Predicting Phase Equilibria of Oxygenated Compounds Using Molecular Models Results from the MEMOBIOL Project.

Transcription

1 Renewable energies Eco-friendly production Innovative transport Eco-efficient processes Sustainable resources Predicting Phase Equilibria of Oxygenated Compounds Using Molecular Models Results from the MEMOBIOL Project. Rafael LUGO, on behalf of the MEMOBIOL Partners

2 Agenda Context General description of the project Duration, partners, Objectives Deliverables, organization Definition of the targets What models? What molecules? What properties? Main results WP1 : COSMO models WP2 : MC Molecular Simulation WP3 : GC-PPC-SAFT EOS WP4 : Experimental data 2

3 Introduction (1) : Second and third generation biofuels Clarck,

4 Facts on oxygen-bearing compounds within the context ob biomass valorization Process modeling & simulation require knowing phase equilibria (e.g. separation) Large amount of molecules Large variety of oxygenated molecular families Scarce / poor data available Experiments might be expensive, unsafe, unfeasible Predictive models are needed 4

5 The MEMOBIOL Project MEMOBIOL : Molecular-based (predictive) Models for Lignocellulosic Bioreffineries 42 months project (october 2009 march 2013) granted by the French National Resarch Agency 3 academic partners, 3 industrial partners Partners IFP Energies nouvelles (leader) CET-TEP (Armines) Ensta-Paristech LSPM Materials Design ProSim 5

6 Deliverables of MEMOBIOL STI deliverables (reports, publications, conferences) Evaluation of the ability of existing predictive models to reproduce the thermodynamics of complex oxygenated molecules and their mixtures Development of new predictive models and new fitting strategies Databases Exhaustive inventory of the available experimental data for high-value oxygenated molecules and their mixtures New / original experimental data for high-value oxygenated compounds New molecular parameters / descriptions / validated implementation for existing predictive models Softwares New release of Simulis Thermodynamics from ProSim, providing the new PPC-SAFT EOS New release of MedeA-Gibbs with new functionalities 6

7 What predictive models? (1) COSMO-RS, COSMO-SAC COnductor-like Screening MOdel 7

8 What predictive models? (2) Molecular Simulation methods Use of Gibbs Ensemble Monte Carlo for phase equilibria (Panagiotopoulos, Mol. Phys., 1987) with AUA and UA force fields Use of either Monte Carlo or molecular dynamics (LAMMPS) for phase properties (density, energy, derivative properties) Description of molecules using the Anisotropic United Atom (AUA), TraPPE-UA and PCFF force fields Extended coverage, large applicability Good compromise between accuracy and computing time 8

9 What predictive models? (3) "New" equations of state GC-PPC-SAFT EOS 9

10 What molecules? - Pure - In binary / ternary solutions with Solvents ( water, toluene, hexane, octanol ) 10

11 What properties, what phenomena? 11

12 12 Organization & WP's (1) Méthodes COSMO MC Molecular Simulation GC-PPC-SAFT EOS WP1 WP2 WP3 Existing Data Inventory Development of models & methods Industrialization Screening, parameters for ge models (e.g. ProSim) MedeaGibbs WP5 Simulis Thermodynami WP6 cs Measurements WP4 Research Industrialization Industrial case studies Project management WP0

13 ) 2) Organization & WP's (2) 13 Ensta-Paristech ProSim A i p()(a 2 ) Sigma Profiles Measurements Water C o s m o- S A C C o s m o- R S Octanol e/ 4 Paramétrage UNIQUAC, NRTL ( COSMO Theory Paramétrage, validation m Interfaçage (UAS, CAPE-OPEN, ) A A m. Armines LIMHP, Ensta-Paristech, IFPEN a /k a hs a disp a assoc a chain NkT GC-PPC-SAFT EOS MC Molecular Simulation Paramétrage, validation IFP Energies nouvelles Algorithmes, paramétrage de champs de force Process simulation in Silico Experiments MedeA-Gibbs (Materials Design)

14 Results from WP1 : COSMO models COSMO-RS has been used to predict Log P coefficients for different oxygen-bearing compounds System CAS Experimental (or estimated) values COSMO-RS Number 43, 44, 45 Log P Refs. BP-TZVP Fumaric acid Itaconic acid (EST.) b Levulinic acid Glycerol Sorbitol Furfural Anisole m-cresol Vanillin Quinone AAD c R d Refs. F. Dongfu et al. J. Chem. Eng. Data, 56, 1323 (2011) J. Li and P. Paricaud, Energy & Fuels, submitted (2012) 14

15 Results from WP2 : Molecular Simulation Familiy AUA4 TraPPE-UA Alcools (dev. on P sat > 25 %) Diols shorts :, longs : P (kpa) Propanoic Acide propanoïque + pentanoic acid + acide at pentanoïque K Ketones (dev. on P sat > 25 %) Aldehydes (dev. on P sat > 25 %) Glycerol (dev. on ρ ~5 %, dev. on P sat ~ 3 %) Ethers (dev. on P sat > 25 %) Glycols (dev. on ρ ~2. 5 %, dev. on P sat ~ 15 %) (specific term required, dev. on P sat > 25 % ) Esters Furans (MEMOBIOL) (has been done with TraPPE-EH ) Carboxylic (MEMOBIOL) (dev. on P sat > 25 %) acids T (K) Fraction molaire en acide propanoïque Mole fraction of propanoic acid 350 Furane + n -Hexane atm Fraction Mole fraction molaire of en furane

16 WP2 - Validation of force fields implementation for pure components and gas solubility in alcohols Henry solubility constants of various gases in ethanol High pressure phase diagram of hydrogen and m-cresol at K AUA: Boiling Point Temperature average deviation <1.7% TraPPE-UA : Boiling Point Temperature average deviations <5.3% NB : Simulation results (lines) obtained from biased test insertions in 2 days on a local PC cluster with MedeA -GIBBS 16

17 WP2 Implementaion of the Thermodynamic Integration algorithm in the MC Gibbs code and evaluation Thermodynamic integration is used: To predict solvation / hydration energies To predict solubilities and inifinite dilution states To predict partition coefficients Can be used to predict K OW λ = 0 Ne solute-solvent interaction Ghyd (kj/mol) Apolar solutes λ = λ i Λ i -weighted solutesolvent interaction Hydration Gibbs Energy... Experimental λ = 1 Full solute-solvent interaction Ce travail (AUA4/TIP4P2005 ; TI,MC) TraPPE-UA / MSPCE (TI,MD) TraPPE-UA/TIP4P (GEMC) GROMOS-AA/MSPCE (TI,MD) Methane Ethane Propane Hexane Methanol Ethanol Acetone Acétaldéhyde IFPEN Polar solutes 17

18 Results from WP3 : GC-PPC-SAFT Previous work 1-alkanes i-alcanes 1-alcohols3,4-alcoho esters formates ethers ketones aldehydeskyl-benzen acides ycloalkane alcenes yaromatiq H2 CH4 CO2 methano ethanol water H2S N2 1-alkanes 1 i-alcanes alcohols a variety of oxygen-bearing aromatic compunds (phenolics, furanics, ) 2,3,4-alcohols esters formates ethers ketones aldehydes alkyl-benzenes acides 3 3 cycloalkanes alcenes polyaromatiques 8 8 H CH CO methanol ethanol water H2S N E+04 6.E+04 4.E+04 2.E+04 0.E x,y 3-methylphenol Water + butane 3-methylphenol + decane P (Pa) K K K 1 de Hemptinne, OGST Mourah, NguyenHuynh, rapport 2 4 NguyenHuynh, IECR NguyenHuynh, FPE NguyenHuynh, thèse (2008) 7 NguyenHuynh, FPE 2008 (segment approach 8 Tran, E&F, Mourah, thèse (2009) 10 NguyenHuynh, IECR 2008, part 1 11 NguyenHuynh, IECR 2008, part II 12 NguyenHuynh, FPE (2008) heavy ester

19 Results from WP3 : GC-PPC-SAFT Renormalization of dipole moment: µ ' E µ p 1 N Polarizability effects E A µ ' pol T (K) Alcohol-alkanes P C2oh-c4 C2oh-c5 C2oh-c6* C2oh-c7* C4oh-c5 C4oh-c E-02 1.E-01 x, y phenol 1.E+00 Water-phenol : requires two BIP s 19

20 Results from WP4 : New experimental data VLE (ebulliometry) : 2 binary systems Furan + hexane and furan + toluene LLE (cell + sampling) : 3 binary systems Furfural + hexane, furan + water, eugenol + water Densities (vibrating tube): Furan hexane, furan toluene, eugenol hexane, eugenol 1 octanol, etc.. Henry s law constant (low pressure): Solute Furan Solvents: water, ethanol, toluene, 1 octanol, hexane, butanol, hexanol, isopropanol Next system ELL: Furfural + eugenol + hexane 20

21 When experiments meet predictions 345 temperature (K) COSMO-SAC VT2005 COSMO-RS MC GC-PPC-SAFT Experimental data (WP4) mole fraction of furan 21

22 Aknowledgements We gratefully acknowledge the ANR for support of this work though grant ANR-09-CP2D MEMOBIOL This work was granted access to the HPC resources of CCRT/CINES under the allocation 2011-X made by GENCI (Grand Equipement National de Calcul Intensif) 22

23 Renewable energies Eco-friendly production Innovative transport Eco-efficient processes Sustainable resources