COSMO-RS and process simulation: from physical properties to environmental impact … Maurizio...

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

[email protected]

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

[email protected]

Bergisches Land, 11 April 2023 - slide 4Cosmologic Symposium 2009

MeccanicaQuantistica(elettroni)

Meccanicamolecolar

e(atomi)

Modellazionedi

mesoscala(insiemi di

atomi o molecole)

Simulazione di

processo

FEM

Engineering design

1Å

Characteristic Length

1nm 1μm 1mm 1m

years

seconds

nanoseconds

picoseconds

femtoseconds

QuantumMechanics(electrons)

MolecularMechanics

(atoms)

Mesoscale modeling

(segments)

Process Simulation

FEM

Engineering design

1Å

Characteristic Time

1nm 1μm 1mm 1m

hours

minutes

microseconds

Multiscale Molecular Modeling

Message passing multiscale modeling

Reverse mapping


Outline of talkIntroduction


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


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: 2371-2382 (2001)


A* = r 2NA

From molecular area A

V* = (/6) r 3NA

From molecular volume V

E* = r (/k) Rg

From A*, V* and Ek/Ep

From A*, V* and P0

3 parameters: r, a and b

3 parameters: , /k and r

recasting

PHSCT EOS: pure components

111 2

Re

dgrdgbrkT

P

f


The PHSCT EOS – mixtures

m

ijijji

rm

iiiiiiijijijj

m

jiir

r

akT

dgdgbkT

P)(

11)(1

,

ij

bijijij

kTFb 3

3

2

2

σσσ ji

ij

ijjiij k 1εεε

ij

aijijij

kTFa 3

3

2


Connolly dot surface algorithm corrected for quantum effects

Molecular volumes and surfaces of HCCs and HCFCs

Molecular volumes and surfaces of CH4 as reference

A*, V*

Simulation ProtocolPure Components

Mixtures Gibbs Energy calculation using COSMO RS for binary

mixtures .. estimation of binary kij



Chloro – Fluoro HydrocarbonsPure compounds C3H8 (R290) C3H2F6 (R236fa) C3H2F6 (R236ea) CH2F2 (R32) C2HF5 (R125) C3H3F5 (R245fa) C2H2F4 (R134a) C3OF8 (E218 ) C2SF6 C2OH6 (RE170)

Mixtures R32-R134a @293,343K R32-R236ea @288,303,318K R32-R236fa @303,323K R32-R245 @303K R32-C2SF6 @250K R32-E218 @214,240K R32-RE170 @249K R125-R134a @283K R125-R236ea

@288,303,318K R125-R236fa @303,323K R125-R245 @298,313K R290-R236fa

@283,303,323K R290-R245 @298,313K


R20 CHCl3

R40 CH3Cl

R143a C2H3F3

0

5

10

15

20

25

30

35

40

100 200 300 400

T (K)

P0 (

ba

r) R114 C2Cl2F4

R125 C2HF5

R13 CClF3

Pure component prediction


CFHs PVT VLE P range

(bar) T range

(K) AD (%)

Range P0 (bar)

AD P0 (%)

AD VL (%)

R13 5 - 20 150-240 1.79 0.03 - 15.93 3.36 2.35 R14 6 - 20 110 - 200 1.93 0.07 - 15.50 1.71 2.27 R21 3 - 10 220 - 310 1.69 0.03 - 23.96 3.08 3.05 R22 5 - 20 250 - 340 1.59 1.77 - 19.82 4.62 1.16 R23 2 - 10 200 - 290 1.65 0.03 - 20.02 5.98 1.78 R32 12 - 98 253 - 333 3.74 3.47 - 8.64 4.33 2.66 R123 1.013 203 - 393 0.622 0.12 - 21.022 3.30 4.29 R134a 8 - 20 285 - 374 1.79 2.16 - 4.27 5.34 0.772 R142b 1.03 - 3.45 266 - 350 1.90 1.14 - 6.20 2.10 1.01 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


0

5

10

15

20

25

0 0.2 0.4 0.6 0.8 1

x,y

Vp

(bar

)

R125/[email protected]@303.2K@318K

0

5

10

15

0 0.2 0.4 0.6 0.8 1

x,y

Vp

(bar

)

R290/R245@[email protected]

EOS predictions – mixtures



R32/R236fa@303K@323K

0

5

10

15

20

25

30

35

0 0.2 0.4 0.6 0.8 1

x,y

Vp

(bar

)

EOS predictions – mixtures

0

0.5

1

1.5

2

2.53

3.5

4

4.5

5

0 0.2 0.4 0.6 0.8 1

x,y

Vp

(bar

)


R32/C2SF6@250K


Results for mixtures

System T [K] RMSD Pv RMSD Y

hfc32-hfc134a 293 3.7 0.043

343 7.8 0.055

hfc32-hfc236ea 288 2.3 0.011

303 5.4 0.003

318 7.9 0.009

hfc32-hfc236fa 303 5.6 0.003

323 8.6 0.01

hfc125-hfc134a 283 0.8 0.014

hfc125-hfc236ea 288 0.6 0.009

303 1.1 0.009

318 2.7 0.015

hfc125-hfc236fa 303 1.3 0.009

323 3.6 0.018

hfc125-hfc245 298 0.7 0.006

313 1.3 0.01

r290-hfc236fa 283 1.7 0.011

303 3.1 0.018

323 7.5 0.047

r290-hfc245 298 5 0.016

313 17.1 0.103

AVERAGE 4.39 0.02095


Process simulationThe EOS is applied to simulate the process Production of R 134a

R-1P-1

E-1

M-1

V-2

P-2

V1

SP-1

E-2

M-2E-3

E-4 C-1

S15IN

S16

S17

S2

S16IN

S3

S4

S6S7

S5

S8

S9 S9IN

S10 S11

S1

E-5

S12

C-2S13

S14

S15

S18

S19

S20

F-1

T-2

S20OUT

S21

E-6

S22

R-2

T-1




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


Sustainability evaluation (of a process)…

Optimize the process at design time

Cleaner production no end of pipes

Process simulatorToxicological data

prediction from MM

From Martins, 2006

SocietyEcologyEconomy

Sustainable DevelopmentSustainable DevelopmentSustainable DevelopmentSustainable Development

Economy

Environment

Society

3D

2D

2D

2D

1D 1D

1DFermeglia M., Longo G., Toma L., AIChE J (2009)


PSP frameworkCollaboration with

Process Simulator Database

CAPE OPEN Unit Operation

Modules

Data

Data regarding streams

Data regarding the substances involved in

the process

Types of streams Mass flow rate Substances

name Substances

CAS-Number

Risk Phrases for each

substance

Process input stream

Process output stream containing the main product

Process output stream containing

salable co-products

Process waste stream

PSPFramework

3D indicators 1D indicators

Material intensity

Energy intensity

Potential chemical risk

evaluation

Potential environmental impact

evaluation

Waste Reduction (WAR)

Algorithm

Sustainability evaluation

Additional data introduced on the interface


3D

Process Simulators

CAPE OPEN (CoLan)

Toxicological data

Process Design

Molecular Modeling

1D

Indexes

PSP Framework

PSP framework & Indexes

Environmental Impact

Categories

Global Atmospheric

Global Warming Potential(GWP)

Acidification Potential

(AP)

Ozone Depletion Potential(ODP)

Photochemical Oxidation Potential(PCOP)

Local Toxicological

Human Toxicity Potential by

Ingestion (HTPI)

Human Toxicity Potential by

Inhalation and Dermal Exposure

(HTPE)

Aquatic Toxicity Potential

(ATP)

Terrestrial Toxicity Potential

(TTP)


Molecular modeling

Fermeglia M., Longo G., Toma L., AIChE J (2009)

Environmental Impact categories

Thermo-physical properties used in the Environmental

Impact Indexes calculation

Molecular methods used for the

calculation of thermo-physical

properties

Software used for the calculation of thermo-physical

properties

GWP ODP PCOP AP HTPE HTPI TTP ATP

Radiative Efficiency

Lifetime of the compound in the

atmosphere

Reaction rate with ozone

Reaction rate with hydroxyl

Lethal dose

Lethal concentration

Qunatum Mechanics

SAR QSARMonte Carlo

CERIUS2MS

ModelingAOP

(by EPA)

Kow

PCLOGP

CosmoTherm

KOWWIN

ClogP

ECOSAR

ALOGPS

logKOW

TOPKAT

Release rate of

H+

AUTOLOG

MolecularDynamics


Octanol-water partition coefficient

Kow is used to calculate different properties

phaseaqueousinionconcentrat

phaseoctanolinionconcentratKow

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)


Substance

logKow(exp)

logKow calculated

logKow RAD

QSAR COSMO RS

QSAR COSMO RS

Methanol -0.77 -0.63 -0.73 0.1818 0.0529

Naphthalene

3.3 3.17 3.09 0.0393 0.0625

Phenol 1.46 1.51 1.42 -0.0342 0.0192

Chloroform 1.97 1.52 2.1 0.2284 -0.066

Toluene 2.73 2.54 2.65 0.0696 0.0275

Anisole 2.11 2.07 2.23 0.0189 -0.0555

Methyl Ethyl Chloride

1.25 1.34 1.26 -0.072 0.0073

Benzene 2.13 1.99 2.1 0.0657 0.0121

Methane 1.09 0.78 1.02 0.2844 0.0677

Ethane 1.81 1.32 1.63 0.2707 0.0984

Propane 2.36 1.81 2.18 0.2330 0.0077

Benzoic Acid

1.87 1.87 2.24 0 -0.1967

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

alexperiment

calculatedalexperiment

Kow

KowKowRAD

log

loglog


Thermo-physical property

Available MM

methods

Used MM methods

Results

Octanol Water partition coefficient

FEP+MC+MD QSAR 0-28.44%

FEC+EEM

MC

QSAR

CSM COSMO RS 0.73-9.84%

Life time SAR SAR 2.35-44.7%

Reaction rate with Ozone

SAR SAR 0-50%

Reaction rate with hydroxyl

GCM GCM 0.75-44.3%

ab-initio

QSAR QSAR 0-40.2%


Processes developed and analyzed with PSP Framework

Acrylic Acid production processSweetening natural gas by DGA absorptionFormaldehyde production processPhthalic Anhydride production processMaleic Anhydride Production processDimethylether production processDimethylformamide production processR134a production process

Process from the scientific literature

Chemical Plants situated in the developing countries

Fuel Ethanol production from sugar cane molasses (Cuba)

Multiple Effect of Sugar Cane Juice (Mexico)

Electroplating wastewater discharge process (Russia)


R-134a Production Process

R-1P-1

E-1

M-1

V-2

P-2

V1

SP-1

E-2

M-2E-3

E-4 C-1

S15IN

S16

S17

S2

S16IN

S3

S4

S6S7

S5

S8

S9 S9IN

S10S11

S1

E-5

S12

C-2S13

S14

S15

S18

S19

S20

F-1

F-2

S20OUT

S21

E-6

S22

R-2

T-1

Cooling water in

P-2

Water out

P-5

Case2

Case3 R-1P-1

E-1

M-1

V-2

P-2

V1

SP-1

E-2

M-2E-3

C-1

S15IN

S16

S17

S2

S16IN

S3

S4

S6S7

S5

S8

S9 S9IN

S10 S11

S1

E-5

S12

C-2S13

S14

S15

S18

S19

S20

F-1

F-2

S20OUT

S21

E-6

S22

R-2

T-1

Base CaseR-1P-1

E-1

M-1

V-2

P-2

V1

SP-1

E-2

M-2E-3

E-4 C-1

S15IN

S16

S17

S2

S16IN

S3

S4

S6S7

S5

S8

S9 S9IN

S10S11

S1

E-5

S12

C-2S13

S14

S15

S18

S19

S20

F-1

T-2

S20OUT

S21

E-6

S22

R-2

T-1

Fermeglia M., Longo G., Toma L., AIChE J (2009)


3D Results: production of R-134a

A) Material IntensityB) Energy IntensityC) Potential

Chemical RiskD) Potential

Environmental Impact

Environmental Results using 3D Indicators

0

100

200

300

400

500

600

700

800

1

cases

MI v

alu

es Case1

Case2

Case3


0

1

2

3

4

5

6

1

cases

EI v

alu

es Case1

Case2

Case3


0

200000

400000

600000

800000

1000000

1200000

1400000

1

cases

PC

R v

alu

es Case1

Case2

Case2

Environmantal Results using 3 d Indicators

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1

cases

PE

I val

ues Case1

Case2

Case3

A B

C D


3D Results: production of R-134a

A) IoutB) Iout /product massC) IgenD) Igen/product mass

Note: the energy production process is considered

Environmental Results using 1D Indicator

450

460

470

480

490

500

510

520

530

540

1

cases

Iou

t(P

EI/h

r) Case1

Case2

Case3

Environmental Results using 1D Indicator

0.215

0.22

0.225

0.23

0.235

0.24

0.245

0.25

0.255

0.26

1

cases

Iou

t_m

p(P

EI/k

g)

Case1

Case2

Case3

Environmantal Results using 1D Indicator

-10600

-10590

-10580

-10570

-10560

-10550

-10540

-10530

-10520

-10510

1

cases

Igen

(PE

I/hr) Case1

Case2

Case3

Environmantal Results using 1D Indicator

-5.12

-5.115

-5.11

-5.105

-5.1

-5.095

-5.09

-5.085

-5.08

-5.075

1

cases

Igen

_mp

(PE

I/kg

)

Case1

Case2

Case3

A B

C D




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


Polymer blend PC - ABS

Rear lamp of the FIAT Idea

Recycling of industrial scrapsPC/ABS blends are promising for rear lamps

PC: transparency and good mechanical properties ABS: reflecting properties, lower viscosity

Side view Front view


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 chainFraaije, J.G.E, et al., J. Chem. Phys. 106(10), 1997


Mesoscopic Dynamics (MesoDyn)Free energy equation = ideal + external potential + non ideal

The nonideal term (interaction among chains)

where εIJ(r-r‘) is a cohesive interaction between beads I and J (Gaussian form)

IJ V V

JIIJnid rdrdrrrrF ''

2

1

22

'2

323

20

2

3'

rra

ijij ea

rr

nid

III FrdrrUnnF

)()(!lnln 11

F-H χ


Input parameters for MesoDynThe most important parameters for MesoDyn are 1. the bead size and Gaussian chain architecture2. 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 Xij

Fermeglia M., Cosoli P., Ferrone M., Piccarolo S., Mensitieri G., Pricl S. Polymer 47:5979-5989 (2006)

0

10

20

30

40

50

60

70

80

90

100

0 50 100 150 200

N

Cin

f(%

)

MSD vs Time (LDPE)

y = 0,0388x + 3,2678

0

5

10

15

20

0 100 200 300 400

Time [ps]

MS

D [

A2]


PC and ABS modeling assumptions

Modeling at mesoscale levelABS 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 fragmentsNeed of constant bead dimensions

A B SA B S ( PM 30.000 )( PM 30.000 )

S A NS A N POLYBPOLYB POLYB-POLYB-SANSAN

ABS blend structure

Polycarbonate; PC monomer was split in 2 fragments in simulations


Mesoscale results

Segregation occurs for all the systems investigatedMorphology: islands of AN (blue) are surrounded by S (cyan)S enwraps the AN segmentsS shields PC from unfavorable interactions with the polar AN SAN engulfs the B (purple) domainsShear effect (elongated phases)

ANS

BPC

PC

B

AN

S


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


Dissipative Particle Dynamics

Soft potentials calculations

Fi = f (aii, aij, …, rc )

Characteristic dimension of mesoscopic system

Micro - FEM Simulation

FEM Analysis:

Macroscopic properties

From beads … to micro: fixed grid

Geometry: map cubes to Palmyra tetrahedronsLaplace equation is solved for electric conductance,diffusion and permeabilityLocal deformation allow the calculation of mechanicalproperties


FEM results: propertiesPC/ABS 15/8515/85 35/6535/65 55/4555/45 85/15

Young modulus [GPa] 2.40 2.39 2.35 2.34

Shear modulus [GPa] 8.6·10-1 8.5·10-1 8.3·10-1 8.3·10-1

Bulk modulus [GPa] 4.15 4.38 4.55 4.85

Thermal expansion coefficient [K-1]

6.92·10-

5

6.80·10-

5

6.72·10-5 6.56·10-

5

Bayblend® T 85 (Bayer)

85/15 PC/ABS

Young Modulus [Gpa] 2.30 2.34 Thermal expansion coefficient [K-1]

7.5*10-5 6.56*10-5

Calculated material properties (top) and comparison with experimental data* (bottom) * www.plastics.bayer.com


Conclusions: the role of COSMO RSCOSMO RS has a role in Multiscale ModellingDirectly 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


AcknowledgementsPersoneel

2 Staf: Maurizio Fermeglia - Sabrina Pricl

4 research contractsMarco Ferrone -Andrea Metzgez - Maria Silvia Paneni – Radovan Toth

6 PHD studentsLetitia 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 ……

COSMO-RS and process simulation: from physical properties to environmental impact … Maurizio...

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Transcript of COSMO-RS and process simulation: from physical properties to environmental impact … Maurizio...