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

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Second Kazan Summer School on Chemoinformatics – 06/07/2015 to 09/07/2015

Structure-Property modeling in the oil industry

Benoît Creton

[email protected]

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Structure-Property modeling in the oil industry – KSSCI – 06/07/2015 to 09/07/2015 2

IFP Energies nouvelles

Identity card

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9

GENERAL MANAGEMENT

Chairman: D. Houssin

Executive Vice-Presidents:

P. Barthélemy,

P.-H. Bigeard, G. Picard

IFPEN

Organization

Applied Chemistry and Physical Chemistry

Process Design and Modeling

RESOURCESDirector: M. Boutéca

TRANSPORTDirector: G. Monnier

PROCESSDirector: J.-P. Burzynski

EDUCATION AND TRAININGDirector: Ph. Pinchon

INDUSTRIAL DEVELOPMENTDirector: É. Lafargue

BOARD OF DIRECTORS

Human Resources

Positions attached to General

Management

Scientific Management

Strategy Division

Economics and Information Watch

and Management

SMEs Relations

Internal Auditing and Risk

Lyon site

Institutional Relations

and Communications

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Finance

Legal

International Relations

Geosciences

Applied Mechanics

Physics and Analysis

Catalysis and Separation

Process Experimentation

Powertrain and Vehicle

Mechatronics, Computer Science and Applied Mathematics

SCIENTIFIC BOARD

ADVISORY BOARD

OF IFP SCHOOL

BUSINESS UNITS

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E

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E

A

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C

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D

I

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F U N C T I O N A L

D I V I S I O N S

IFP School

INDUSTRIAL COMMITTEES

Organization

Structure-Property modeling in the oil industry – KSSCI – 06/07/2015 to 09/07/2015

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4 departments:

Physical chemistry of complex fluids

Biotechnology

Electrochemistry and materials

Thermodynamic and molecular simulation

Applied Chemistry and Physical Chemistry

4 Structure-Property modeling in the oil industry – KSSCI – 06/07/2015 to 09/07/2015

f(P,V,T) = 0

4 departments:


Biotechnology


Thermodynamic and molecular simulation:

EoS

4 departments:


Biotechnology



EoS

Molecular simulation

4 departments:


Biotechnology



EoS

Molecular simulation

Structure-property relationships

Structure

Property

Processing

Statistics

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Outline

Transport: prediction of fuel properties

Pure compound properties

Fuel properties

Resources: enhanced oil recovery (EOR)

Surfactant properties

Process: gas separation using nanoporous materials

New materials and materials properties

ANR funded project: CHESDENS


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Fuel properties

Why QSPR for fuel properties?

6

The knowledge of fuel properties is extremely important because they drive the conditions for storage, transportation, and combustion quality.

The introduction of “renewable” molecules with different chemistry as compared to that of petroleum derivatives requires a large amount of

research and development work.

The development of fast and accurate models for the prediction of fuel properties, useable in a screening procedure.

Molecular simulation.

Good description of systems BUT time-

consuming and some properties can not be

assessed.

Equation of State.

Fast and accurate methods to compute property values BUT some properties can

not be assessed.

QSPR.

I do not need to convince anyone

here…


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Fuel properties

2010-2013: Prediction of some fuel properties, (PhD study

by D. Saldana ➨ pure compounds / mixtures).

Pure compound properties: CN, FP, DcH, Tm, h(T), r(T) [1-5]


[1] Creton, B.; Dartiguelongue, C.; de Bruin, T.; Toulhoat, H. Energy Fuels 2010, 24, 5396-5403. [2] Saldana, D. A.; Starck, L.; Mougin, P.; Rousseau, B.; Pidol, L.; Jeuland, N.; Creton, B. Energy Fuels 2011, 25, 3900-3908. [3] Saldana, D. A.; Starck, L.; Mougin, P.; Rousseau, B.; Ferrando, N.; Creton, B. Energy Fuels 2012, 26, 2416-2426. [4] Saldana, D. A.; Creton, B.; Mougin, P.; Jeuland, N.; Rousseau, B.; Starck, L. OGST-revue IFP Energies nouvelles 2013, 68, 651-662. [5] Saldana, D. A.; Starck, L.; Mougin, P.; Rousseau, B.; Creton, B. SAR and QSAR in Environmental Research 2013, 24, 259-277.

FP CN ΔcH Tm η(T) ρ(T)

MD

PLS

GFA

FFANN

GRNN

SVM

FG

CD

PLS

GFA

FFANN

GRNN

SVM

GM

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Flash point [1]


[1] Saldana, D. A.; Starck, L.; Mougin, P.; Rousseau, B.; Pidol, L.; Jeuland, N.; Creton, B. Energy Fuels 2011, 25, 3900-3908.

Database:

625 compounds:

From DIPPR, articles,…

Hydrocarbons, Alcohols, Esters

Database:

625 compounds:



QSPR based models:

Various algorithms

Two families of descriptors

Database:

625 compounds:



QSPR based models:

Various algorithms

Two families of descriptors

Consensus modeling

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Use of models: predictions for new molecules, trends[1]


[1] Saldana, D. A.; Starck, L.; Mougin, P.; Rousseau, B.; Pidol, L.; Jeuland, N.; Creton, B. Energy Fuels 2011, 25, 3900-3908.

Predicted value

FP 368 K

CN 46

ρ at 15°C 789 kg.m-3

η at -20°C 6.4 mPa.s

η at 40°C 1.4 mPa.s

Tm 201 K

ΔHc -4690 kJ.mol-1

Fuel properties

2,6,10-trimethyldodecane

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Fuel properties


2010-2013: Prediction of some fuel properties, (PhD study

by D. Saldana ➨ pure compounds / mixtures).

FP prediction for mixtures [1]

QSPR with MDmix (calculated using a linear mixing rule)

[1] Saldana, D. A.; Starck, L.; Mougin, P.; Rousseau, B.; Creton, B. Energy Fuels 2013, 27, 3811-3820.

Predictive

Model

R2 0.974

CCC 0.986

RMSE 4.4

AAE 3.4

AARE 1.2

Bias -1.2

SVM-FGCD + non-linear mixing rule

Mixing rule:

, fed using

SVM-FGCD predictions.

N

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11

11,

DN

i

FPFPR

H

iii

ivap

ex

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[1] Saldana, D. A.; Starck, L.; Mougin, P.; Rousseau, B.; Creton, B. Energy Fuels 2013, 27, 3811-3820.

Fuel properties

Application to surrogate fuels:

9 jet fuels and 7 biodiesels,

up to 23 components.

Predictive

Model

R2 0.985

CCC 0.993

RMSE (K) 2.16

AAE (K) 2.0

AARE (%) 0.6

Bias (K) -1.8

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Transport: Fuel properties

Various combinations of Machine learning-descriptors

QSPR for pure compounds

QSPR for mixtures

Application of the hybrid model to surrogate fuels


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Oil recovery:

Primary (natural pressure),

Secondary (injection of gas or water),

Tertiary (EOR). Chemical EOR, injection of:

S : surfactants,

SP : surfactants/polymers,

ASP : alkaline/surfactants/polymers.


~45%

~60%

~15%

Enhanced oil recovery (EOR)

Oil recovery:

Primary (natural pressure),

Secondary (injection of gas or water),

Tertiary (EOR). Chemical EOR, injection of:

S : surfactants,

SP : surfactants/polymers,

ASP : alkaline/surfactants/polymers.

The S/SP/ASP formulation is a challenging task

Each eligible reservoir exhibits different conditions:

Oil composition,

salinity,

temperature,...

Development of QSPR for

surfactant properties[1-3]

[1] Creton, B.; Nieto-Draghi C.; Pannacci, N. Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 2012, 67(6), 969-983. [2] Moreau, P.; Oukhemanou, F.; Maldonado, A.G.; Creton, B. SPE International Symposium on Oilfield Chemistry 2013, 164091-MS. [3] Muller, C.; Maldonado, A.G.; Varnek, A.; Creton, B. Energy Fuels 2015, DOI: 10.1021/acs.energyfuels.5b00825.

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Enhanced oil recovery (EOR)


Winsor I O/W

Winsor III Winsor II W/O

Optimal salinity

Oil

Brine Salinity (g/L)

Behaviour of the system {brine, surfactant, oil}

Sopt Optimal salinity

Sopt obtained for the following conditions: Temperature: 40 C

Oil: n-dodecane

Brine: H2O + NaCl

S* = ln (Sopt/10)

°

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Surfactants

Generic molecular structure of AOS (A), IOS (B), AES (C)

and AGES (D) used in this study.

A surfactant formulation contains up to 20 amphiphilic

molecules.


(A) (B)

(C) (D)

[1] Muller, C.; Maldonado, A.G.; Varnek, A.; Creton, B. Energy Fuels 2015, DOI: 10.1021/acs.energyfuels.5b00825.

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Models for the optimal salinity

3 families of descriptors: FGCD, SMF, MD

Descriptors for mixtures using:

3 machine learning methods: PLS, RS, SVM

SVM-SMF appears as the best combination to obtain

local and global models, RMSE values:


Descriptors PLS RS SVM

Local 1 Local 2 Global Local 1 Local 2 Global Local 1 Local 2 Global

SMF 0.159 0.198 0.215 0.216 0.190 0.224 0.164 0.180 0.216

FGCD 0.226 0.254 0.253 0.224 0.299 0.285 0.203 0.256 0.249

CMD 0.218 0.298 0.328 0.203 0.313 0.323 0.196 0.333 0.302

[1] Muller, C.; Maldonado, A.G.; Varnek, A.; Creton, B. Energy Fuels 2015, DOI: 10.1021/acs.energyfuels.5b00825.

N

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Resources: Surfactant property, S*


Various combinations of Machine learning-descriptors

QSPR for mixtures

Solid step in the efforts of in silico determination of S*

for promising surfactants

Further progress are related to the extension of the

surfactants families and to the accounting for

temperature and brine composition as variables in

QSPR models for optimal salinity of surfactants.

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Why QSPR for materials ?


Gas separation with porous materials

Carbon dioxide capture and storage (CCS) appears as a transitional key technology to regulate the anthropogenic CO2 emissions to the atmosphere.


Search for an adsorbent:

high adsorption capacity,

high selectivity,

easily regenerable,

price affordable,

chemically stable…


Search for an adsorbent:

high adsorption capacity,

high selectivity,

easily regenerable,

price affordable,

chemically stable…

Fast and accurate techniques to screen adsorbents:

QSPR !

Pressure Swing Adsorption:

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New family of adsorbents: Metal Organic Frameworks,

Zeolitic Imidazolate Frameworks

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Gas separation with porous materials

Organic Units + Metal Units = ZIF

+ Zn2+


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New nanoporous materials.[1]


Gas separation using porous materials

Zeolitic Imidazolate Frameworks (ZIF is a

subfamily of MOFs).

Methyl groups localization.

Database using quantum chemical

calculations.

Descriptors for nanoporous materials.

Search for a multilinear model.

2

2 1

4 5

circle : DFT, square : FAU, diamonds : GIS,

triangle up : LTA, triangle down : MER, cross :

RHO and star : SOD

black : Im, orange : 2-MeIm, blue : 4-MeIm, grey :

5-MeIm, red : 2,4-MeIm, violet : 2,5-MeIm and

green : 4,5-MeIm

,'

5_*'

4_*'

2_*'

*

3

3

3

D

nbCH

nbCH

nbCH

FDET

[1] Galvelis, R.; Slater, B.; Chaudret, R.; Creton, B.; Nieto-Draghi, C.; Mellot-Draznieck, C. CrystEngComm 2013, 15, 9603-9612.

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Gas separation using porous materials


0

20

40

60

80

100

120

0 20 40 60 80 100 120q°st sim./qst exp. (kJ/mol)

q° s

t pre

d.

(kJ/m

ol)

TRAINING

TEST

EXP

Interactions gas/solids.[1]

Zeolitic Imidazolate Frameworks (ZIF is a

subfamily of MOFs).

Gases : Ar, CH4, C2H6, N2, O2, CO2, CO,

H2S, SO2, H2O, CH3CN.

Database obtained using molecular

simulations.

Descriptors for solids calculated

considering ligands, topology,...

Multilinear model.

gsgOLgb

sOLst

µµHT

HQq

/43

fg210

ln

n

[1] Amrouche, H.; Creton, B.; Siperstein, F.; Nieto-Draghi, C. RSC Advances 2012, 2, 6028-6035.

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Solution for CO2 capture: Can we predict which are

the best materials for a given separation ?

Partners:

MADIREL, Experiments and coordinator of the project

Institut Charles Gerhardt, Theoretical structures

IFP Energies nouvelles, QSPR

Website: http://www.agence-nationale-recherche.fr/?Project=ANR-13-SEED-0001



Known materials

Adsorption experiments

Molecular simulations

Database

Descriptors + QSPR based models

Theoretical structures (Automated Assembly of Secondary Building Units)

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Process: Porous materials properties

QSPR for nanoporus materials and host-guest

Descriptors for such materials



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Acknowledgments

Strasbourg University

A. Varnek



H. Amrouche

T. de Bruin

C. Nieto

P. Mougin

C. Muller

D.A. Saldana

L. Starck

H. Toulhoat

…

TNO

A.G. Maldonado

Orsay University

B. Rousseau

Solvay

P. Moreau

F. Oukhemanou

benoit.creton@ifpen - kpfu.rukpfu.ru/portal/docs/F1453638380/Creton.pdf2014 -s Fuel properties...

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