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COSMO-RS and process simulation: from physical properties to environmental impact … Maurizio Fermeglia & Sabrina Pricl MOSE Lab, Department of Chemical.

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Presentation on theme: "COSMO-RS and process simulation: from physical properties to environmental impact … Maurizio Fermeglia & Sabrina Pricl MOSE Lab, Department of Chemical."— Presentation transcript:

1 COSMO-RS and process simulation: from physical properties to environmental impact … Maurizio Fermeglia & Sabrina Pricl MOSE Lab, Department of Chemical Engineering, University of Trieste, Italy mose.units.it

2 COSMO-RS and process simulation: from physical properties to environmental impact … and beyond. Maurizio Fermeglia & Sabrina Pricl MOSE Lab, Department of Chemical Engineering, University of Trieste, Italy mose.units.it

3 Bergisches Land, 12 June, slide 3Cosmologic Symposium 2009 Outline of talk Introduction Multiscale Molecular Modeling Applications EOS parameter estimation for pure components and mixtures Physical properties calculations for alternative refrigerants Prediction of solvent effect in enzymatic reactions Prediction of toxicological data to be used in process simulation Direct calculation of mesoscale parameters (DPD and Mesodyn) Conclusions

4 Bergisches Land, 12 June, slide 4Cosmologic Symposium 2009 Meccanica Quantistica (elettroni ) Meccanica molecolare (atomi) Modellazione di mesoscala (insiemi di atomi o molecole) Simulazione di processo FEM Engineering design 1Å Characteristic Length 1nm 1μm 1mm1m years seconds nanoseconds picoseconds femtoseconds Quantum Mechanics (electrons ) Molecular Mechanics (atoms) Mesoscale modeling (segments) Process Simulation FEM Engineering design 1Å Characteristic Time 1nm 1μm 1mm1m hours minutes microseconds Multiscale Molecular Modeling Message passing multiscale modeling Reverse mapping

5 Bergisches Land, 12 June, slide 5Cosmologic Symposium 2009 Outline of talk Introduction Multiscale Molecular Modeling Applications EOS parameter estimation for pure components and mixtures Physical properties calculations for alternative refrigerants Prediction of solvent effect in enzymatic reactions Prediction of toxicological data to be used in process simulation Direct calculation of mesoscale parameters (DPD and Mesodyn) Conclusions

6 Bergisches Land, 12 June, slide 6Cosmologic Symposium 2009 Molecular Modeling (MM) DFT/COSMO RS Calculations Molecular Volume and Surfaces Vapor Pressure & Activity coeff. Estimation EOS Parameters (PHSCT) pure & mixture Thermophysical Properties Structural and Physico-chemical Data Prediction of EOS parameters M. Fermeglia, S. Pricl, AIChE Journal 47: (2001)

7 Bergisches Land, 12 June, slide 7Cosmologic Symposium 2009 A* = r 2 N A From molecular area A V* = ( /6) r 3 N A From molecular volume V E* = r ( /k) R g From A*, V* and E k /E p From A*, V* and P 0 3 parameters: r, a and b 3 parameters:, /k and r recasting PHSCT EOS: pure components

8 Bergisches Land, 12 June, slide 8Cosmologic Symposium 2009 The PHSCT EOS – mixtures

9 Bergisches Land, 12 June, slide 9Cosmologic Symposium 2009 Connolly dot surface algorithm corrected for quantum effects Molecular volumes and surfaces of HCCs and HCFCs Molecular volumes and surfaces of CH 4 as reference A*, V* Simulation Protocol Pure Components Mixtures Gibbs Energy calculation using COSMO RS for binary mixtures.. estimation of binary k ij M. Fermeglia, S. Pricl, AIChE Journal 47: (2001)

10 Bergisches Land, 12 June, slide 10Cosmologic Symposium 2009 Chloro – Fluoro Hydrocarbons Pure compounds C3H8 (R290) C3H2F6 (R236fa) C3H2F6 (R236ea) CH2F2 (R32) C2HF5 (R125) C3H3F5 (R245fa) C2H2F4 (R134a) C3OF8 (E218 ) C2SF6 C2OH6 (RE170) Mixtures

11 Bergisches Land, 12 June, slide 11Cosmologic Symposium 2009 R20 CHCl 3 R40 CH 3 Cl R143a C 2 H 3 F 3 R114 C 2 Cl 2 F 4 R125 C 2 HF 5 R13 CClF 3 Pure component prediction

12 Bergisches Land, 12 June, slide 12Cosmologic Symposium 2009 Fermeglia and Pricl, Fluid Phase Equilibria, 158, 49-58, 1999 Fermeglia and Pricl, Fluid Phase Equilibria, 166, 21-37, 1999 Refrigerants: VLE and PVT predictions

13 Bergisches Land, 12 June, slide 13Cosmologic Symposium EOS predictions – mixtures M. Fermeglia, S. Pricl, AIChE Journal 47: (2001)

14 Bergisches Land, 12 June, slide 14Cosmologic Symposium EOS predictions – mixtures M. Fermeglia, S. Pricl, AIChE Journal 47: (2001)

15 Bergisches Land, 12 June, slide 15Cosmologic Symposium 2009 Results for mixtures SystemT [K]RMSD PvRMSD Y hfc32-hfc134a hfc32-hfc236ea hfc32-hfc236fa hfc125-hfc134a hfc125-hfc236ea hfc125-hfc236fa hfc125-hfc r290-hfc236fa r290-hfc AVERAGE

16 Bergisches Land, 12 June, slide 16Cosmologic Symposium 2009 Process simulation The EOS is applied to simulate the process Production of R 134a

17 Bergisches Land, 12 June, slide 17Cosmologic Symposium 2009 Outline of talk Introduction Multiscale Molecular Modeling Applications EOS parameter estimation for pure components and mixtures Physical properties calculations for alternative refrigerants Prediction of solvent effect in enzymatic reactions Prediction of toxicological data to be used in process simulation Direct calculation of mesoscale parameters (DPD and Mesodyn) Conclusions

18 Bergisches Land, 12 June, slide 18Cosmologic Symposium 2009 Sustainability evaluation (of a process)… Optimize the process at design time Cleaner production no end of pipes Process simulator Toxicological data prediction from MM From Martins, 2006 Society EcologyEconomy Sustainable Development Fermeglia M., Longo G., Toma L., AIChE J (2009)

19 Bergisches Land, 12 June, slide 19Cosmologic Symposium 2009 PSP framework Collaboration with

20 Bergisches Land, 12 June, slide 20Cosmologic Symposium D Process Simulators CAPE OPEN (CoLan) Toxicological data Process Design Molecular Modeling 1D Indexes PSP Framework PSP framework & Indexes

21 Bergisches Land, 12 June, slide 21Cosmologic Symposium 2009 Molecular modeling Fermeglia M., Longo G., Toma L., AIChE J (2009)

22 Bergisches Land, 12 June, slide 22Cosmologic Symposium 2009 Octanol-water partition coefficient Kow is used to calculate different properties Soil/sediment adsorption coefficients Bioconcentration Human Toxicity Potential Terrestrial Toxicity Potential Kow Aquatic Toxicity Potential Ingestion Inhalation Dermal Exposure Fermeglia M., Longo G., Toma L., Env.Progress (2008)

23 Bergisches Land, 12 June, slide 23Cosmologic Symposium 2009 SubstancelogKow (exp) logKow calculatedlogKow RAD QSARCOSMO RS QSARCOSMO RS Methanol Naphthalene Phenol Chloroform Toluene Anisole Methyl Ethyl Chloride Benzene Methane Ethane Propane Benzoic Acid Kow-Results The relative absolute deviation (RAD) between the experimental and the predicted method (QSAR e COSMO RS) has been calculated The error has lower values for when Kow is calculated using COSMO RS

24 Bergisches Land, 12 June, slide 24Cosmologic Symposium 2009 Thermo- physical property Available MM methods Used MM methods Results Octanol Water partition coefficient FEP+MC+MDQSAR % FEC+EEM MC QSAR CSMCOSMO RS % Life time SAR % Reaction rate with Ozone SAR 0-50% Reaction rate with hydroxyl GCM % ab-initio QSAR %

25 Bergisches Land, 12 June, slide 25Cosmologic Symposium 2009 Processes developed and analyzed with PSP Framework Acrylic Acid production process Sweetening natural gas by DGA absorption Formaldehyde production process Phthalic Anhydride production process Maleic Anhydride Production process Dimethylether production process Dimethylformamide production process R134a production process Fuel Ethanol production from sugar cane molasses (Cuba) Multiple Effect of Sugar Cane Juice (Mexico) Electroplating wastewater discharge process (Russia)

26 Bergisches Land, 12 June, slide 26Cosmologic Symposium 2009 R-134a Production Process Case2 Case3 Base Case Fermeglia M., Longo G., Toma L., AIChE J (2009)

27 Bergisches Land, 12 June, slide 27Cosmologic Symposium D Results: production of R-134a A) Material Intensity B) Energy Intensity C) Potential Chemical Risk D) Potential Environmental Impact A B CD

28 Bergisches Land, 12 June, slide 28Cosmologic Symposium D Results: production of R-134a A) Iout B) Iout /product mass C) Igen D) Igen/product mass Note: the energy production process is considered AB C D

29 Bergisches Land, 12 June, slide 29Cosmologic Symposium 2009 Outline of talk Introduction Multiscale Molecular Modeling Applications EOS parameter estimation for pure components and mixtures Physical properties calculations for alternative refrigerants Prediction of solvent effect in enzymatic reactions Prediction of toxicological data to be used in process simulation Direct calculation of mesoscale parameters (DPD and Mesodyn) Conclusions

30 Bergisches Land, 12 June, slide 30Cosmologic Symposium 2009 Polymer blend PC - ABS Rear lamp of the FIAT Idea Recycling of industrial scraps PC/ABS blends are promising for rear lamps PC: transparency and good mechanical properties ABS: reflecting properties, lower viscosity Side viewFront view

31 Bergisches Land, 12 June, slide 31Cosmologic Symposium 2009 Mesoscopic Dynamics (MesoDyn) real system = ideal system + an effective external potential + non ideal term polymer chain is the fundamental building block of the model The ideal term is described by non interacting Gaussian chains it allows a factorization of the interactions hence is computationally very efficient The non – ideal term takes into account interchain, i.e., non-bonded, interactions Interchain reactions The molecular ensemble is represented by n Gaussian chains, made up of m beads of types J with a total number of N beads per chain Fraaije, J.G.E, et al., J. Chem. Phys. 106(10), 1997

32 Bergisches Land, 12 June, slide 32Cosmologic Symposium 2009 Mesoscopic Dynamics (MesoDyn) Free energy equation = ideal + external potential + non ideal The nonideal term (interaction among chains) where ε IJ (r-r) is a cohesive interaction between beads I and J (Gaussian form) F-H χ

33 Bergisches Land, 12 June, slide 33Cosmologic Symposium 2009 Input parameters for MesoDyn The most important parameters for MesoDyn are 1. the bead size and Gaussian chain architecture 2. the bead mobility M, 3. the effective Flory-Huggins interactions ij Obtained by molecular modeling tools: bead size and Gaussian chain architecture: by Molecular Dynamics from characteristic ratio (C ) in terms of Kuhn length mobility: by Molecular Dynamics Bead self diffusion coefficients FH interactions: by COSMO - RS Bead – bead interactions Gibbs Energy with COSMO RS for binary mixtures of molecules containing same groups of beads.. estimation of binary X ij Fermeglia M., Cosoli P., Ferrone M., Piccarolo S., Mensitieri G., Pricl S. Polymer 47: (2006)

34 Bergisches Land, 12 June, slide 34Cosmologic Symposium 2009 PC and ABS modeling assumptions Modeling at mesoscale level ABS is modeled by three different structures Styrene Acrylonitrile block copolymer (SAN) Butadiene homopolymer (POLYB) A branched macromolecule SAN- POLYB-SAN (backbone butadiene) PC is modeled by two different fragments Need of constant bead dimensions A B S ( PM ) S A N POLYB POLYB- SAN ABS blend structure Polycarbonate; PC monomer was split in 2 fragments in simulations

35 Bergisches Land, 12 June, slide 35Cosmologic Symposium 2009 Mesoscale results Segregation occurs for all the systems investigated Morphology: islands of AN (blue) are surrounded by S (cyan) S enwraps the AN segments S shields PC from unfavorable interactions with the polar AN SAN engulfs the B (purple) domains Shear effect (elongated phases) AN S B PC B AN S

36 Bergisches Land, 12 June, slide 36Cosmologic Symposium 2009 Morphology and phase behavior Validation of multiscale approach with TEM analysis PC/ABS simulations Mesodyn density field compared with TEM (S.Wang et. Al., 2002) Same morphology PC B AN S

37 Bergisches Land, 12 June, slide 37Cosmologic Symposium 2009 Dissipative Particle Dynamics Soft potentials calculations F i = f (a ii, a ij, …, r c ) Characteristic dimension of mesoscopic system Micro - FEM Simulation FEM Analysis: Macroscopic properties From beads … to micro: fixed grid Geometry: map cubes to Palmyra tetrahedrons Laplace equation is solved for electric conductance, diffusion and permeability Local deformation allow the calculation of mechanical properties

38 Bergisches Land, 12 June, slide 38Cosmologic Symposium 2009 FEM results: properties PC/ABS15/8535/6555/4585/15 Young modulus [GPa] Shear modulus [GPa]8.6· · ·10 -1 Bulk modulus [GPa] Thermal expansion coefficient [K -1 ]6.92· · · ·10 -5 Bayblend® T 85 (Bayer) 85/15 PC/ABS Young Modulus [Gpa] Thermal expansion coefficient [K -1 ] 7.5* *10 -5 Calculated material properties (top) and comparison with experimental data* (bottom) *

39 Bergisches Land, 12 June, slide 39Cosmologic Symposium 2009 Conclusions: the role of COSMO RS COSMO RS has a role in Multiscale Modelling Directly At QM level Calculate interaction energy for molecules and fragments AS a message generator To avoid long MD and MC calculations.. To mesoscale DPD and Mesodyn (FH parameter).. To process simulators (EOS parameters) Applications Phase equilibrium Physical properties for PS Toxicological data Polymer blends Diblock copolymers phase behavior

40 Bergisches Land, 12 June, slide 40Cosmologic Symposium 2009 Acknowledgements Personeel 2 Staf: Maurizio Fermeglia - Sabrina Pricl 4 research contracts Marco Ferrone -Andrea Metzgez - Maria Silvia Paneni – Radovan Toth 6 PHD students Letitia Toma - Paolo Cosoli – Giulio Scocchi – Paola Posocco - Giovanni Maria Pavan – Iztok Bajc Variable number of master students (3-5) Research Support: material science EU MoMo Project, VI FP grant on Molecular Modelling EU MULTIPRO Project,VI FP grant on hybrid material EU MULTIHYBRID IP Project VI FP on PCNs and CNT EU NanoModel Project VII FP on Multiscale Modeling Ministry of University, Italy, PRIN 2002, 03, 05, 06 SIPA Zoppas Ind. research grant Centro Ricerche Plast Optica – FIAT ALRI (Automotive Lighting Rear Lamp) Industrial collaborations: Solvay, Karton, Caffaro, Serichim Collaborations Martin Lisal, Academy of Science, Czech Republic Marek Maly, Purkinje University, Czech Republic Laura Martinelli, Sabino Sinesi, CRP FIAT Research Andrea Danani, SUPSI, Lugano, Switzerland Hans Fraije, Culgi, The NEtherland Fernando DelaVega, Cimananotech, Israel Loredana Incarnato, Giovanna Russo, University of Salerno), Italy Peppe Mensitieri, University of Naples, Italy Stefano Piccarolo, University of Palermo, Italy Gianluca Cicala, University of Catania, Italy Emo Chiellini, University of Pisa, Italy Lucia Comper, Andrea Morellato, Zoppas Ind., Italy ……


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