importance of phase equilibrium measurement in these systems are addressed; (ii) in which the experimental process carried out in the laboratory to obtain the data, as well as the equipment, the different existing phase equilibrium measurement methods and the one used in this work are explained; and (iii) in which the results obtained are analysed with the aim of predicting and understanding the behaviour of the system. To this end, a discussion of the raw data is conducted and the thermodynamic modelling using the Peng-Robinson and Soave- Redlich-Kwong equations of state is carried out. Keywords: High-pressure phase equilibrium ∙ Ternary system ∙ Static methods ∙ View cell ∙ Carbon dioxide ∙ Acetone ∙ Isopropanol ∙ Thermodynamic modelling. 5 List of Figures Figure 1. Single-component phase diagram with the critical pressure and temperature of carbon dioxide included. ............................................................................................. 10 Figure 2. Dew-bubbles curves for a non-volatile solute at infinite dilution ................. 11 Figure 3. Historical development and geographical distribution of publications and patents in the field of scCO2 power systems. ............................................................... 14 Figure 4. Classification of experimental methods for high-pressure phase equilibria ... 18 Figure 5. Image of phase transitions for the system CO2 + n-hexadecane like those observed in this work .................................................................................................. 19 Figure 6. Schematic representation of the cell ............................................................. 24 Figure 7. P-T diagram for a 1:1 volumetric acetone/isopropanol ratio .......................... 38 Figure 8. P-T diagram for a 3:1 volumetric acetone/isopropanol ratio .......................... 39 Figure 9. P-T diagram for a 5:1 acetone/isopropanol ratio ........................................... 40 Figure 10. Linear surface graph for measurement results with a 1:1 volumetric ratio of acetone/isopropanol .................................................................................................... 43 Figure 11. Linear surface graph for measurement results with a 3:1 volumetric ratio of acetone/isopropanol .................................................................................................... 43 Figure 12. Linear surface graph for measurements with a 5:1 volumetric ratio of acetone/isopropanol .................................................................................................... 44 Figure 13. Quadratic surface graph for 1:1 volumetric ratio of acetone/isopropanol..... 45 Figure 14. Quadratic surface graph for 3:1 volumetric ratio of acetone/isopropanol..... 46 Figure 15. Quadratic surface graph for 5:1 volumetric ratio of acetone/isopropanol..... 46 Figure 16. P-T diagram for the PR model with the BIPs (non-temperature dependant). 51 Figure 17. P-T diagram for the PR model with the BIPs=0. ......................................... 52 Figure 18. P-T diagram for the PR model with the BIPs (temperature dependant). ...... 53 Figure 19. P-T diagram for the PR model with BIPs obtained when limits are relaxed (temperature dependant). ............................................................................................. 54 Figure 20. P-T diagram for the SRK model (non-temperature dependant). .................. 56 Figure 21. P-T diagram for the SRK model (temperature dependant). ......................... 56 Figure 22. Estimated pressure vs measured pressure for the PR model. ....................... 57 Figure 23. Estimated pressure vs measured pressure for the SRK model. .................... 58 Figure 24. Estimated temperature vs measured temperature for the PR model. ............ 58 Figure 25. Estimated temperature vs measured temperature for the SRK model. ......... 59 6 List of Tables Table 1. Summary of literature on high-pressure studies with the compounds involved in this work ................................................................................................................. 22 Table 2. Compounds used in this work ........................................................................ 23 Table 3. Measurements conducted in this work ........................................................... 25 Table 4. Average data of the opacity points for each composition................................ 29 Table 5. Average data of the redissolution points for each composition ....................... 33 Table 6. Linear fitting results of the cloud point pressure as a function of the x_CO2 and temperature. ................................................................................................................ 42 Table 7. Quadratic fitting results of the cloud point pressure as a function of the x_CO2 and temperature. ......................................................................................................... 45 Table 8. Critical properties and acentric factor for pure compounds. ........................... 49 Table 9. Binary parameters obtained from the regression with Aspen. Peng Robinson equation of state. ......................................................................................................... 51 Table 10. Binary parameters for Peng Robinson's model. Temperature dependence. ... 52 Table 11. Dependence of binary parameters on temperature for the PR model............. 53 Table 12. Binary parameters obtained from the regression with Aspen. SRK model. ... 55 Table 13. Binary parameters obtained from the regression with Aspen. SRK model. Temperature dependence. ............................................................................................ 55 Table 14. Dependence of binary parameters on temperature for the SRK model. ......... 55 Table 15. Molar densities of CO2 from NIST. ............................................................. 68 Table 16. Laboratory raw data. Opacity points ............................................................ 68 Table 17. Laboratory raw data. Redissolution points ................................................... 73 7 List of Acronyms BIP: binary interaction parameter EOS: equation of state GAS: gas antisolvent precipitation HTL: hydrothermal liquefaction HTC: hydrothermal carbonization PR: Peng-Robinson RESS: rapid expansion of supercritical solutions RMSE: root mean square error SAS: supercritical antisolvent precipitation scCO2: supercritical carbon dioxide SCF: supercritical fluid SCW: supercritical water SFE: supercritical fluid extraction SRK: Soave-Redlich-Kwong VLE: vapor-liquid equilibrium List of Symbols a: Energy parameter [J∙m3/mol2] b: Co-volume parameter [m3/mol] i,j: counter k: interaction parameter 𝑇: temperature [K] 𝑇𝑐: critical temperature [K] 𝑇𝑟: reduced temperature [K] 𝑃𝑐: critical pressure [MPa] ω: acentric factor 𝑉𝑚: molar volume [m3/mol] 14 obtained directly from hydrothermal processes such as hydrothermal liquefaction (HTL), hydrothermal carbonization (HTC) or hydrothermal gasification (HTG) of biomass, among others (Knez et al. 2014). The advantage of some of these technologies is that they allow working with feedstocks with a high degree of moisture, avoiding the drying of the same at the entrance of the process, which results in significant energy savings. Despite this, it is necessary to assess the process from a global perspective due to the higher energy expenditure because of high pressure and temperature (Hrnčič et al. 2016). Supercritical carbon dioxide (scCO2) is probably the supercritical fluid with the widest variety of applications and articles available. 2.2. Supercritical carbon dioxide (scCO2) There is a global trend towards the use of new sustainable and green technologies due to the growing need to eliminate more environmentally damaging processes and match increasingly restrictive regulations. In this context, scCO2 has been presented as an alternative to other traditionally used compounds, such as organic solvents. What was more experimental work a few years ago is now becoming a reality at industrial level. The market of products manufactured in processes with scCO2 is a field that is constantly growing and the use of scCO2 for energy purposes has led to an exponential increase in publications and patents in this field in recent years (Figure 3). Most of the patents associated with this topic have been developed in China (dark blue), while the literature has mainly been developed in other countries (White et al. 2021). Figure 3. Historical development and geographical distribution of publications and patents in the field of scCO2 power systems.CN=People´s Republic of China; US= United States of America; KR=Republic of Korea; OTHER=Rest of the World (White et al. 2021). 15 The main advantage of scCO2 has to do with its thermodynamic and transport properties that make it suitable for use in many industrial applications, which will be discussed in this section. 2.2.1. Properties and interest ScCO2 is non-toxic (in small quantities), non-flammable and can be considered relatively inert. It is also a compound that is available in large quantities and can be integrated into processes in a circular way in the future. Despite being a well-known greenhouse gas, if it is obtained from an environmentally benign source, these characteristics make it of particular interest as a green solvent, but it can also be used for the purpose of selectively dissolving a compound, among others (Boyère et al. 2014). Products of high added value may compensate for the costs of high-pressure industrial procedures. The critical pressure (7.39 MPa) and critical temperature (31.1 ºC) of carbon dioxide, are relatively mild conditions to reach compared to other supercritical fluids such as water (Kiran et al. 2000). It is a poor solvent for polar substances, although some polar compounds such as acetone or methanol are soluble. Solubility increases for non-polar and low molecular weight substances (Boyère et al. 2014). On the other hand, it is a compound with a high diffusion rate, which results in supersaturated systems that allow the formation of particles with low particle size and a narrow particle size distribution (Wang et al. 2021). This high capacity in transport and mass transfer properties coupled with a low viscosity make scCO2 a fluid capable of replacing many organic solvents but also warns of good heat transfer, which makes it suitable for use in power generation processes (Crespi et al. 2017). In addition, other advantages that increase its interest for industrial applications are the feasibility of obtaining high purity carbon dioxide and its ability to be recovered and reintroduced into the process due to its high volatility (Nikolai et al. 2019). Another advantage is that carbon dioxide almost completely evaporates from the products. If said products are later ingested, the traces of carbon dioxide in them are not harmful, unlike those of many organic solvents, which may be of great interest for food applications. 16 2.2.2. Processes and applications. The specific properties of scCO2 result in several direct applications. Firstly, it can be used in solid-fluid extraction processes such as decaffeination of coffee and tea where the use of other conventional processes with higher temperatures is avoided (Wang et al. 2021) or the extraction of sesame oil (Brunner 2010). Selective extraction, purification and fractionation can be carried out easily by modifying the density of scCO2. For instance, a low-density scCO2 can imitate the polarity of n-pentane (non-polar) while a high-density scCO2 can mimic the polarity of pyridine (polar) (Ramsey et al. 2009). The extraction of metals from aqueous solutions such as Cu2+, Cr3+ or Zn2+ among others, is a direct application of the advantages of this compound as an extracting agent over organic solvents (Erkey 2000). Firstly, the volume of organic solvent used in conventional solvent extraction is usually high compared to scCO2, as a specific organic solvent to aqueous phase ratio must be maintained. Secondly, the residual contamination of the aqueous phase is higher when organic solvents are used. Thirdly, the excellent diffusivity properties and low viscosities coupled with the low surface tension compared to organic solvents provides a larger contact area between the phases, resulting in a reduction of equipment size. The second use has to do with the ability of this fluid to serve as a medium for different chemical reactions to take place. Frequently, reaction rates are enhanced and the energy required in the process is decreased compared to traditional solvents, due to the improved matter transfer of the fluid, leading to copolymerisation, oxidation, dimerization and trimerization, carbonylation and other reactions (Ramsey et al. 2009). At other times, however, reaction rates are lower, as in the Wacker reaction (Gang et al. 2003). Some authors have studied the characteristics of various polymerisation reactions in scCO2 which are directly related to the synthesis of polymers, using techniques such as homogenisation, precipitation or for example dispersion polymerisation for the formation of PVC (poly(vinyl chloride)), PCL (polycaprolactone) or PDMS (poly(dimethyl siloxane) derivatives) among others (Boyère et al. 2014). In addition, scCO2 can be used as a reaction medium and as a reactant at the same time, which has led to the study of improvements in the selectivity and yield of reactions in molecular catalysis (Ikariya and Kayaki 2000). Homogeneous and heterogeneous catalysis processes can be carried out in this medium (Ramsey et al. 2009) such as the hydroformylation process of olefins (Erkey 2011) or the hydrogenation of limonene (Bogel-Łukasik et al. 2010) respectively. The use 17 in biotechnological applications is also of great importance. Thus, enzyme stabilisation processes as well as reaction processes involving enzymes have been studied (Matsuda 2013). Polymer formation processes based on biocatalysis are also among the possibilities that are still being investigated (Ramsey et al. 2009). Another application is the formation of aerogels by supercritical drying, where the original organic solvent in the gel is removed, giving rise to structures with a particular consistency, which can be used as catalyst supports or as drug carriers (Brunner 2010). Some of the factors in the preparation of aerogels from pure silica has recently been studied (Shafi et al. 2021). Cleaning and degreasing procedures are other not so common processes where scCO2 is used, replacing detergents, water, or organic solvents. This application, together with dyeing processes are starting to be applied in the textile industry, where the cost of wastewater treatment and the required water supply is known to be very high (Ramsey et al. 2009). The solubility of the dye needs to be known as a function of pressure and temperature, so it is important to know the phase equilibrium data. In addition, some natural fibres can be dyed without pre-treatment and the dyeing time can be greatly reduced with scCO2 compared to other traditional disperse dyes (Brunner 2010). Supercritical carbon dioxide has also been utilized as a solvent in industrial-scale wood impregnation processes. Finally, the use of scCO2 as an agent in power generation cycles is again on the rise and it has been theoretically demonstrated that they can be competitive in the sense that they have a high versatility and high yields at moderate temperatures compared to other classical technologies (Crespi et al. 2017). Some of the potential markets for this technology are focused on industrial waste heat recovery, nuclear plants or bulk energy storage and geothermal scCO2 power plants (Brun et al. 2017). 2.3. Equilibrium measurement methods (modelling) Phase equilibrium data to describe the behaviour of a system becomes valuable even during the early stage of the development of processes applying supercritical fluids. Precise laboratory measurement methodologies have been elaborated to efficiently gather the necessary information. The methods used in these studies are mainly classified into two large families: analytical methods and synthetic methods (Figure 4). Both differ in whether the exact 18 composition of the equilibrium phases is determined, or the total composition of the mixture is known. Thus, analytical methods do not require knowledge of the total composition, but rather an analytical study of the coexisting phases is carried out (Fonseca et al. 2011). Figure 4. Classification of experimental methods for high-pressure phase equilibria (Peper et al. 2019). These phases are subsequently analysed at atmospheric pressure by sampling (for example by chromatography) or without sampling and using physicochemical methods such as spectroscopy. Analytical methods with sampling include isothermal analytical methods (AnT), where the temperature stays constant during the process, isobaric analytical methods (AnP), where the pressure stays constant, or a combination of both (AnPT), where a stream is constantly pumped into a cell and the temperature is also controlled. Non-sampling methods include mainly spectrometric and gravimetric methods (Fonseca et al. 2011). Synthetic methods are based on knowing the exact total composition of the mixture and then observing how it behaves at equilibrium without further sampling of the phase or phases. These methods differ in whether a phase transition occurs or not. In methods with phase transition, first the pressure and temperature conditions of the system must be adjusted to obtain a single homogeneous phase. Subsequently, the pressure or temperature is varied until there is an abrupt change in the system indicating the formation (or disappearance) of a new phase the composition of which is not known. Depending on whether the phase transition is detected visually or not, the methods can be divided into visual (SynVis) or non-visual (SynNon) groups. Figure 5 shows a visual synthetic process in which the different phase equilibria can be observed (Braga et al. 2021). Picture 1 shows a completely homogeneous system where only one liquid phase is visible. Picture 19 2 shows a system with two liquid phases while picture 3 shows a vapour phase and two liquid phases. The white object at the bottom is a magnetic stirrer. Figure 5. Image of phase transitions for the system CO2 + n-hexadecane like those observed in this work (Braga et al. 2021). In non-phase transition methods, the mass balance is used to calculate the compositions and other properties such as temperature, pressure, volumes of each phase or densities are measured. There are isothermal (SynT), isobaric (SynP), or other methods (SynOth). In the isothermal process, a compound with exactly known amount is introduced into the cell before it is brought to the temperature that is set. Subsequently, a known exact amount of a second compound is introduced into the cell and a pressure drop occurs after dissolving in the liquid phase. The composition of the vapour phase can be calculated from phase equilibrium models using pressure and temperature measured in the cell. The mass and component balance is used to calculate the liquid’s composition. The process would be similar for the isobaric method but setting a constant pressure. Synthetic methods are often used to solve problems that may arise when using analytical methods, such as in systems where there is no good phase separation due to the very similar densities of the forming phases. In addition, they are faster methods as they avoid the characterisation of the samples (Fonseca et al. 2011; Peper et al. 2019) In obtaining thermodynamic data, it is desirable that systems are studied by a larger number of authors and therefore, combining the use of synthetic and analytical methods as well as the use of different equipment. In this way, the validation of the results can be carried out and the new data set is contrasted. This work uses a synthetic method with visual phase transition in a static cell. 20 2.4. Short review on the ternary mixtures available There are many pure substances that can be combined and that can give rise to different phase equilibrium situations. Such situations may include the mixture of carbon dioxide and a single co-solvent, eventually in the presence of a solute (forming a ternary mixture). In case of multiple co-solvents to tune the polarity and dissolving capability of the mixture, even more complex phase behaviour may be expected. In each process, where a mixture is applied, the phase behaviour has to be taken into account, whether there will be one homogeneous phase or multiple phases under the circumstances to be established, and the behaviour of the system has to be carefully analysed. This work focuses mainly on the use of three compounds, namely 2-propanol (isopropanol), 2- propanone (acetone) and carbon dioxide. Acetone and isopropanol are common chemical compounds the behaviour of which has been studied. There is a large body of literature in which these compounds are used separately (Pasanen et al. 2006; Lazzaroni et al. 2006; Wang et al. 2021), although their occurrence together in ternary systems is non-existing. The importance of this work lies in the fact that it has studied a system that has not been previously analysed. It should be noted that this work focuses on high-pressure systems, traditionally considered to be those where the pressure is above 1 MPa. For isopropanol, there are different studies working with the pure compound, in varying temperature ranges, from which the critical parameters of the compound can be obtained (Dell’Era et al. 2007; Khoiroh and Lee 2011; Keshtkari et al. 2013). Authors of other papers have studied different thermodynamic properties of pure acetone between 5-50 °C and at high pressures (Malhotra and Woolf 1991). The breadth of literature on systems studying carbon dioxide is greater, mainly due to the possible applications described above in high-pressure systems. Generally, these publications deal with the study of the equilibrium of binary mixtures, but the behaviour of pure carbon dioxide is also studied in a temperature range similar to the present work (Kim et al. 2010; Fonseca and von Solms 2012). There are also studies where these compounds are combined as part of binary, ternary or quaternary mixtures where the pressure ranges are varied and may include carbon dioxide (Peper et al. 2019). For instance, the CO2-acetone binary system is a mixture that has been studied in detail. Some studies use an isochoric technique to investigate its behaviour and to obtain the dew points in areas close to the critical region (pressure and temperature) (Wu et al. 2004). There are others that also study this system, focusing on solubility of CO2 in 21 acetone and 1-butyl-3-methylimidazolium tetrafluoroborate (Lei et al. 2012) or using a continuous process that takes advantage of the change in signal from a Flame Ionisation Detector (FID) (Novitskiy et al. 2009). Some studies related to volumetric expansion in binary CO2 systems with some ketones, such as acetone, cite the presence of a slow volume expansion zone up to a CO2 mole fraction of 0.7 and a fast volume expansion zone above this value (Aida et al. 2010). Another study correlates the experimental results using the Peng-Robinson equation of state and the generalised version of the Bender equation of state (Bamberger and Maurer 2000). Studies of binary mixtures of carbon dioxide with isopropanol are not as numerous as for acetone but they are common and have been studied since the last quarter of the 20th century (Radosz 1986; Suzuki et al. 1991; Yaginuma et al. 1997). The presence of a cell for equilibrium measurements is already common at this time and properties such as solubility or density are studied. Also, there are studies using the binary mixture of carbon dioxide with isopropanol to validate a new method or to jointly determine vapour-liquid equilibrium (VLE) data and saturation densities (Galicia-Luna and Elizalde-Solis 2010). There are also articles where acetone and isopropanol appear together in the same study with carbon dioxide, either separately (Bamberger and Maurer 2000) or as part of ternary or quaternary systems together with other compounds such as argon (Lazzaroni et al. 2006). Some ternary systems formed by water + fluoromethane + isopropanol/acetone have been studied due to their importance in hydrate formation processes (Imai et al. 2012). The effect of salting out has also been studied in multiphase systems consisting of ethene, water and isopropanol to which an electrolyte is added. The aim of these experiments is to analyse this effect in systems using biomolecules to be extracted or to avoid their denaturation by adjusting the pH or the ionic strength of the solution (Ulanova et al. 2009). As for studies of ternary mixtures involving carbon dioxide together with acetone/isopropanol and a third compound, the present literature is abundant. For isopropanol, the number of studies is smaller than for acetone. Some examples are the use of synthetic methods with isopropanol as co-solvent in mixtures with palmitic acid (Brandt et al. 2010) or the study of phase behaviour when mixed with an ionic liquid such as 1-hexyl-3-methylimidazolium tetrafluoroborate (Kroon et al. 2010). For acetone, the variety of compounds used is greater. For example, the study of the structures of the hydrates formed with the use of supercritical CO2 and their stability were studied for different mole fractions of acetone (Maekawa 2011). As with isopropanol, studies have also been carried out with ionic liquids such as 1-butyl-3- 22 methylimidazolium tetrafluoroborate (Lei et al. 2012). Studies on these systems typically cover temperature ranges of around 50 °C (Peper et al. 2019), however, there are some more specific reviews carried out at much higher temperatures (>150 ºC) working with compounds such as β-cyclodextrin (Grandelli et al. 2012). Table 1 summarises the literature of relevance to this work. Table 1. Summary of literature on high-pressure studies with the compounds involved in this work. Mixture Conditions of the study Modelling CO2 (i)+acetone(j) (Wu et al. 2004) T(K): 313.2 and 393.2 P(MPa): 4.19-43.06 CO2 mole fraction: 0.9 Correlation with Peng-Robinson (PR) EOS and Sánchez-Lacomb (SL) EOS. PR mixing rule. PR 𝑘𝑖𝑗 = -0.03 (Lei et al. 2012) T(K): 298.2; 313.2 and 323.2 P(MPa): > 6 CO2 mole fraction: 0.1-0.9 PR EOS and PR mixing rule. 𝑘𝑖𝑗 nontemperature dependant between 298.2 and 323.2. PR 𝑘𝑖𝑗= 0.007 (Novitskiy et al. 2009) T(K): 350-380 P(MPa): 7.3-11.6 CO2 mole fraction: 0.2 and 0.7 Does not model the data (Bamberger and Maurer 2000) T(K): 293-333 P(MPa): 7.3-11.6 CO2 mole fraction: 0-1 PR EOS modified with Melhem and Generalized Bender EOS. Mixing rules of Panagiotopoulos and Reid. PR 𝑘𝑖𝑗= -0.0251; 𝑘𝑗𝑖= -0.0008 CO2 (i)+ isopropanol (j) (Radosz 1986) T(K): 317;335;354 and 394 P(MPa): 1.4-12 CO2 mole fraction: 0.45-0.60 Soave-Redlich-Kwong (SRK), PR and Zudkevitch and Joffe (RKJZ) EOS. Van der Waals on-fluid mixing rules. SRK 𝑘𝑖𝑗= 0.098; PR 𝑘𝑖𝑗= 0.107 (Suzuki et al. 1991) T(K): 313.7 and 333.7 P(MPa): >11 CO2 mole fraction: 0.95-1 No correlation. Enhancenment factor method for solubility consistency. (Yaginuma et al. 1997) T(K): 313.5 P(MPa): >9.8 CO2 mol fraction: 0-1 No correlation. VLE and density curves. (Galicia-Luna and Elizalde-Solis 2010) T(K): 313-363 P(MPa): 2.4-12.5 CO2 mole fraction: 0.1-0.9 Peng–Robinson equation of state coupled to classical and Wong–Sandler mixing rules. PR 0.111≤kij≤0,128 (T-dep.) (Bamberger and Maurer 2000) T(K): 293-333 P(MPa): 7.3-11.6 CO2 mol fraction: 0-1 PR EOS modified with Melhem and Generalized Bender EOS. Mixing rules of Panagiotopoulos and Reid. PR 𝑘𝑖𝑗= 0.1467; 𝑘𝑗𝑖= 0.1005 Others (Lazzaroni et al. 2006) CO2 + isopr.+ argon (1) CO2 + isopr. + argon + acetone (2) T(K): 313 P(MPa): 6.9-15 CO2 mol fraction (1): 0.15-0.85 CO2 mol fraction (2): 0.27-0.48 Patel-Teja (PT) EOS. Mathias–Klotz– Prausnitz (MKP) mixing rules. 𝑘𝑖𝑗: temperature dependant (Brandt et al. 2010) CO2 + palmitic acid + isopr. Tª(K): 313-318 P(MPa): 10-25 CO2 mol fraction (2): 0.94 and 0.97 Density-based models proposed by Mendez-Santiago and Teja for consistency of the data 23 3. Materials and methods 3.1. Materials The three compounds used are carbon dioxide, 2-propanone (acetone) and 2-propanol (isopropanol). The carbon dioxide was supplied by Linde Gas Hungary. The purity of this carbon dioxide (product Biogon C for food industry applications) is approximately 95 %. Carbon dioxide is used in all of the laboratory equipment freshly distilled. The acetone was supplied by Carlo Erba and had a purity of over 99.8% (GC) and isopropanol was supplied by Merck with a purity of over 99.5% (GC). The properties of the compounds are summarised in Table 2. Table 2. Compounds used in this work. Chemical Name CAS Number Source Purity (% mol) 2-propanone 67-64-1 Carlo Erba 99.8 2-propanol 67-63-0 Merck 99.5 Carbon dioxide 124-38-9 Linde Gas Hungary 95 3.2. Experimental method The main objective of the measurements carried out is to obtain a set of data that can be applied over a range of temperatures and compositions for the ternary equilibrium of carbon dioxide, 2-propanone (acetone) and 2-propanol (isopropanol). Acetone and isopropanol may be used as organic solvents in for example antisolvent fractionation. The measurements were carried out in a high-pressure view-cell (New Ways of Analytics GmbH.) containing two sapphire windows that allow the interior to be illuminated and fully visible from the outside (Figure 6). The cell can be used at a maximum pressure of 60 MPa and a maximum temperature of 250 °C. The illumination is carried out with a high brightness LED. The view of the cylindrical cell is provided horizontally. The cell is assembled using a circular set of screws between the lid and the rest of the cell. The sample to be tested can be introduced by removing this cover or through the opening corresponding to the temperature sensor. The choice depends on the state of the sample, in this case, liquid mixtures are used, so the sample is introduced through the ¼” opening of the temperature sensor (1.) by means of a measuring pipette. 30 Table 4. Average data of the opacity points for each composition (2/5). Volumetric ratio of organic solvents xCO2 xacetone x isopropanol T [K] P[MPa] 0.8930 0.0540 0.0530 317.55 7.97 0.8930 0.0540 0.0530 322.82 9.07 0.8930 0.0540 0.0530 327.28 9.65 0.9351 0.0326 0.0323 307.98 7.15 0.9351 0.0326 0.0323 312.35 7.87 0.9351 0.0326 0.0323 317.08 8.37 0.9351 0.0326 0.0323 322.45 8.89 0.9351 0.0326 0.0323 327.82 9.75 0.9444 0.0280 0.0277 307.28 7.23 0.9444 0.0280 0.0277 311.65 7.62 0.9444 0.0280 0.0277 318.12 8.56 0.9444 0.0280 0.0277 322.78 9.06 0.9444 0.0280 0.0277 327.98 9.51 0.9798 0.0103 0.0099 307.08 7.17 0.9798 0.0103 0.0099 311.80 7.67 1:1 Acet/Isopr 0.9798 0.0103 0.0099 317.18 8.30 0.9798 0.0103 0.0099 322.30 8.86 0.9798 0.0103 0.0099 327.63 9.51 0.9822 0.0091 0.0087 307.38 7.20 0.9822 0.0091 0.0087 312.43 7.65 0.9822 0.0091 0.0087 317.68 8.27 0.9822 0.0091 0.0087 322.60 8.96 0.9822 0.0091 0.0087 327.75 9.84 0.9939 0.0031 0.0030 306.45 7.13 0.9939 0.0031 0.0030 311.95 7.49 0.9939 0.0031 0.0030 317.30 8.19 0.9939 0.0031 0.0030 322.50 8.69 0.9939 0.0031 0.0030 327.08 9.36 0.9948 0.0026 0.0025 306.40 7.16 0.9948 0.0026 0.0025 312.28 7.64 0.9948 0.0026 0.0025 317.53 8.12 0.9948 0.0026 0.0025 321.98 8.77 0.9948 0.0026 0.0025 327.83 9.47 0.7668 0.1750 0.0582 307.60 5.65 3:1 Acet/Isopr 0.7668 0.1750 0.0582 312.15 6.00 31 Table 4. Average data of the opacity points for each composition (3/5). Volumetric ratio of organic solvents xCO2 xacetone x isopropanol T [K] P[MPa] 0.7668 0.1750 0.0582 316.23 6.91 0.7668 0.1750 0.0582 321.50 6.94 0.7668 0.1750 0.0582 327.15 7.77 0.7927 0.1556 0.0518 309.03 5.82 0.7927 0.1556 0.0518 311.98 6.79 0.7927 0.1556 0.0518 317.13 7.03 0.7927 0.1556 0.0518 320.63 7.24 0.7927 0.1556 0.0518 328.80 7.99 0.9052 0.0711 0.0237 306.93 6.76 0.9052 0.0711 0.0237 312.55 7.23 0.9052 0.0711 0.0237 317.85 7.83 0.9052 0.0711 0.0237 322.22 8.49 0.9052 0.0711 0.0237 327.58 9.22 0.9181 0.0614 0.0204 307.28 6.77 0.9181 0.0614 0.0204 311.55 7.35 3:1 Acet/Isopr 0.9181 0.0614 0.0204 317.85 8.08 0.9181 0.0614 0.0204 323.38 8.94 0.9181 0.0614 0.0204 327.08 10.37 0.9512 0.0366 0.0122 307.60 7.25 0.9512 0.0366 0.0122 311.58 7.73 0.9512 0.0366 0.0122 315.98 8.38 0.9512 0.0366 0.0122 321.43 9.25 0.9512 0.0366 0.0122 327.83 10.05 0.9570 0.0323 0.0107 307.35 7.38 0.9570 0.0323 0.0107 311.03 7.80 0.9570 0.0323 0.0107 316.03 8.48 0.9570 0.0323 0.0107 320.55 9.47 0.9570 0.0323 0.0107 326.05 10.21 0.9775 0.0169 0.0056 308.40 7.21 0.9775 0.0169 0.0056 312.63 7.84 0.9775 0.0169 0.0056 316.55 8.25 0.9775 0.0169 0.0056 322.13 9.52 0.9775 0.0169 0.0056 327.83 11.32 0.9810 0.0143 0.0047 307.75 7.46 0.9810 0.0143 0.0047 312.93 8.06 0.9810 0.0143 0.0047 317.13 8.55 32 Table 4. Average data of the opacity points for each composition (4/5). Volumetric ratio of organic solvents xCO2 xacetone x isopropanol T [K] P[MPa] 0.9810 0.0143 0.0047 322.58 10.06 0.9810 0.0143 0.0047 327.73 11.54 0.9988 0.0009 0.0003 307.18 7.21 3:1 Acet/Isopr 0.9988 0.0009 0.0003 312.08 7.57 0.9988 0.0009 0.0003 317.08 8.20 0.9988 0.0009 0.0003 322.58 8.87 0.9988 0.0009 0.0003 327.03 9.55 0.7129 0.2401 0.0471 307.95 5.03 0.7129 0.2401 0.0471 312.45 5.71 0.7129 0.2401 0.0471 317.65 5.90 0.7129 0.2401 0.0471 322.48 6.26 0.7129 0.2401 0.0471 326.62 7.06 0.7499 0.2091 0.0410 307.52 5.30 0.7499 0.2091 0.0410 310.78 6.00 0.7499 0.2091 0.0410 313.52 6.41 0.7499 0.2091 0.0410 323.32 6.93 0.7499 0.2091 0.0410 327.48 7.35 0.8961 0.0877 0.0162 307.45 6.55 0.8961 0.0877 0.0162 312.62 7.25 5:1 Acet/Isopr 0.8961 0.0877 0.0162 316.35 7.98 0.8961 0.0877 0.0162 322.48 8.88 0.8961 0.0877 0.0162 327.30 9.35 0.9100 0.0760 0.0140 307.88 6.71 0.9100 0.0760 0.0140 313.05 7.50 0.9100 0.0760 0.0140 317.02 7.98 0.9100 0.0760 0.0140 322.65 8.67 0.9100 0.0760 0.0140 327.55 9.71 0.9368 0.0534 0.0098 307.35 7.01 0.9368 0.0534 0.0098 312.00 7.70 0.9368 0.0534 0.0098 317.53 8.19 0.9368 0.0534 0.0098 322.63 9.16 0.9368 0.0534 0.0098 327.15 10.40 0.9454 0.0461 0.0085 306.95 7.11 0.9454 0.0461 0.0085 311.45 7.59 0.9454 0.0461 0.0085 317.28 8.57 0.9454 0.0461 0.0085 321.75 9.09 33 Table 4. Average data of the opacity points for each composition (5/5). Volumetric ratio of organic solvents xCO2 xacetone x isopropanol T [K] P[MPa] 0.9454 0.0461 0.0085 327.68 10.23 0.9759 0.0203 0.0037 307.40 7.32 0.9759 0.0203 0.0037 311.90 7.80 0.9759 0.0203 0.0037 316.85 8.50 0.9759 0.0203 0.0037 322.63 9.56 0.9759 0.0203 0.0037 327.40 10.38 0.9796 0.0172 0.0032 307.43 7.29 0.9796 0.0172 0.0032 311.83 8.03 5:1 Acet/Isopr. 0.9796 0.0172 0.0032 318.13 9.03 0.9796 0.0172 0.0032 321.98 10.02 0.9796 0.0172 0.0032 327.25 10.93 0.9938 0.0050 0.0011 310.08 7.45 0.9938 0.0050 0.0011 314.40 8.14 0.9938 0.0050 0.0011 318.38 8.61 0.9938 0.0050 0.0011 323.55 9.04 0.9938 0.0050 0.0011 328.60 9.62 Table 5. Average data of the redissolution points for each composition (1/5). Volumetric ratio of organic solvents x CO2 x acetone x isopropanol T [K] P[MPa] 0.7118 0.1476 0.1406 308.45 5.85 0.7118 0.1476 0.1406 313.98 6.33 0.7118 0.1476 0.1406 319.58 6.96 0.7118 0.1476 0.1406 324.62 7.68 0.7118 0.1476 0.1406 327.48 7.89 0.7491 0.1285 0.1224 311.63 6.77 0.7491 0.1285 0.1224 314.58 6.59 1:1 Acet/Isopr. 0.7491 0.1285 0.1224 319.60 7.28 0.7491 0.1285 0.1224 323.65 7.55 0.7491 0.1285 0.1224 329.03 7.95 0.8930 0.0540 0.0530 310.05 7.23 0.8930 0.0540 0.0530 315.35 7.65 0.8930 0.0540 0.0530 318.85 8.03 0.8930 0.0540 0.0530 325.68 9.90 0.8930 0.0540 0.0530 329.82 10.64 0.9351 0.0326 0.0323 310.55 8.15 34 Table 5. Average data of the redissolution points for each composition (2/5). Volumetric ratio of organic solvents x CO2 x acetone x isopropanol T [K] P[MPa] 0.9351 0.0326 0.0323 314.52 8.41 0.9351 0.0326 0.0323 319.05 9.23 0.9351 0.0326 0.0323 324.18 9.45 0.9351 0.0326 0.0323 330.12 10.03 0.9444 0.0280 0.0277 309.35 8.15 0.9444 0.0280 0.0277 313.95 8.45 0.9444 0.0280 0.0277 319.08 8.70 0.9444 0.0280 0.0277 325.25 9.76 0.9444 0.0280 0.0277 331.78 10.30 0.9798 0.0103 0.0099 309.43 7.38 0.9798 0.0103 0.0099 314.58 8.28 0.9798 0.0103 0.0099 318.98 8.73 0.9798 0.0103 0.0099 324.08 9.39 0.9798 0.0103 0.0099 328.73 9.76 1:1 Acet/Isopr. 0.9822 0.0091 0.0087 309.53 7.43 0.9822 0.0091 0.0087 314.98 8.05 0.9822 0.0091 0.0087 319.20 8.73 0.9822 0.0091 0.0087 324.03 9.61 0.9822 0.0091 0.0087 328.78 9.97 0.9939 0.0031 0.0030 307.73 7.48 0.9939 0.0031 0.0030 314.73 7.86 0.9939 0.0031 0.0030 318.33 8.59 0.9939 0.0031 0.0030 324.15 8.83 0.9939 0.0031 0.0030 328.35 9.67 0.9948 0.0026 0.0025 309.88 7.92 0.9948 0.0026 0.0025 314.28 8.03 0.9948 0.0026 0.0025 318.70 8.45 0.9948 0.0026 0.0025 323.30 9.24 0.9948 0.0026 0.0025 328.30 9.59 0.7668 0.1750 0.0582 309.58 5.90 0.7668 0.1750 0.0582 314.95 6.78 0.7668 0.1750 0.0582 320.83 8.16 0.7668 0.1750 0.0582 325.20 7.92 0.7668 0.1750 0.0582 329.50 8.41 3:1 Acet/Isopr. 0.7927 0.1556 0.0518 310.65 6.40 0.7927 0.1556 0.0518 314.33 7.40 0.7927 0.1556 0.0518 320.13 7.59 35 Table 5. Average data of the redissolution points for each composition (3/5). Volumetric ratio of organic solvents x CO2 x acetone x isopropanol T [K] P[MPa] 0.7927 0.1556 0.0518 324.88 7.98 0.7927 0.1556 0.0518 328.85 8.43 0.9052 0.0711 0.0237 310.63 7.24 0.9052 0.0711 0.0237 314.78 7.70 0.9052 0.0711 0.0237 319.40 8.20 0.9052 0.0711 0.0237 323.82 8.81 0.9052 0.0711 0.0237 329.80 9.69 0.9181 0.0614 0.0204 308.88 7.41 0.9181 0.0614 0.0204 317.38 8.26 0.9181 0.0614 0.0204 320.42 8.61 0.9181 0.0614 0.0204 325.48 10.30 0.9181 0.0614 0.0204 329.55 11.51 0.9512 0.0366 0.0122 309.83 7.77 0.9512 0.0366 0.0122 315.53 8.71 0.9512 0.0366 0.0122 320.60 9.77 3:1 Acet/Isopr. 0.9512 0.0366 0.0122 325.33 10.30 0.9512 0.0366 0.0122 331.10 10.90 0.9570 0.0323 0.0107 309.83 7.86 0.9570 0.0323 0.0107 315.50 8.83 0.9570 0.0323 0.0107 322.00 10.10 0.9570 0.0323 0.0107 324.13 10.21 0.9570 0.0323 0.0107 329.70 11.03 0.9775 0.0169 0.0056 309.75 7.38 0.9775 0.0169 0.0056 314.68 7.87 0.9775 0.0169 0.0056 320.02 9.19 0.9775 0.0169 0.0056 326.05 10.79 0.9775 0.0169 0.0056 328.68 12.39 0.9810 0.0143 0.0047 311.58 7.79 0.9810 0.0143 0.0047 315.50 8.11 0.9810 0.0143 0.0047 322.43 9.64 0.9810 0.0143 0.0047 323.65 11.18 0.9810 0.0143 0.0047 328.75 12.79 0.9988 0.0009 0.0003 309.85 7.48 0.9988 0.0009 0.0003 313.98 7.88 0.9988 0.0009 0.0003 318.90 8.59 0.9988 0.0009 0.0003 323.68 9.05 0.9988 0.0009 0.0003 328.53 10.04 36 Table 5. Average data of the redissolution points for each composition (4/5) Volumetric ratio of organic solvents x CO2 x acetone x isopropanol T [K] P[MPa] 0.9988 0.0008 0.0003 308.93 7.39 0.9988 0.0008 0.0003 313.68 7.91 3:1 Acet/Isopr. 0.9988 0.0008 0.0003 318.88 8.32 0.9988 0.0008 0.0003 323.38 9.04 0.9988 0.0008 0.0003 329.12 9.87 0.7129 0.2401 0.0471 308.95 5.42 0.7129 0.2401 0.0471 315.55 6.57 0.7129 0.2401 0.0471 319.58 6.58 0.7129 0.2401 0.0471 323.88 6.83 0.7129 0.2401 0.0471 330.05 8.19 0.7499 0.2091 0.0410 309.72 5.62 0.7499 0.2091 0.0410 315.05 7.07 0.7499 0.2091 0.0410 320.38 8.20 0.7499 0.2091 0.0410 325.02 7.52 0.7499 0.2091 0.0410 329.75 8.04 0.8961 0.0877 0.0162 308.68 6.77 0.8961 0.0877 0.0162 314.72 7.67 5:1 Acet/Isopr. 0.8961 0.0877 0.0162 320.38 9.12 0.8961 0.0877 0.0162 324.08 10.09 0.8961 0.0877 0.0162 329.70 10.48 0.9100 0.0760 0.0140 308.72 6.91 0.9100 0.0760 0.0140 314.55 7.79 0.9100 0.0760 0.0140 319.58 8.28 0.9100 0.0760 0.0140 323.85 9.09 0.9100 0.0760 0.0140 329.05 10.65 0.9368 0.0534 0.0098 308.87 7.18 0.9368 0.0534 0.0098 314.08 7.90 0.9368 0.0534 0.0098 319.55 8.81 0.9368 0.0534 0.0098 324.33 9.71 0.9368 0.0534 0.0098 329.35 10.70 0.9454 0.0461 0.0085 310.20 8.89 0.9454 0.0461 0.0085 315.75 8.89 0.9454 0.0461 0.0085 319.13 9.17 0.9454 0.0461 0.0085 324.15 9.95 0.9454 0.0461 0.0085 329.70 10.76 0.9759 0.0203 0.0037 309.08 7.58 0.9759 0.0203 0.0037 316.18 8.56 37 Table 5. Average data of the redissolution points for each composition (5/5) Volumetric ratio of organic solvents x CO2 x acetone x isopropanol T [K] P[MPa] 0.9759 0.0203 0.0037 319.55 9.06 0.9759 0.0203 0.0037 324.55 10.33 0.9759 0.0203 0.0037 328.63 11.16 0.9796 0.0172 0.0032 312.13 8.34 0.9796 0.0172 0.0032 314.68 8.73 0.9796 0.0172 0.0032 319.33 9.41 0.9796 0.0172 0.0032 324.18 11.21 0.9796 0.0172 0.0032 329.83 11.95 0.9938 0.0050 0.0011 310.08 7.45 0.9938 0.0050 0.0011 314.40 8.14 0.9938 0.0050 0.0011 318.38 8.61 0.9938 0.0050 0.0011 323.55 9.04 0.9938 0.0050 0.0011 328.60 9.62 An analysis of these data reveals some of the following characteristics of the system. Firstly, from the raw data, the redissolution points have a higher pressure than the opacity points observed at the same composition and temperature. The difference is usually about 0.3 or 0.4 MPa, although for a 3:1 ratio of acetone/isopropanol this difference is increased especially at higher temperatures. The most likely reason for the difference is visual observation itself. It is useful to plot the P-T diagrams for each of the ratios to see this and other aspects. Thus, in Figure 7, the temperature range of the study is plotted on the horizontal axis and the measured pressure data on the vertical axis. Each of the colours represents the carbon dioxide composition in mole fraction. The volumetric ratio of acetone and isopropanol is 1:1. The opacity points are represented by circles while the redissolution points are represented by triangles. The diagram shows how the pressure characterising phase transition increases as the temperature inside the cell increases. On the other hand, it is true that, up to a carbon dioxide mole fraction of approx. 0.8930, when more carbon dioxide is added to the cell, the measured pressures are higher, although this tendency does not always occur, especially when comparing some of the redissolution points. For example, for a mole fraction of 0.8930 CO2 (shown in red) the pressure of the dissolution points at approx. 330 K is higher than for mole fractions higher than this. 38 CO2 mole fraction Colour legend 0.7118 0.7491 0.8930 0.9351 0.9444 0.9798 0.9822 0.9939 0.9948 Figure 7. P-T diagram for a 1:1 volumetric acetone/isopropanol ratio. The same representation is carried out for a 3:1 ratio and is shown in Figure 8. The pressures reached for this ratio are higher than for the 1:1 ratio and this effect is particularly noticeable at temperatures above 320 K. The trend is still linear even though the data is more scattered and more disordered, probably due to possible errors in experimentation such as the inherent error of visual observation or the difficult visual assessment of turbidity and clarity at high temperatures. 39 CO2 mole fraction Colour legend 0.7668 0.7926 0.9052 0.9181 0.9512 0.9570 0.9752 0.9810 0.9987 0.9988 Figure 8. P-T diagram for a 3:1 volumetric acetone/isopropanol ratio. For the 5:1 acetone/isopropanol volumetric ratio, the P-T diagram is shown in Figure 9. Again, there is a linear trend in the cloud point and redissolution point pressures with temperature. 46 Figure 14. Quadratic surface graph for 3:1 volumetric ratio of acetone/isopropanol. Figure 15. Quadratic surface graph for 5:1 volumetric ratio of acetone/isopropanol. 47 The fitted, simple equations may be used to approximate the pressure needed to form a homogeneous mixture knowing the desired composition and temperature of the system. 4.2. Thermodynamic modelling The Aspen Plus V.10 © programme is used to model the behaviour of the phase data measured in this work. The aim of the Aspen data processing is to find the binary interaction parameters between the pairs of components by regressing the experimentally obtained data. Although in this work we have a ternary mixture, often, the parameters obtained from binary mixtures can be adapted to ternary systems unaltered or by adding some modification. Table 1 in the literature section 2.4 presents the main parameters found for the carbon dioxide + organic solvent systems. Comparison of these parameters can be carried out depending on whether the EOS and mixing rule used in the modelling are the same as that found in the literature. In case this does not happen, the BIPs reported in those articles cannot be combined but have to be re-adjusted with the simple mixing rules used by the modelling EOS. It should also be noted that the new BIPs obtained in the modelling of the ternary system do not necessarily describe the behaviour of the binary mixtures and should therefore be checked for their proper use. The thermodynamic models used in this work are Peng-Robinson (PR) and Soave- Redlich-Kwong (SRK) cubic equations of state. The PR EOS has been used in many articles with supercritical CO2 and acetone/isopropanol (Bamberger and Maurer 2000; Wu et al. 2004; Lei et al. 2012) and where it was concluded that this equation correlated correctly with the experimental data. SRK EOS has also been used for this purpose (Radosz 1986). Both equations are therefore models that correctly describe the behaviour of multicomponent systems, although their origin was focused on the study of hydrocarbon systems. The aim of this paper is to see whether the correlation with these two EOS is also able to describe the system appropriately. The Peng-Robinson cubic equation of state (Peng-Robinson 1976) is defined as: 𝑃= 𝑅𝑇 𝑉𝑚−𝑏−𝑎 𝑉𝑚(𝑉𝑚+𝑏)+𝑏(𝑉𝑚−𝑏) [ 10.] The pressure 𝑃 (Pa = J/m3) is determined from the absolute temperature 𝑇 (K) and molar volume 𝑉𝑚 (m3/mol). 𝑅 is the universal gas constant (J/mol K). The attraction parameter a (J∙m3 /mol2) is a function of the temperature of the mixture (𝑇), the critical 48 temperature (𝑇𝑐), the critical pressure (𝑃𝑐), and the acentric factor (𝜔) of the compounds. Parameter b (van der Waals covolume) (m3 /mol) is a function of the critical temperature and critical pressure of the compounds in the mixture. The Aspen programme uses this function based on the parameters presented below: 𝑎=∑∑𝑥𝑖𝑥𝑗 𝑗𝑖 (𝑎𝑖𝑎𝑗)0.5(1−𝑘𝑖𝑗) [ 11.] The temperature dependence of the parameter a can be expressed based on the parameter 𝑘𝑖𝑗 as follows: 𝑘𝑖𝑗 =𝑘𝑖𝑗 (1)+𝑘𝑖𝑗 (2)𝑇+𝑘𝑖𝑗 (3) 𝑇 [ 12.] The dependence of a on temperature is given not only by the interaction parameter 𝑘𝑖𝑗: 𝑎𝑖=𝑓(𝑇,𝑇𝑐𝑖,𝑃𝑐𝑖,𝜔𝑖) [ 13.] 𝑎𝑖(𝑇)=𝑎(𝑇𝑐)∙𝛼(𝑇𝑟,𝜔) [ 14.] The particularised parameter a at the critical point can be expressed as: 𝑎𝑖(𝑇𝑐)=0.45724𝑅2𝑇𝑐2 𝑃𝑐 [ 15.] In equation [ 14.] the parameter α depends on the reduced temperature ( 𝑇𝑟) and a characteristic constant for each compound (𝜅): 𝛼1/2 =1+𝜅(1−𝑇𝑟1/2) [ 16.] The reduced temperature 𝑇𝑟 is calculated as: 𝑇𝑟=𝑇 𝑇𝑐 [ 17.] Where 𝜅 is a constant characteristic of each compound: 𝜅=0.37464+1.54226𝜔−0.26992𝜔2 [ 18.] In equation [18.], the letter 𝜔 denotes the acentric factor of the component i. The parameter b depends on the composition as follows: 𝑏=∑𝑥𝑖𝑏𝑖 𝑖 [ 19.] 49 The temperature dependence of b can be expressed as follows: 𝑏𝑖=𝑓(𝑇𝑐𝑖,𝑃𝑐𝑖) [ 20.] 𝑏(𝑇)=𝑏(𝑇𝑐) [ 21.] The particularised parameter b at the critical point can be expressed as: 𝑏(𝑇𝑐)=0.07780𝑅𝑇𝑐 𝑃𝑐 [ 22.] It can be seen from these equations how the critical parameters of each of the compounds are basic for the modelling of the mixture. In this sense, it is worth comparing the data used by Aspen by default with some literary reviews where these parameters have also been obtained (Table 8). Although some differences can be discovered among the values obtained from different sources, these can be considered minor, and the default values of Aspen Plus may be accepted and used in the calculations. Table 8. Critical properties and acentric factor for pure compounds. Compound Tc [K] Pc[bar] ω Reference 508.30 47.63 0.665 (Khoiroh and Lee 2011) 2-propanol 508.31 47.64 0.669 (Dell’Era et al. 2007) 508.30 47.62 0.665 (Poling et al. 2001) 508.26 47.50 0.664 Default Value Aspen Plus V.10 2-propanone 508.10 47.00 0.305 (Bamberger and Maurer 2000) 508.10 47.00 - (Melhem et al. 1989) 508.06 47.05 0.308 Default Value Aspen Plus V.10 CO2 304.20 73.90 0.213 (Bamberger and Maurer 2000) 304.13 73.77 0.224 (Span and Wagner 1996) 304.16 73.81 0.225 Default Value Aspen Plus V.10 The binary interaction parameters between the pairs of components that serve as the basis for the regression are sought in the literature. In the found papers, the interaction parameters were only present in binary systems formed from the component pairs discussed in this work. The binary parameters found using the PR EOS and classical mixing rules of this equation as in this modelling, reported a carbon dioxide-acetone 50 interaction parameter -0.03 (Wu et al. 2004) and 0.007 (Lei et al. 2012). The carbon dioxide-isopropanol parameter was 0.128. (Galicia-Luna y Elizalde-Solis 2010). No literature data were found for the interaction between acetone and isopropanol. Other articles using modified PR as the equation of state and the Panagiotopoulos and Reid mixing rule reported a CO2/acetone interaction parameter of -0.0251 and CO2 /isopropanol of 0.1467 (Bamberger and Maurer 2000). As the mixing rule applied in the current study is different, these values cannot be directly used in the current simulation. The binary interaction parameters should be re-fitted to the already existing measurement data of the other authors. However, such values may need to undergo modification to be used to appropriately describe the behaviour of the ternary system. In Aspen, this binary interaction is defined as PRKBV-1. On the other hand, it should be noted that symmetrical parameters between the pairs have been considered as in Lei et al. 2012 and Wu et al. 2004 . In Bamberger and Maurer 2000 were not considered symmetrical, with the parameters acetone/CO2 of -0.0008 and isopropanol/CO2 of 0.1005. As the amount of measured phase equilibrium data is significant, I decided to use the regression function of the process simulator to determine binary interaction parameters and extend their applicability through including the temperature-dependence using their second and third elements (see equation [12.]). The process followed to carry out the regression takes as initial values the binary parameters found in the literature (Wu et al. 2004; Galicia-Luna and Elizalde-Solis 2010) takes as upper and lower limits +10 and -10 respectively. The objective function is rotated with the Maximum-Likelihood estimation and the algorithm of minimization is the Britte- Luecke algorithm. The problem with cubic equations of state is that they do not correctly describe those points that are close to the critical point of the mixture. Near the mixture critical point, the estimation of the molar volume becomes inaccurate. Together with the measurement error, this may result in the incapability of solving the phase equilibrium problem. In this case, this occurs when the CO2 mole fraction takes values higher than approximately 0.95. In practical terms, the simulation programme presents errors and warnings when these points are included, and the residual error values obtained are also higher. For this reason, only compositions that avoid such problems (<0.95) were included in the data to be regressed. As a result, a filtering of the experimental data was carried out, reducing the number of points included in the programme to around 300, which corresponds to the range of compositions. The opacity and redissolution data were treated together, although 51 the difference between them cause the root mean square error (RMSE) of the regression to increase. 𝑅𝑀𝑆𝐸=[∑(𝑥𝑒𝑥𝑝−𝑥𝑒𝑠𝑡)2𝑁 ⁄ 𝑁 𝑖=1 ]1/2 [ 23.] Where: (𝑥𝑒𝑥𝑝−𝑥𝑒𝑠𝑡)2 is the quadratic difference between the measured value and the estimated value in the regression and 𝑁 the total number of values in the regression. Table 9 shows the results obtained for the regression with PR when the temperature dependence is not considered (𝑘𝑖𝑗 =𝑘𝑖𝑗 (1)): Table 9. Binary parameters obtained from the regression with Aspen. Peng Robinson equation of state. CO2-Acet. CO2-Isopr. Acet.-Isopr. RMSE 𝑘𝑖𝑗 (1) -0.0214 -0.1001 -0.2460 12.78 The P-T diagram was constructed for one of the studied compositions to see if there is a real difference between considering or not considering BIPs. The composition for which this analysis is carried out is x_CO2 = 0.7491; x_acet = 0.1285; x_isoprop = 0.1224. Figure 16 and Figure 17 show the differences of fit obtained for the data studied. Figure 16. P-T diagram for the PR model with the BIPs (non-temperature dependant). x_CO2 = 0.7491; x_acet = 0.1285; x_isoprop = 0.1224. BIPs CO2/Acetone: -0.0214; CO2/Isopropanol: -0.1001; Isopropanol/Acetone: -0.2460 The circles represent the opacity points while the crosses represent the redissolution points. 52 Figure 17. P-T diagram for the PR model with the BIPs=0. x_CO2 = 0.7491; x_acet = 0.1285; x_isoprop = 0.1224. By adding the values for the interaction parameters, the fit with respect to the PR model is more correct than for the case where regression was carried out setting all BIPs to 0. The next step is to add the temperature dependence to the regression to see whether it influences the fit. The dependence of the parameter 𝑘𝑖𝑗 on temperature is shown in [ 13.]. There are now 9 parameters to be regressed, which are 𝑘𝑖𝑗 (1), 𝑘𝑖𝑗 (2)and 𝑘𝑖𝑗 (3) corresponding to each pair of compounds. The process carried out is the same, however, it is observed that the parameter 𝑘𝑖𝑗 (2) has a much smaller influence on 𝑘𝑖𝑗, than the parameter 𝑘𝑖𝑗 (3) and the limits assigned to the regression have an influence. The regression is tested with upper and lower bounds of +10 and -10 and +1000 and -1000, since both results offer values that could be acceptable. However, it is observed that when the limits are lower, the parameter 𝑘𝑖𝑗 obtained by assigning different temperatures is more similar to the parameter obtained without temperature dependence and that they are closer to the previously mentioned literary values. The values with a range at the lower limits are therefore chosen (Table 10). Table 10. Binary parameters for Peng Robinson's model. Temperature dependence. CO2-Acet. CO2-Isopr. Acet.-Isopr. RMSE 𝑘𝑖𝑗 (1) 0.1922 0.3671 -0.4159 12.46 𝑘𝑖𝑗 (2)[1/K] -0.0008 -0.0015 0.0006 𝑘𝑖𝑗 (3)[K] 10 3.4470 -8.2567 53 A check can be made at this point to see how the variation of the parameter 𝑘𝑖𝑗 is for each pair of compounds with respect to the temperature range applied in this study (Table 11). The parameters obtained for the CO2-acetone interaction are certainly close to the literature value of -0.03 (Wu et al. 2004) while the same is not true for the CO2- isopropanol interaction (0.128) (Galicia-Luna and Elizalde-Solis 2010). Table 11. Dependence of binary parameters on temperature for the PR model. Temperature (K) 𝑘𝐶𝑂2−𝑎𝑐𝑒𝑡. 𝑘𝐶𝑂2−𝑖𝑠𝑜𝑝𝑟. 𝑘𝑎𝑐𝑒𝑡−𝑖𝑠𝑜𝑝𝑟. 303.15 -0.0124 -0.0849 -0.4431 308.15 -0.0130 -0.0928 -0.4427 313.15 -0.0135 -0.1006 -0.4422 318.15 -0.0140 -0.1084 -0.4418 323.15 -0.0145 -0.1162 -0.4414 328.15 -0.0149 -0.1240 -0.4410 Visually, the P-T-diagram is performed again to see if there is a temperature dependence in the fitting (Figure 18). Figure 18. P-T diagram for the PR model with the BIPs (temperature dependant). x_CO2 = 0.7491; x_acet = 0.1285; x_isoprop = 0.1224. Compared to Figure 16, it appears that the new fit better models the points at a higher temperature for the composition under study. Figure 19 shows how the model behaves when using the BIPs obtained from the regression with +1000 and -1000 bounds. It can be seen that the modelling does not correctly describe the behaviour of the system over the whole range of temperatures and pressures at a given composition. 54 Figure 19. P-T diagram for the PR model with BIPs obtained when limits are relaxed (temperature dependant). x_CO2 = 0.7491; x_acet = 0.1285; x_isoprop = 0.1224. An identical process is followed for the SRK thermodynamic model (Soave 1971) which is defined as follows: 𝑃= 𝑅𝑇 𝑉𝑚−𝑏−𝑎 𝑉𝑚(𝑉𝑚+𝑏) [ 24.] The parameter 𝑎 is defined differently this time than in the case of Peng Robinson: 𝑎=∑ ∑ 𝑥𝑖𝑥𝑗 𝑛 𝑗=1 𝑛 𝑖=1 √𝑎𝑖𝑎𝑗(1−𝑘𝑖𝑗) [ 25.] The particularised parameter a at the critical point can be expressed as: 𝑎𝑖(𝑇𝑐)=0.42747𝑅2𝑇𝑐2 𝑃𝑐 [ 26.] Equations [ 14.] and [ 16.] concerning the α parameter are the same as PR EOS while 𝜅 changes: 𝜅=0.480+1.574𝜔−0.176𝜔2 [ 27.] The parameter b depends on the composition as follows: 𝑏=∑𝑥𝑖𝑏𝑖 𝑖 [ 28.] 55 The particularised parameter b at the critical point changes and can be expressed as: 𝑏(𝑇𝑐)=0.08664𝑅𝑇𝑐 𝑃𝑐 [ 29.] The binary interaction parameters 𝑘𝑖𝑗 for this model are defined as SRKKIJ-1. No binary parameters for this thermodynamic model were found in the literature that could serve as a reference for the regression, so a value of 0 was used as initial guess. The parameter 𝑘𝑖𝑗 obtained from the regression for the SRK thermodynamic model are shown in Table 12: Table 12. Binary parameters obtained from the regression with Aspen. SRK model. CO2-Acet. CO2-Isopr. Acet.-Isopr. RMSE 𝑘𝑖𝑗 (1) 0.0352 0.0638 0.0178 16.64 When the regression is done with the temperature dependence for the parameter 𝑘𝑖𝑗, it is observed that the second term has no influence again. The resulting fit is worse than for the PR case with the RMSE being higher. However, at the graphical level, no noticeable differences with Figure 18 are observed, so it has not been included. For this case, no influence on the variation of the limits for the third term is denoted. The binary parameters of temperature dependence are shown in Table 13. Table 13. Binary parameters obtained from the regression with Aspen. SRK model. Temperature dependence. CO2-Acet. CO2-Isopr. Acet.-Isopr. RMSE 𝑘𝑖𝑗 (1) 0.0037 0.0234 0.0026 16.42 𝑘𝑖𝑗 (2) 0 0 0 𝑘𝑖𝑗 (3) 3.3638 6.9543 -0.0113 Table 14 shows the result of the binary parameters for the studied temperature range and for the SRK model. Table 14. Dependence of binary parameters on temperature for the SRK model. Temperature (K) 𝑘𝐶𝑂2−𝑎𝑐𝑒𝑡. 𝑘𝐶𝑂2−𝑖𝑠𝑜𝑝𝑟. 𝑘𝑎𝑐𝑒𝑡−𝑖𝑠𝑜𝑝𝑟. 303.15 0.0154 -0.0849 -0.4431 308.15 0.0152 -0.0928 -0.4427 313.15 0.0151 -0.1006 -0.4422 318.15 0.0149 -0.1084 -0.4418 323.15 0.0147 -0.1162 -0.4414 328.15 0.0146 -0.1240 -0.4410 62 6. Literature bibliography AIDA, T., AIZAWA, T., KANAKUBO, M. and NANJO, H., 2010. Dependence of volume expansion on alkyl chain length and the existence of branched methyl group of CO2-expanded ketone systems at 40°C. The Journal of Supercritical Fluids, vol. 55, no. 1, pp. 71-76. ISSN 08968446. DOI 10.1016/j.supflu.2010.05.025. ANASTAS, P. and EGHBALI, N., 2010. Green Chemistry: Principles and Practice. Chem. Soc. 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YAGINUMA, R., NAKAJIMA, T., TANAKA, H. and KATO, M., 1997. Densities of Carbon Dioxide + 2-Propanol at 313.15 K and Pressures to 9.8 MPa. Journal of Chemical & Engineering Data, vol. 42, no. 4, pp. 814-816. ISSN 0021-9568, 1520-5134. DOI 10.1021/je9700028. YEO, S.-D. and KIRAN, E., 2005. Formation of polymer particles with supercritical fluids: A review. The Journal of Supercritical Fluids, vol. 34, no. 3, pp. 287- 308. ISSN 08968446. DOI 10.1016/j.supflu.2004.10.006. 68 7. Appendix 7.1. Molar density of CO2 in the applied temperature range. Table 15. Molar densities of CO2 from NIST. Tª (ºC) density (mol/L) 26 20.667 25.9 20.678 25.8 20.688 25.7 20.699 25.6 20.721 25.4 20.731 25.3 20.742 25.2 20.752 25.1 20.763 25 20.773 24.9 20.784 24.8 20.794 24.7 20.805 24.6 20.816 24.5 20.826 24.4 20.837 24.3 20.847 24.2 20.858 24.1 20.868 24 20.879 23.9 20.889 23.8 20.900 7.2. Laboratory measurements. Opacity Points. Table 16. Laboratory raw data. Opacity points. Bold Type: Data included in Aspen. 1:1 Acet/Isop. 3:1 Acet/Isop. 5:1 Acet/Isop. x_CO2 T[K] P[MPa] x_CO2 T[K] P[MPa] x_CO2 T[K] P[MPa] 0.7118 305.65 5.25 0.7668 307.75 5.88 0.7129 307.65 4.91 0.7118 306.95 5.33 0.7668 308.15 5.38 0.7129 307.15 5.35 0.7118 307.65 5.37 0.7668 308.05 5.83 0.7129 308.05 4.87 0.7118 312.15 5.69 0.7668 306.45 5.52 0.7129 308.95 5 0.7118 312.05 5.74 0.7668 312.25 5.82 0.7129 310.75 5.73 0.7118 312.15 5.7 0.7668 311.85 6.18 0.7129 312.75 5.93 0.7118 316.45 5.99 0.7668 312.35 6.06 0.7129 313.85 5.48 0.7118 317.95 6.04 0.7668 312.15 5.93 0.7129 317.15 5.92 69 Table 16. Laboratory raw data. Opacity points. Bold Type: Data included in Aspen (2/5) 0.7118 317.45 5.95 0.7668 316.55 6.85 0.7129 318.65 5.92 0.7118 322.85 6.92 0.7668 316.55 7.52 0.7129 317.15 5.85 0.7118 322.55 6.91 0.7668 315.85 6.51 0.7129 322.35 6.13 0.7118 321.85 6.86 0.7668 315.95 6.75 0.7129 322.95 6.26 0.7118 324.55 7.05 0.7668 318.55 6.71 0.7129 322.25 6.47 0.7118 327.75 7.11 0.7668 322.15 7.04 0.7129 322.35 6.16 0.7118 326.65 7.26 0.7668 323.25 7.02 0.7129 327.25 7.04 0.7491 308.45 5.97 0.7668 322.05 6.99 0.7129 325.95 6.97 0.7491 308.65 5.46 0.7668 325.45 7.95 0.7129 326.65 7.18 0.7491 306.65 5.8 0.7668 327.75 7.6 0.7499 307.35 5.36 0.7491 305.95 5.55 0.7668 327.75 7.7 0.7499 306.25 5.19 0.7491 309.35 5.79 0.7668 327.65 7.83 0.7499 308.95 5.36 0.7491 311.45 5.6 0.7927 308.25 5.57 0.7499 311.25 6.16 0.7491 312.85 6.28 0.7927 307.95 5.7 0.7499 310.35 5.99 0.7491 311.55 6.01 0.7927 307.25 5.55 0.7499 310.75 5.84 0.7491 317.35 6.44 0.7927 309.95 5.86 0.7499 313.75 6.37 0.7491 318.05 6.99 0.7927 311.55 6.59 0.7499 313.25 6.55 0.7491 316.45 6.54 0.7927 311.95 6.77 0.7499 313.55 6.32 0.7491 315.45 6.85 0.7927 310.75 6.86 0.7499 323.05 6.82 0.7491 319.35 6.83 0.7927 313.65 6.94 0.7499 323.45 7.02 0.7491 321.75 7.13 0.7927 315.95 6.6 0.7499 323.45 6.94 0.7491 322.65 6.97 0.7927 317.65 7.35 0.7499 326.75 7.47 0.7491 322.75 6.9 0.7927 317.15 6.81 0.7499 327.75 7.39 0.7491 327.35 7.59 0.7927 317.75 7.34 0.7499 327.95 7.18 0.7491 327.95 7.7 0.7927 323.15 7.1 0.8961 306.85 6.68 0.7491 328.05 7.53 0.7927 322.45 7.08 0.8961 307.65 6.51 0.7491 327.65 7.55 0.7927 313.85 7.55 0.8961 307.85 6.47 0.8930 307.85 7.01 0.7927 323.05 7.21 0.8961 312.55 7.29 0.8930 307.95 6.88 0.7927 327.65 7.86 0.8961 312.25 7.2 0.8930 307.75 6.79 0.7927 328.85 7.92 0.8961 313.05 7.27 0.8930 312.45 7.49 0.7927 328.45 7.66 0.8961 316.65 8.15 0.8930 313.35 7.4 0.7927 327.35 7.84 0.8961 315.45 7.91 0.8930 312.45 7.61 0.9052 306.45 6.73 0.8961 316.95 7.89 0.8930 318.15 8.03 0.9052 305.25 6.7 0.8961 322.15 8.8 0.8930 317.65 7.95 0.9052 307.95 6.88 0.8961 322.65 8.99 0.8930 316.85 7.94 0.9052 308.05 6.73 0.8961 322.65 8.86 0.8930 322.45 9.1 0.9052 312.15 7.27 0.8961 328.15 9.7 0.8930 323.05 9.09 0.9052 312.35 7.26 0.8961 326.45 9.36 0.8930 322.95 9.01 0.9052 312.55 7.27 0.8961 327.35 9.18 0.8930 327.55 9.5 0.9052 313.15 7.12 0.8961 327.25 9.15 0.8930 327.25 9.67 0.9052 317.85 7.87 0.9100 307.65 6.7 0.8930 327.05 9.77 0.9052 318.05 7.82 0.9100 308.05 6.77 0.9351 308.05 7.09 0.9052 317.55 7.79 0.9100 307.95 6.67 0.9351 308.05 6.99 0.9052 317.95 7.82 0.9100 312.05 7.45 0.9351 307.85 7.36 0.9052 322.95 8.56 0.9100 313.25 7.59 0.9351 311.85 7.82 0.9052 321.75 8.47 0.9100 313.85 7.47 70 Table 16. Laboratory raw data. Opacity points. Bold Type: Data included in Aspen (3/5) 0.9351 312.55 7.99 0.9052 321.95 8.45 0.9100 316.35 8.09 0.9351 312.65 7.81 0.9052 326.55 9.12 0.9100 317.45 7.94 0.9351 316.35 8.27 0.9052 328.15 9.38 0.9100 317.25 7.92 0.9351 317.35 8.71 0.9052 327.85 9.05 0.9100 322.75 8.64 0.9351 317.55 8.13 0.9052 327.75 9.32 0.9100 322.65 8.75 0.9351 321.95 8.9 0.9181 306.45 6.68 0.9100 322.55 8.61 0.9351 322.65 8.87 0.9181 307.45 6.78 0.9100 327.35 9.73 0.9351 322.75 8.9 0.9181 307.95 6.86 0.9100 327.65 9.56 0.9351 327.75 9.72 0.9181 312.05 7.44 0.9100 327.35 9.55 0.9351 328.05 9.61 0.9181 311.45 7.28 0.9100 327.85 9.99 0.9351 327.65 9.93 0.9181 311.15 7.32 0.9368 306.45 7.04 0.9444 306.85 7.3 0.9181 317.65 8.01 0.9368 307.55 7.05 0.9444 307.45 7.31 0.9181 318.25 8.17 0.9368 307.45 7.06 0.9444 307.55 7.07 0.9181 317.65 8.07 0.9368 307.35 6.89 0.9444 311.15 7.61 0.9181 322.05 9 0.9368 307.95 7.01 0.9444 312.55 7.73 0.9181 322.35 9.02 0.9368 309.95 7.48 0.9444 311.25 7.53 0.9181 325.75 8.81 0.9368 312.05 7.84 0.9444 317.15 8.37 0.9181 327.05 10.7 0.9368 312.95 7.61 0.9444 319.05 8.79 0.9181 327.05 10.32 0.9368 313.05 7.88 0.9444 318.15 8.51 0.9181 327.15 10.1 0.9368 318.05 8.23 0.9444 322.35 8.99 0.9512 307.85 7.25 0.9368 317.95 8.4 0.9444 323.35 9.29 0.9512 307.55 7.25 0.9368 316.55 7.99 0.9444 322.65 8.89 0.9512 308.05 7.31 0.9368 317.55 8.15 0.9444 326.65 9.48 0.9512 306.95 7.19 0.9368 322.55 9.23 0.9444 328.65 9.56 0.9512 311.55 7.91 0.9368 322.65 9.36 0.9444 328.65 9.48 0.9512 312.55 7.72 0.9368 322.75 9.09 0.9798 306.35 7.13 0.9512 312.05 7.52 0.9368 322.55 8.96 0.9798 307.45 7.13 0.9512 310.15 7.77 0.9368 326.75 10.14 0.9798 307.35 7.17 0.9512 317.35 8.32 0.9368 326.35 10.59 0.9798 307.15 7.24 0.9512 314.75 8.38 0.9368 327.55 10.36 0.9798 311.65 7.66 0.9512 315.55 8.32 0.9368 327.95 10.5 0.9798 311.75 7.66 0.9512 316.25 8.51 0.9454 305.75 7.1 0.9798 311.75 7.69 0.9512 320.15 9.22 0.9454 308.45 7.25 0.9798 312.05 7.65 0.9512 321.05 9.11 0.9454 305.95 7.05 0.9798 317.05 8.29 0.9512 322.15 9.42 0.9454 307.65 7.04 0.9798 317.05 8.29 0.9512 322.35 9.23 0.9454 309.05 7.49 0.9798 317.35 8.33 0.9512 327.95 9.92 0.9454 312.65 7.49 0.9798 317.25 8.28 0.9512 327.35 10.22 0.9454 312.55 7.67 0.9798 322.45 8.88 0.9512 327.65 9.78 0.9454 311.55 7.71 0.9798 322.85 8.82 0.9512 328.35 10.29 0.9454 317.45 8.45 0.9798 321.05 8.72 0.9570 306.45 7.28 0.9454 316.65 8.51 0.9798 322.85 9.01 0.9570 307.75 7.49 0.9454 317.65 8.65 0.9798 327.25 9.6 0.9570 307.85 7.42 0.9454 317.35 8.66 0.9798 327.85 9.63 0.9570 307.35 7.31 0.9454 321.55 9.36 0.9798 327.15 9.33 0.9570 311.05 7.78 0.9454 321.65 8.96 0.9798 328.25 9.47 0.9570 312.15 7.89 0.9454 321.95 9.19 0.9822 306.65 7.15 0.9570 310.65 7.83 0.9454 321.85 8.86 71 Table 16. Laboratory raw data. Opacity points. Bold Type: Data included in Aspen (4/5) 0.9822 307.75 7.16 0.9570 310.25 7.71 0.9454 327.65 10.52 0.9822 307.95 7.26 0.9570 316.45 8.52 0.9454 326.95 10.07 0.9822 307.15 7.21 0.9570 316.95 8.42 0.9454 327.75 10.04 0.9822 309.95 7.6 0.9570 316.45 8.67 0.9454 328.35 10.3 0.9822 312.95 7.52 0.9570 314.25 8.31 0.9759 306.05 7.13 0.9822 313.15 7.64 0.9570 320.15 9.42 0.9759 307.85 7.34 0.9822 313.65 7.85 0.9570 320.35 9.51 0.9759 307.45 7.33 0.9822 316.85 8.07 0.9570 320.75 9.47 0.9759 308.25 7.48 0.9822 317.85 8.27 0.9570 320.95 9.48 0.9759 310.65 7.84 0.9822 318.15 8.31 0.9570 325.55 10.18 0.9759 311.65 7.71 0.9822 317.85 8.41 0.9570 326.35 10.39 0.9759 313.05 7.86 0.9822 322.65 8.93 0.9570 325.75 10.18 0.9759 312.25 7.78 0.9822 322.75 9.05 0.9570 326.55 10.09 0.9759 316.25 8.35 0.9822 322.45 8.98 0.9775 307.65 7.13 0.9759 316.75 8.43 0.9822 322.55 8.89 0.9775 308.75 7.24 0.9759 316.95 8.56 0.9822 327.05 9.91 0.9775 308.95 7.3 0.9759 317.45 8.67 0.9822 327.55 9.75 0.9775 308.25 7.18 0.9759 322.55 9.56 0.9822 328.35 9.79 0.9775 312.25 7.62 0.9759 322.05 9.55 0.9822 328.05 9.89 0.9775 313.05 7.76 0.9759 322.45 9.63 0.9939 306.85 7.18 0.9775 312.15 7.79 0.9759 323.45 9.5 0.9939 305.75 7.12 0.9775 313.05 8.18 0.9759 327.15 10.27 0.9939 306.95 7.14 0.9775 315.75 8.21 0.9759 327.95 10.34 0.9939 306.25 7.09 0.9775 316.65 8.15 0.9759 327.45 10.41 0.9939 311.85 7.56 0.9775 317.25 8.39 0.9759 327.05 10.48 0.9939 311.55 7.5 0.9775 321.35 9.6 0.9796 306.05 7.09 0.9939 312.35 7.47 0.9775 322.25 8.99 0.9796 308.05 7.41 0.9939 312.05 7.42 0.9775 322.45 9.89 0.9796 307.85 7.32 0.9939 317.35 8.15 0.9775 322.45 9.58 0.9796 307.75 7.33 0.9939 317.75 8.2 0.9775 328.05 11.7 0.9796 310.75 7.8 0.9939 317.05 8.15 0.9775 327.95 11.14 0.9796 311.85 8.26 0.9939 317.05 8.27 0.9775 327.95 11.47 0.9796 312.25 8 0.9939 321.75 8.74 0.9775 327.35 10.98 0.9796 312.45 8.06 0.9939 322.75 8.58 0.9810 306.85 7.4 0.9796 317.55 9.15 0.9939 322.85 8.67 0.9810 308.25 7.64 0.9796 317.65 9.06 0.9939 322.65 8.75 0.9810 306.65 7.2 0.9796 318.85 9 0.9939 326.55 9.48 0.9810 309.25 7.6 0.9796 318.45 8.92 0.9939 327.05 9.37 0.9810 312.45 7.77 0.9796 322.85 10.18 0.9939 327.75 9.35 0.9810 314.15 8.15 0.9796 322.05 9.97 0.9939 326.95 9.23 0.9810 312.75 8.09 0.9796 321.05 9.93 0.9948 305.35 7.09 0.9810 312.35 8.21 0.9796 321.95 9.98 0.9948 306.25 7.19 0.9810 317.15 8.34 0.9796 326.85 11.05 0.9948 307.15 7.21 0.9810 317.75 8.82 0.9796 327.55 10.84 0.9948 306.85 7.13 0.9810 317.25 8.17 0.9796 326.85 11.26 0.9948 311.35 7.61 0.9810 316.35 8.88 0.9796 327.75 10.58 0.9948 312.65 7.62 0.9810 322.15 9.72 0.9938 306.45 7.25 0.9948 312.35 7.74 0.9810 322.95 9.95 0.9938 307.35 7.22 0.9948 312.75 7.58 0.9810 322.45 10.19 0.9938 307.85 7.36