rgani cmu bayer lecture 2013

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Computer Aided Solvent Selection, Design& Application

Rafiqul Gani

CAPEC, Department of Chemical & BiochemicalEngineering, Technical University of Denmark,

Lyngby, Denmark

[email protected]



DTU at Lyngby

Bayer Lecture, Carnegie-Mellon University, 5 March 2013 2

Some photos of Denmark!

Little mermaid

Nyhavn – a

popular locationin Copenhagen

Hamlet’s castle



Technical University of Denmark - Main Campus

DTU Chemical Engineering

3Bayer Lecture, Carnegie-Mellon University, 5 March 2013




Research at CAPEC

6 Research Areas) Programs




CAPEC

Faculty Members & Administrative Secretary

Researchers: 2

Lab Technicians: 0PhD-students: 17* + 4

MSc/BSc-students: 7+

Visitors/Guests: 3Total: 39

(RaG, JA, GSI, JKH & EVA)

List of CAPEC Co-workers : December 2012

Head of CAPEC: RaG

Secretary: Eva; (Gitte)

* Including joint projects with PROCESS; + Per term




Introduction

Computer aided solvent selection, design & application




Solvent: Solvent is that constituent of a solution that is

liquid in the pure state, is usually present in the larger

amount and has dissolved the other constituent (a solute)

of the solution. The solute may be a solid, a liquid or a gas.

The solvent may be a single compound or a mixture of

compounds.

Solvents




Solvent applications

Reaction/Synthesis

Mixing: mass transport / phasesSelectivity

Reaction rateScalability

Isolation/Separation

Solvent extractionAzeotropic distillationCooling crystallisation

Precipitation using an anti-solvent

Washing of solid product Cleaning – waste removal

Product DeliveryPaints, Inks, consumer products

(lotion, hair spray, ...)

Safetyexotherm control

Easier operation




Industrial sector usage – tonnes per year

Source: KemI's Products Register 2004

Solvents (many are Volati le Organic Compounds, have

dif ferent uses in dif ferent industr ial sectors)

Paint manufacturers 79,376

Manufacture of organic base chemicals 74,163

Pharmaceutical industry 22,180

Metal coating industry 14,172

Construction 11,276

Graphic industry 8,624

Manufacture of wood articles (not furniture) 6,928

Manufacture of perfume and toilet articles 6,252

Manufacture of plastic articles 4,652

Food industry 3,717

http://webreq.msu.edu/Strstock/Active/15543770.jpg

http://www.nexternal.com/gallade/images/1164206112.jpg




Examples of industrial usage & problem

59%20%

1%

20%Water

Other

Reactants

Solvents 59%20%

1%

20%Water

Other

Reactants

Solvents

• Solvent and watercontribute ~80% of theprocess mass intensity.

• Emphasizes need forresearch to reduce theuse & hazard of thesolvent; and improveprocess efficiency

Gonzalez-Jimenez, GSK, 2007

Use of Solvents in Industry - Pharmaceutical

Note: Contents of formulated (consumer) products arealso nearly 80% solvents



Motivation versus contradictions


Objective is to achieve zero solvent synthesis(but is i t poss ib le wi thout change of catalysts? )

Use green solvents ( but the def in i tion of what is

green is not clear! )

Make processes-products more sustainable ( butwhat is the solvent-separat ion -energy demand

versus sus tainabi l ity analys is boundary? )

Ionic liquids have very low vapor pressures,they can substitute organic solvents to reducethe VOC problem ( bu t many appl icat ions need the

so lvent to vapor ize ou t! )




Solvent selection and design problem

How do we find the most appropriatesolvents (pure or blends) from them, for

specific applications?

More than a million chemicals have been

identified and many more millions can begenerated through special software




Introduction

Problem definition & solution approach




The work-flow/data-flow for solubility calculation


AI or API’s

or Solvents

Do we have the

necessary

properties?

( ΔH f , T m )

Model-based

properties

estimation

Do we have

values for γi?

Choose a solubility

model

(UNIFAC, PC-

SAFT, NRTL-SAC,

UNISAC)

Do we have

pure-compounds

parameters?

Routine for Mixture

Model parameters

estimation

Do we have

experimental

data?

Regression routine for

pure-compounds

parameters

Yes

NoNo

Yes

Yes

Yes

DatabasesDatabases

No

Databases

No

Model-based

Pure-compounds

parameters estimation

Routine for

Activity coefficients

calculation

Routine for

phase-diagram

generation

Databases Databases Databases

Databases




Separation

Cleaning

Organicsynthesis

Biphasicreactionsystems

Formulatedproducts

LLE; SLE; VLE

Solids; liquids

Inert; promotereactions

Create 2-phases& promotereactions

Dissolve &deliver AI

Mechanisms involved

Process design; product

recovery

Operation design;equipments

Feasibility of synthesisroute ; process design

Reaction feasibility;process design

Product design;evaluate performance

Problems MechanismsApplications

Pharmaceuticalproducts

Dissolve,deliver, enhanceProduct –process design




Mathematical (problem) definition

Fobj = min {CT S(Y, ) + f(x, y, u, d, θ ) + .........}

L1 ≤ θ1(Y, ) ≤ U1

L2 θ2(Y, , θ) ≤ U2

L3 θ3(Y, , θ, x) ≤ U3

L4 θ4(Y, , θ, y) ≤ U4

SL S(Y, ) ≤ SU

B x + CTY ≤ D

P = f(x, y, u, d, θ )

property functionmodels

molecular/processstructural constraints

process functionmodels




Mathematical (problem) definition: Example


L1 θ1(Y, ) ≤ U1 Tb = Yi i (ni, Ci)

L2 θ2(Y, , θ) ≤ U2 T = [(Hv - RT)/Vm]0.5

L3 θ3(Y, , θ, x) ≤ U3 Log Ps = A + [B/(C + T)]

L4 θ4(Y, , θ, y) ≤ U4

SL S(Y, ) ≤ SU

B x + CTY ≤ D

P = f(x, y, u, d, θ )

Design molecules that matches targets of Tb, T andhas vapor pressures at specified temperatures




Mathematical (problem) definition: Example


L1 θ1(Y, ) ≤ U1

L2 θ2(Y, , θ) ≤ U2

L3 θ3(Y, , θ, x) ≤ U3

L4 θ4(Y, , θ, y) ≤ U4 m = [(1/Vmi ) xi]

SL S(Y, ) ≤ S

U T

m = [(T

mi ) x

i]

B x + CTY ≤ D VLE; miscibility

P = f(x, y, u, d, θ )

Design liquid blends that matches targets of Tm, density,bubble point temperature at a specified pressure

S l i S H b id




Solution Strategy: Hybrid


L1 θ1(Y, ) ≤ U1

L2 θ2(Y, , θ) ≤ U2

L3 θ3(Y, , θ, x) ≤ U3

L4 θ4(Y, , θ, y) ≤ U4

SL S(Y, ) ≤ SU

B x + CTY ≤ D

P = f(x, y, u, d, θ )

Stage IV

Stage IV: Min Fobj sub ject to θ 4



Our target optimalsolution


Another view of ”decomposition-hybrid" approach

Where is Wally?

Donde esta Waldo?

Hvor er Holger?

........

........

Target

A t l l




A conceptual example

"I want acyclic

alcohols, ketones,

aldehydes and ethers

with solvent properties

similar to Benzene"

A set of building blocks:CH3, CH2, CH, C, OH,

CH3CO, CH2CO, CHO,

CH3O, CH2O, CH-O

+

A set of numerical

constraints

A collection of groupvectors like:

3 CH3, 1 CH2, 1 CH,

1 CH2O

All group vectors

satisfy constraints

Refined property

estimation. Ability to

estimate additional

properties or use

alternative methods.

Rescreening against

constraints.CH3

CH2

O

CH

CH2

CH3

CH3

CH3

CH2

O

CH2

CH

CH3

CH3

CH3

CH2

O

CH

CH2

CH3

CH3

CH3

CH2

O

CH2

CH

CH3

CH3

2.order

group

Pre-design Design (Start)

Design (Higher levels) Start of Post-design

Interpretation to

input/constraints

Group from

other GCA

method

Solvent selection-substitution-design problem

Method: CAMD (Computer Aided Molecular Design)

C t id d l t l ti d i & li ti




Introduction


Computer aided framework





Computer Aided Framework – Main Features

•Method & tool for generation of molecular

structures & blends•Method & tool for property estimation

•Databases (data)

•Property models

•Property based function evaluations (eg,VLE, LLE, SLE, ...)

•Method & tool for screening of alternatives

•Process-product performance models




Descriptors & Molecule Representation

• Chemical Formula: C n H 2n+2

• Structural description:

CH 3 - CH - CH 2 - CH2 - CH 3

|

CH 3

• Bonds: C-C, C- H, C=C, …

• Conjugates: Occurrences of different bonds

• Groups: CH 3 -, - CH 2 -, - OH, CH 3 CO - , …

Method & tool for generation of alternatives




Generation of alternatives

Groups as bui lding blocks: CH 3 -, - CH 2 -, - OH,

CH 3 CO - , … (a set of about 180 groups available)

Structural constraints

Size constraints

P di ti t d l




0 1 2 3

17.5C j j

j

c c i i j j k k i j k

V n

V V N Vc w M Vc z O Vc

Examples:

GC MODELS

pure compoundsmixtures

Examples: UNIFAC (VLE, LLE, SLE)

Marrero and Gani

Jobackln ln lnCOM RES

i i i

Compound 1 Compound 2

Predictive property models

Group contribution (GC) methods for propertyprediction

Solvent Selection Design Software




Solvent Selection-Design Software

Methodology

Database

Databases

Models:

UNIFAC,

UNISAC, PC-

SAFT, NRTL-

SAC

Methodology

Solvent screening and solvent mixture

design for pharmaceutical applications

ProCAMD

VLE SLE LLE

ProCAMD

ProCAMD

ProPred

Solvent database

IL (900)

1500 solvents

SolventPro

ESCAPE-23, 2012;AIChE annualmeeting 2012

Database: Knowledge Representation



28

Molecular Types

Property Types

V1,1,1

…….

Vk,1,1

…….

VT1

…...

VTf,

VTr

…...

Model Types

PP1,1

…….

PP j,1

…….

Chemical Types

P1

……

Pi

Pm

……

Property variables

u1,1,1,1

…….

ul,1,1,1

…….

1n , 1PP

1,1q ,1, 1V

1,1,1ac ,1, 1, 1uKnowledge

base

Organic ChemicalsSolventsIonic LiquidsLipidsAromaActive Ingredients

Example: Organic chemicals

Example : Alcohols

Example: Primary

Normal boiling point

Normal melting point

Critical temperature

....

....

Database: Knowledge Representation

Bayer Lecture, Carnegie-Mellon University, 5 March 2013

Use of ontology

At the end is aframe with theproperty value,references,

uncertainty, etc



M d lli f k Fl ibl lti l bl




Modelling framework : Flexible, multiscale, reusable

• Add one or moreparameters (regressthem with available

experimental data)

• Or, use another modelto generate themissing data

…..

Develop a better theory !

How to perform

miracles in modelling?

Property Prediction : Flexible multiscale reusable




Property models at different scales

Property Prediction : Flexible, multiscale, reusable

Correlations

Molecular

*Groups

*Atoms

MicroAcc uracy (ver i f icat ion)

Predict ive power (design)

Log Pi = Ai + [Bi/(Ci + T)]

Zc = (Pc*Vc)/(83.14*Tc)

Tb = 222.543*log(Sum.Groups.I +

Sum.Groups.II + Sum.Groups.III)

CH3-; -CH2-; -OH; …..

P=∑niPi + b(vχ0) + 2c(vχ1)C, H, O, N, S, ….

* Use smaller scale models

to predict parameters forthe larger scale model

* Use same data setsto develop models attwo adjacent levels

Modelling framework : Flexible multiscale reusable




Process models at various scales

Modelling framework : Flexible, multiscale, reusable

Accuracy (ver i f icat ion)

Predict ive power (design)

Use smaller scalemodel to predictparamters/data forlarger scale model. Usesame data sets todevelop two adjascent

level models



Computer aided solvent selection design & application




Introduction


Computer aided frameworkExamples of application


Example of solvent based separation




ProcessRaw Mater ials

Clean Water Contaminated Water

Products

Determine target for solvent

* f S = FW (Xin,s – Xout,s)

Solubility, S = f S/FS

* Find solvent to match

target S – use data & models

Extraction

Solvent

Solvent + solute

Clean Water

Problem solution

* f S = 100 (0.018 – 0.00)

Solubility, S. FS = 1.8

• Solvent ID provides S

• FS = S/1.8

• Solvent ID decides extractionprocess; solvent-solute relationsidentify the process parameters

Order dif ferent solutions according

to cost of solvent & operation

Example of solvent-based separation

Solvent Substitution: Benzene




Chemicals Design: Replacement of Benzene

We have an aqueous mixture of phenolin a waste water stream. We need to

remove the phenol. Benzene is known as

a solvent but due to environmentalreasons, we cannot use it. What should

be a good replacement solvent for

benzene?






Step 1: Problem Formulation

* Property specifications: – Tb > 322 K

– Tm < 314 K

– 29 kJ/mol < Hvap < 34 kJ/mol – logP > 1.5

– High solvent power

– High Phenol precipitation mole fractionat 298 K






Step 2: Initial Search (generate

candidates through database search)* Property specifications:

– Tb > 322 K

– Tm < 314 K – SP (solubility parameter)

* Use the above properties to search

among non-aromatic compounds* Design acyclic compounds: alcohols,

ketones, aldehydes, ethers.


Step3a: Database search




Step3a: Database search

Problem: Find solvents that have

19.5 > Sol Par < 20.5

Solution: Use a search engine within a database toidentify the set of feasible molecules

Step3b: CAMD




Step3b: CAMD

Step 4: Verification & Further Analysis




Step 4: Verification & Further Analysis

• COSMO solvation energy calculated usingMOPAC93

• Indication that the aldehyde is a lowerranking alternative (stripping operation).

Methyl sec-Butyl Ether 2,2-Dimethyl-1-propanal

Structure

Solvation energy -3.863 kcal/mol -7.081 kcal/mol



Example:Separation of an azeotropic mixture




Example:Separation of an azeotropic mixture

Problem: A process stream of 50 mole% Acetone and 50

mole% Chloroform at 300K, is to be separated.

Separation techniques considered:

Adsorption (liquid, gas)

Crystallization

Desublimation

Distillation – simple

Distillation – extractiveDistillation with decanter

Liquid-liquid extraction

Flash/evaporation

Membrane (gas, liquid)

Microfiltration

Partial condensation

Separation techniques:Distillation – simple

Distillation – extractive

Distillation – azeotropicLiquid extraction

Pressure swing

No external medium knownBinary ratios of propertiesidentify the followingalternatives

Note: Acetone-chloroform forms a high boil ing azeotropethat is pressure sensitive

Solvent design sub-problem




Solution:1-Hexanal

Methyl-n-pentyl ether

(Benzene)

Solvent design sub problem

• CAMD problem:• 340 < Tboil < 420

• Selectivity > 3.5

• Solvent power > 2.0• No azeotropes

• Number of compounds designed: 47792

Number of compounds selected: 53• Number of isomers designed: 528

Number of isomer selected: 23

• Total time used to design: 57.01 s

Verification: Phase behaviour




Verification: Phase behaviour

Verification: Process simulation




Verification: Process simulation

Objective function:

Maximize

Profit = Earnings

– Solvent cost

– Energy costs

Constraints:

Acetone purity > 0.99

Chloroform purity > 0.98

Results:

Solvent Solventflow rate RefluxReb. 1 RefluxReb. 2 Objectivefunction1-hexanal 0.082 kmol/hr 0.45 0.65 2860.51 $/hr

Compu ters & Chemical Eng ineering , 1999 (org anic so lvents ), 2012 (ILs )

Product recovery/purification




Product recovery/purification

Combine CAMD, ProPred, Solubility tools & Database

To solve following problems –

Given, the molecular description of a pharmaceutical product,

find solvents needed a) in its production; b) in its formulation

Only problem a) to be highlighted

Solution strategy: Define CAMD problem to generate solventcandidates; verify performance through solubility calculations;check database to verify predictions

Solvent design: Sub-product & process design




Solvent design: Sub product & process design

1CH3, 2CH2, 1CH3COO, 1CH2O

2-EthoxyEthlyAcetate SLE diagram

T-X Diagram

298

303

308

313

318

323

328

333

0 0.2 0.4 0.6 0.8

Mole fraction of Ibuprofen (x1)

T e m p e r a t u r e ( K )

Optimal Solvent

n- Hexane

Ethylene Glycol

A computer aided-molecular design framework for crystallization solvent design.

Karunanithi, A.T; Achenie, L.E.K; Gani, R. Chemical Engineering Science, 2006, 61, 1243-1256.

Designedsolvent

Ibuprofen

RecrystallizedIbuprofen

SEM

(morphologystudy)

P-XRD

(Structure Analysis)

Product Recovery: Crystallization





Solution strategy: Define CAMD problem to generate solventcandidates; verify performance through solubility calculations;check database to verify predictions

Chemical product:

Ibuprofen

Find solvents, anti-solvents & their mixture that satisfy thefollowing:

Potential recovery > 80%

Solubility parameter > 18 MPA1/2 (or > 30)

Hydrogen bonding solubility parameter > 9 MPA1/2 (or > 24)

Tm < 270 K; Tb > 400 K; -log (LC50) < 3.5

Consider cooling as well as

drowning-out crystallization





Chemical product:

IbuprofenConsider cooling as well as







Chemical product:

IbuprofenConsider cooling as well as



Formulation Design: Mixtures, blends, ...





Four case studies have been developed:

Design of a white paint for house interiors

Design of an alcohol based insect repellent (spray lotion)

Design of an water based insect repellent (spray lotion)

Design of a water resistant sunscreen (spray lotion)

AIChE J, 2011 (method), 2012 (verif icatio n)






form: viscosity ν, density ρ , solubility δ

Performance criteria:

what do consumers want?

easy and fast applicability

high durability

good stability water resistance

low toxicity

good material compatibility

pleasant skin feel

low price

Target properties:

which are the related chemical properties?

solvents evaporation rate T 90

phase equilibrium: 1 phase system

solvents: oil soluble chemicals

lethal concentration LC 50

suitable database of solvents

solvents with good cosmetic properties

cost C





o u at o es g tu es, b e ds,

Target properties:

viscosity ν

density ρ ( )

solubility δ

lethal concentration LC 50 evaporation time T 90

one phase system

solvent type

cost

Constraints:

V V

0.0 < ν < 75.0 cS

100.0 < < 150.0 l/kmol

0.85·δ AI < δ < 1.15·δ AI MPa½

3.16 < LC 50 < +∞ mol/m3

700 < T 90 < 1300 s

Considered later

in the design





g , ,

Summary of all the actions performed

during the mixture design task:

4656

Linear Design

Non-linear Design

Stability Check

Verification

Optimal 1

77

6

6

6





g , ,

Family Chemical x i

w i

AIs

avobenzone 0.010 0.0325

octyl salicycate 0.013 0.0325

α-Carotene 0.003 0.0163

β-Carotene 0.003 0.0161

vitamin A 0.011 0.0325

TiO2 0.025 0.0325

Solvent

mixture

methoxyacetaldehyde 0.789 0.593

2,2-dimethylpropylbutanoate 0.098 0.157

Additives

octorylene 0.009 0.034

parabens 0.021 0.033

iso-propyl salicylate 0.018 0.033

Final result - optimal formulation:

Solvents for Organic Synthesis




g y

Given: A set of target (desired) properties

Find: Molecules and mixtures that match thetarget properties

Example (solvents for organic synthesis): For aspecific reaction or reaction type, identify if solventsare necessary to improve yield or promote the reaction.If yes, find solvents that will have the least EHS impact

and improve reaction conditions.

Select from known candidates (database)Design when candidates are not known

Application (organic synthesis) example - II



pp ( g y ) p

Collaboration with GSK &AstraZeneca

LIPASE

ENZYME

The objective is to find a

feasible set of chemicalsthat could be used assolvents in an enzymaticglycerolysis reaction, whichtakes place in the presenceof a catalyst (lipaseenzyme).

Glycerolysis reaction: Step 1 – define problem





Introduction


Computer aided framework

Examples of application

New directions & conclusions

Role of solvents in Phase Transition Catalysis




Q+X-R-Y “Active” form of PTC

Q+Y-R-X

Product

“Spent” form of PTC

Q+X-

“Active” form of PTC

Q+Y-

“Spent” form of PTC

M+Y-Final fate of displaced leaving group Y-

M+X-Source of desired anion

Aqueous phase

Starting material

+ +Organic solvent

X = Cl; Y = Br; Q = TBA; R = benzyl-ring; M = Na

Known solvents: hexane, toluene; new: pentylacetate

Picco lo et al. CACE-31, 2012; ECCE-8, Berlin , 2011

Product-process design




“Fish diagram” of the system

water-decane-2-butyloxyethanolKahlweit’s “Fish diagram” (Kahlweit M. and Strey R.,Angew. Chem. Int. Ed. Engl.24 (1985) 654-668)

Emulsified products & biphasic reaction systems

New solvents




Gibbs free energy of

mixing versus drugvolume fraction forSoluplus-Felodipinesystem at T = 140ºC

Modelling of solubility ofAPI in different lipids

Conclusions




• It is important to understand the need forsolvents (when they are needed, what

functions they will have, ...)

• Success of the method depends on data andmodels (properties, process, …) employed –

therefore, much effort has been made toimprove and extend the application range

• Success with problems - biphasic reactions,

phase transition catalysts, API solubility - have

shown that the necessary models can bedeveloped even under limited data availability

• What next? Add uncertainties in data & models

Current Industrial Consortium Members



US-EPA

ChemProcess

Technologies

Neste

Jacob Oy

30 member companies

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rgani cmu bayer lecture 2013

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