Prediction of thermodynamic properties of Water/ Phenol mixtures

In most of the processes which are carried out in the chemical industry, the desired purity of the products is not reached during the reaction process. Therefore, technical separation methods are extremely important in order to reach high-quality products in acceptable yields at low costs. Conventional methods of separating mixtures like distillation, extraction, ad- or absorption as well as crystallization processes and combinations of these methods are well established together with alternative methods like membrane- based separations and others.

 

In particular, the removal of phenol is of great interest in waste-water treatment. With a global production of 8 million tons each year in 2001, phenol is one of the most important intermediates in chemical industry. Phenol contaminated effluents arise, for example, during the production processes of bisphenol A, phenol formaldehyde resins, and the Hock process. The average concentration of phenol in these streams has been evaluated to be between 3 wt% and 10 wt% depending on temperature. Increasing prices for phenol as a raw material makes it also worthwhile to recycle the valuable product from the stream[1].

 

Computational methods, such as Monte Carlo simulations and thermodynamic models can assist in predicting the thermodynamic properties of industrially used chemicals, like for example phenol and its mixture with water. This helps focus the required experiments, and becomes an alternative if poisonous chemical are involved in the process.

 

Monte Carlo simulations in the isobaric- isothermal ensemble allow the direct calculation of properties and their temperature and pressure derivatives from the the ensemble partition function of the molecular system, dependent on the number of particles, the pressure and the temperature. Details and physical foundations are given in[2 ].
Properties calculated and used for further fitting in EOS model can for example be the Vapor- Liquid Equilibrium diagrams for mixtures. Additionally derivative properties can be calculated at selected points.

 

Equations of State(EOS) models have been developed and applied for for many problems in the chemical engineering arena . The generic advantage of EoS calculations over atomistic simulation are the reduced calculation time and lesser compute power demands. Hence EoS calculations are preferred if there is a need for generating a large amount of data over a wide pressure, temperature and composition range.
Higher order EoS have evolved in recent years as a powerful tool for thermodynamic property calculations. In this study the Statistical Associated Fluid Theory (SAFT) and its widely used modification, perturbed chain-SAFT (PC-SAFT)[3,4]are used.

 

Monte Carlo simulations and PC- SAFT have the ability to work together in a powerful manner, since the PC- Saft interaction parameter kij can be regressed based on phase diagram data from Monte Carlo simulations, and so only a few data data points from MC together with a refined PC- SAFT prediction can cover the entire phase space with high accuracy.

 

For this case study the industrial relevant application of a phenol/ water mixture has been chosen.

 

The Monte Carlo simulations were performed using Scienomics' Amorphous Builder and the Towhee plugin, the PC- SAFT calculations used Scienomics SciTherm module.

 

The pure component simulations MC simulations were performed on Phenol using the canonical ensemble and a TraPPE based force field[5,6], the critical temperature was calculated using the density scaling law fit and the boiling point determined from the Clausius Clapeyron equation with the calculated saturated vapor pressures.

 

Figure 1. displays the VLE curve of pure Phenol at various temperatures at atmospheric pressure.

 

c5-f1-vle-pure-phenol
Figure 1: VLE diagram of pure phenol

The critical Temperature was determined with 714 K (exp. 695 K), the boiling point was calculated as 466 K(exp. 456 K).

 

The mixture simulations were performed in the isobaric- isothermal ensemble combining the TraPPE based Phenol force field combined with the TIPS4P2005[7] water force field.
In order to identify the appropriate region for the Monte Carlo simulations, first PC- SAFT calculations without a binary interaction parameter were performed. Based on that information, two data points were simulated and the binary interaction parameter was regressed for a final PC- SAFT iteration.

 

Figure 2. shows the binary mixture in the liquid phase at 420 K, Figure 3. shows the binary phase diagram for the Phenol/ Water mixture including experimental data and the Monte Carlo data points.

 

c5-f2-phenol-water-atomistic
Figure 2. Phenol/ Water liquid phase at 420 K. Water is displayed as CPK, Phenol as ball and stick model

c5-f3-vle-and-lle-phenol-water
Figure 3. Phenol/ Water Vapor-Liquid equilibrium and Liquid-Liquid equilibrium curves

The results show a clear improvement of the PC- SAFT results after fitting of the binary interaction parameter to the Monte Carlo data points. The most dramatic impact of the fitted interaction parameter can be seen in the Liquid-liquid curve before and after fitting, even though the fit was done by using only VLE data.

 

Summary:
A combination of Monte Carlo simulations and thermodynamic equation of state was used to predict the phase equilibrium of the industrially important Phenol/ Water mixture. A clear improvement of the predicted data could be seen after regression of the PC- SAFT interaction parameter. This data is important to know in the chemical engineering process of waste water treatment and the recycling process of Phenol after chemical reactions.

 

References:

  1. Mixa, A. and Staudt, C.: Int. J.Chem. Eng.Volume 2008, 319392(2008)
  2. Allen, M. P., and D. J. Tildsley. 1989. Computer simulation of liquids. Oxford University Press, Oxford
  3. Chapman, W. G.; Gubbins, K. E.; Jackson, G.: Radosz, M. Ind. Eng. Chem. Res. 29, 1709–1721(1990)
  4. Gross, J. and Sadowski, G.: Ind. Eng. Chem. Res. 40, 1244–1260(2001)
  5. Wick, C. D., Martin, M. And Siepman, J. I: J. Phys. Chem. B 104, 8008-8016(2000)
  6. Stubbs, J. M., Potoff, J.J. And Siepman, J.I.: J. Phys. Chem. B 108, 17569-17605(2004)
  7. J. Abascal, and C. Vega, C.: J. Chem. Phys. 123, 234505(2005)