Chemical Product (Formulation) Design Lecture 2Lecture 2old.chemeng.ntua.gr/seminars/download/seminar_2011-3-17_partB.pdf · Chemical Product (Formulation) Design Lecture 2Lecture

Chemical Product (Formulation) DesignLecture 2Lecture 2

Rafiqul GaniCAPEC

Department of Chemical & Biochemical EngineeringDepartment of Chemical & Biochemical EngineeringTechnical University of Denmark

DK-2800 Lyngby, Denmarkwww.capec.kt.dtu.dkwww.capec.kt.dtu.dk

Problem Definition - 2

Given: A set of building blocks for moleculesMolecular DesignGiven: A set of building blocks for molecules and a set of target (property) function values

W t d Th t f l l th t t h thWanted: The set of molecules that match the target function values

G f fMixture (liquid) DesignGiven: A set of molecules and a set of target (property) function values

Wanted: The set of blends (liquid solutions) that match the target function values

Workshop on Product Design, NTUA, March 2011 2

match the target function values

Motivation – I: Chemicals based products

Example-1: Design of a consumer product -an insect repellent lotion

What does it involve? Determine a liquid formualtion that is effective against

it h l t ll d kimosquito, has a pleasant smell, a good skin feeling and is a water-based product (active ingredient water solvents additives)ingredient, water, solvents, additives)

Properties?The above product attributes, when translated means that target valueswhen translated, means that target values on the following properties need to be matched: 90% evaporation time, phase split, p , p p ,solubility, viscosity, molar volume, toxicity, etc., & cost


Formulation design methodology

4Workshop on Product Design, NTUA, March 2011

St 1Stage-1: Computer aidedaided methds and tools


St 1Stage-1: Computer

id d thdaided methds and tools


Stages 2&3:Stages 2&3: Experimental componentcomponent


Formulation Design: Mixtures, blends, ...

INPUTS:1. Database of solvents properties (sub-task

Algorithm: Mixture designp p (

3.1.1)2. Mixture property models (sub-task 3.1.2)3. Number of target properties4. Temperature (K)5 Information for non linear models

LEVEL 2LEVEL 2LEVEL 1LEVEL 1 LEVEL 3LEVEL 3

n-m-l mixturescompositioncost

5. Information for non-linear models

Non-linear designNon-linear design

Linear designLinear design

Stability analysisStability analysis

Rule 1.1Rule 1.1Screening based on pure components propertiesRule 1.2Rule 1.2Calculation of the composition range for each target propertyRule 1 3Rule 1 3

Rule 1.1Rule 1.1Screening based on pure components propertiesRule 1.2Rule 1.2Calculation of the composition range for each target propertyRule 1 3Rule 1 3

Rule 2.1Rule 2.1Non linear models are solved with xi as inputRule 2.2Rule 2.2m mixture not matching non-linear constraints are rejected

Rule 2.1Rule 2.1Non linear models are solved with xi as inputRule 2.2Rule 2.2m mixture not matching non-linear constraints are rejected

Rule 3.1Rule 3.1The stability routine is run for each mixtureRule 3.2Rule 3.2l mixtures showing phase split at xi are rejected

Rule 3.1Rule 3.1The stability routine is run for each mixtureRule 3.2Rule 3.2l mixtures showing phase split at xi are rejected

Rule 1.3Rule 1.3Identification of the overall composition rangeRule 1.4Rule 1.4Identification of the xi leading to the cheapest mixture

Rule 1.3Rule 1.3Identification of the overall composition rangeRule 1.4Rule 1.4Identification of the xi leading to the cheapest mixture

n-m mixturescompositioncost

n mixturescompositioncost


costcost

Computer aided

A Case StudyComputer-aided…

• Design of a paint for house interiors

• Design of a spray insect repellent

• Design of a spray sunscreenDesign of a spray sunscreen

• Verification of a hair spray (in progress)

Experimental… Case study considered: spray insect repellentp

• Verification of the spray insect repellent

• Verification of the spray sunscreen (in progress)


Main activity:

g p p

Task 1: Problem DefinitionPerformance criteria:

Main activity: evaporation rate T90

lethal concentration LC50 effective against mosquitoes

long lasting

low toxicity water+water miscible solvents

non corrosive solventsy

water-based formula

good material compatibility

non corrosive solvents

solubility parameter δ, phase stability

kinematic viscosity ν molar volume good material compatibility

good stability

spray lotion

kinematic viscosity ν, molar volume Vm

cost C spray lotion

low priceQualities to enhance:

cos CConstraints:

500 < T90 < 1500 s0.39 < LC50 < +∞ mol/m3Qualities to enhance: 0.39 LC50 +∞ mol/m0 < ν < 75 cS

21.1 < δ < 27.1 MPa½

20 0 < Vm < 50 0 l/kmol

pleasant scent

good skin feeling

10

20.0 < Vm < 50.0 l/kmol good skin feeling

Workshop on Product Design, NTUA, March 2011

DEET:

Task 2: AI IdentificationDEET: aggressive on surfaces (clothes, plastics,..) high potential of irritating eyes sticky unpleasant odor

Picaridin: higher safety lower toxicity good material compatibility good material compatibility good cosmetic properties low water solubility

Natural AIs: low efficiency

high alcohol solubility

11

low efficiency


Solvent database: water + alcoholsTask 3: Mixture Design

Solvent database: water + alcoholsMixture property models: li /b d d itilinear/based on group decompositionMixture design routine results:

nº Mixtures x1δ

MPa½ν

cSρ

kg/lLC50

mol/m3T90s

Cost$/kg

Phase stabilityStable Phase split

1 methanol + water 0.32 42.0 0.83 0.89 0.74 819 0.65 X2 2-propanol + water 0.24 42.0 1.31 0.87 0.52 661 0.92 X3 allyl alcohol + water 0 29 42 0 1 14 0 96 0 52 598 1 10 X3 allyl alcohol + water 0.29 42.0 1.14 0.96 0.52 598 1.10 X4 tert-butyl alcohol + water 0.24 42.0 1.49 0.94 0.45 588 1.22 0.02 - 0.445 ethanol + water 0.27 42.0 1.01 0.89 0.58 734 1.42 X

6 2-methyl-1-propanol +water 0.23 42.0 1.66 0.88 0.42 597 1.72 0.02 - 0.46

7 2-butanol + water 0.24 42.0 1.62 0.88 0.41 520 1.81 0.02 - 0.467 2 butanol water 0.24 42.0 1.62 0.88 0.41 520 1.81 0.02 0.468 1-propanol + water 0.25 42.0 1.28 0.88 0.47 628 2.07 X

ν -linear ν -Cao RD % δ

Verification results: Optimization:

nº Mixtures ν -linearcS

ν -CaocS

RD %

1 methanol + water 0.83 0.81 2.632 2-propanol + water 1.01 0.97 4.603 allyl alcohol + water 1.28 1.30 1.045 ethanol + water 1 31 1 33 1 57

Alcoholsδ

MPa½

methanol 29.62-propanol 23.5allyl alcohol 27.5ethanol 26.5

nº Mixtures x1Cost$/kg

2 2-propanol + water 0.24 0.925 ethanol + water 0.27 1.42

12

5 ethanol + water 1.31 1.33 1.578 1-propanol + water 1.14 1.06 7.43

ethanol 26.51-propanol 24.5 8 1-propanol + water 0.25 2.07


Task 4: Additive Identification

Aroma compound: Linalool (light and refreshing, floral woody odor)Base Case Formula

Base case formulaFamily Chemical wiy C e c wi

AI picaridin 0.10

Solvent 2-propanol 0.39mixture water 0.50Additive linalool 0.01

Linear Design

n° of 2775

Linear Design

Non-linear Design

St bilit Ch k8

mixturesStability Check

Verification

1

53

8

13

1Optimal

searchWorkshop on Product Design, NTUA, March 2011

Task 5: Design of Experimentnº Test Experimental set-up

1 solubility limit of picaridin in water LLE apparatus2 phase stability of the solvent mixture 3 hours stirring2 phase stability of the solvent mixture 3 hours stirring3 solubility of AI in the solvent mixture 3 hours stirring

4 solubility of additive in the solution AI-solvent mixture 3 hours stirring

5 density of pure compounds, solvent mixture and formula weight of a known volume of liquid

6 viscosity of pure compounds, solvent i t d f l Brookfield viscosimetermixture and formula

7 evaporation time of pure compounds, solvent mixture and formula modified standard method ASTM 3539-87

8 spray-ability commercial spray container is employedproperties that can not be modelled8 spray ability commercial spray container is employed

9 appearance (turbidity/colour), odour observation and sniffing10 stickiness, greasiness, irritation application on the skin11 pH pH indicator strips

not be modelled

11 pH pH indicator strips

12 stability at different temperatures than 25 °C (5 °C, 45 °C)

one product sample in the refrigerator and another sample in the oven for some weeks

13 shelf life a product sample is left resting for three

14

13 shelf life months at room temperatureproperties not considered during the computer-aided design


Task 6: Experimental Verificationnº Test Test results

1 solubility limit of picaridin in water low solubility (9.3 gr/l @ 25 °C)

2 phase stability of the solvent mixture successful (homogeneus system)

3 solubility of AI in the solvent mixture successfulmixture

4 solubility of additive in the solution AI-solvent mixture successful

40% of deviation from exp value for5 properties of pure compounds 40% of deviation from exp value for predicted viscosity of picaridin

6 properties of solvent mixture matching constraints

7 properties of formula still matching constraints

8 spray-ability successful( bidi / l ) i f ( f9 appearance (turbidity/colour),

odournot satisfactory (too strong scent of picaridin)

10 stickiness, greasiness, irritation a little bit too sticky11 i f

15

11 pH not satisfactory

12 stability at different temperatures than 25 °C (5 °C, 45 °C) successfulWorkshop on Product Design, NTUA, March 2011

Tasks 7 & 8: Problems Identification and

A dAmendments

Problem Amendment

The pH of the formula is too high (8 5) for a skin care product which

Addition of a mild acid such as aceticacid to correct the pH. An addition of(8.5) for a skin care product which

should have a pH between 5 ÷ 7

p0.05 % w. of acetic acid lowers thepH to 5.5 (skin pH)Increase of linalool concentration. An

The scent of the formula is not acceptable since the picaridin odor prevails

addition of 4 % w. of linalool versusthe 1 % of the base case improves thescent of the all formula

The product is a little sticky and

Decrease the picaridin concentration,but adding the above compounds willThe product is a little sticky and

this is due to the picaridin lower the concentration of picaridinin the formula, so no otheramendments are planned


Task 9: Shelf Life Test and Iterationsnº Test Test results

1 solubility limit of picaridin in water

these tests are performed just in the first iteration

2 phase stability of the solvent mixture3 solubility of AI in the solvent mixture

4 solubility of additive in the solution AI- the first iteration4 solvent mixture5 properties of pure compounds6 properties of solvent mixture7 properties of formula still matching constraints

8 spray-ability successful9 appearance (turbidity/colour), odour acceptable9 appearance (turbidity/colour), odour acceptable

10 stickiness, greasiness, irritation reduced acceptable stickiness11 pH successful

stability at different temperatures than 2512 stability at different temperatures than 25 °C (5 °C, 45 °C) successful

13 shelf life test still in progress (1.5 months left, successful by now)

17

successful by now)


Final Formula

Family Chemical Wi %

AI picaridin 9 70AI picaridin 9.70

Solventmixture

2-propanol 44.25

t 42 00mixture water 42.00

Additiveslinalool 4.00

i id 0 05acetic acid 0.05


• The stability of the product at different

Modeling Considerations

295

300 25 °Cdesign temperature

designed mixture24% IPA

The stability of the product at differenttemperatures should be taken intoconsideration during the computer-aided design

280

285

290

5 °C

T(°C)

multi-phase region

24% IPA

• The flash point of the mixture shouldbe another constraint to considerduring the computer aided design

0 10 20 80 90 100270

275

isopropanol molar fraction

using the model from Liaw et al. (2002)

• The Hildebrand solubility parameterhas been shown to be a weak

p pparameter to control the solubility, andwe think that it should be replaced bythe 3-D Hansen solubility parameter 420

0.00 1.00 Solvent mixture Active Ingredients

360

380

400

Tflash (K)

designed mixture24% IPA

0.25

0.50 0.50

0.75

Additives

fPfH

300

320

340(K)

25 °Cdesign temperature

0.75

1.00 0.00

0.25

19

0.0 0.2 0.4 0.6 0.8 1.0280

isopropanol molar fraction

0.00 0.25 0.50 0.75 1.00

fD


Remarks

•A hybrid methdology for the design of formulations has been highligthed

through a case studythrough a case study

•It has been demonstrated that the screening of alternatives through a

computer-aided design can save time and resources and the optimal

product candidate can be identified

•Through experimental design, the weak points of computer-aided design

have been identified and suggestions for improvement have been made

•Current work is to complete experimental verification of the sunscreen

formula, while future work is to complete the new case study on a hair

spray product


Use of solvents in product development

Solvent use in different industrial sectors: mostly organic solvents

Reaction/SynthesisMixing: mass transport / phases

Isolation/SeparationSolvent extraction

SelectivityReaction rate

Scalability

Azeotropic distillationCooling crystallisation

Precipitation using an anti-solvent

Product DeliveryP i t I k d t

Easier operation

Washing of solid product

Paints, Inks, consumer products (lotion, hair spray, ...)

CleaningSafetyexotherm control


The Virtual Process-Product Design LabR. Gani et al. (2008, 2009, 2010)

Perform focused virtual experiments related to product process designWorkshop on Product Design, NTUA, March 2011 22

Perform focused virtual experiments related to product-process design

Insect Repellent Formulation Design

TASK 1TASK 1TASK 1Problem definitionProblem definitionProblem definition

TASK 2TASK 2TASK 2

TASK 1TASK 1TASK 1Problem definitionProblem definitionProblem definition

TASK 2TASK 2TASK 2

Virtual product-process lab - applicationInsect Repellent Formulation Design

Family Chemical wi

TASK 2TASK 2TASK 2AI identificationAI identificationAI identification

TASK 3TASK 3TASK 3

TASK 2TASK 2TASK 2AI identificationAI identificationAI identification

TASK 3TASK 3TASK 3

Conte et al. 2009

AI picaridin 0.10

Solvent 2-propanol 0.39

Mixture designMixture designMixture design

TASK 4Additive identification

Mixture designMixture designMixture design

TASK 4Additive identification

mixture water 0.50

Additive linalool 0.01Base case formulaBase case formula

2775

Linear DesignTASK 5 TASK 5 TASK 5 Design of experimentsDesign of experimentsDesign of experiments

TASK 6TASK 6TASK 6

TASK 5 TASK 5 TASK 5 Design of experimentsDesign of experimentsDesign of experiments

TASK 6TASK 6TASK 6 Performn° of mixtures

Non-linear Design

Stability Check8

58

TASK 6TASK 6TASK 6Exp. verificationExp. verificationExp. verification

TASK 7TASK 7TASK 7P blP blP bl

TASK 6TASK 6TASK 6Exp. verificationExp. verificationExp. verification

TASK 7TASK 7TASK 7P blP blP bl

Perform experiments to verify

d t Verification

1

5

3Optimalsearch

Problems Problems Problems identificationidentificationidentification

TASK 8TASK 8TASK 8

Problems Problems Problems identificationidentificationidentification

TASK 8TASK 8TASK 8

product performance

23

AmendmentsAmendmentsAmendments

Final formulaFinal formulaFinal formula

AmendmentsAmendmentsAmendments

Final formulaFinal formulaFinal formulaWorkshop on Product Design, NTUA, March 2011

Other examples of formulations: Fuel blends 1a

Gasoline blend


Other examples of formulations: Fuel blends 1b


Other examples of formulations: Fuel blends 1c

Pure component properties


Other examples of formulations: Fuel blends 1d

Generated results


P ti id U t k i L f

Product Design & Analysis - 1

Pesticide Uptake in a LeafCuticleEpidermisInternal StructureInternal Structure

of Leaf

C14Energy Contribution

C15

C16

Wax

Cuticle

hwax

hcuticle

Multilayer Multilayer Uptake ModelUptake Model

DropletVd

C17

Plant Compartment containing epidermis and the layers beneath

r(t>0) r(t=0)

Droplet Evaporation Model


the layers beneath.

Pesticide Uptake in a Leaf

Challenges and Opportunities – 2Pesticide Uptake in a Leaf

Diagrammatic representation of Equations used for Active Ingredient & Surfactant in the Model

* *( 1- 0) *AI AI AIdM D S C C dCdVddt hwax dt

( )1000* * * *ad j adj water adj adjAI

MW S S dCd dMdCd Vd

Droplet

1000* * * **

j j j jAI

AI adj

Vddt MW Vd dt dt

CO=KwdAI*CdAI0

Layers

hwax

Plus Surfactant Equations

Plus Surfactant0 CO=KwdAI*CdAI

21

1 ( * / )*( 2 2 1 0)AI waxdC D B h C C Cdt

1 Wax

hwax

1 21 2 1 0( * / )*( 2* )adj

adj wax adj adj adj

dCD B h C C C

dt

Equations0

216 15

16 (( * / )*( 17 16)) (( * / * )*(( * 16) 15))AI cut AI wax cutdC D B h C C D B h h KwcAI C Cdt

17dC

16

17

15

Cuticle

hcutPlus SurfactantEquationsPlus Surfactant

217

17 ( * / )*( 18 2* 17 16)AI cutdC D B h C C Cdt

17

30

Cuticle

C AI C30/K AI

Plus SurfactantEquations

Plus Surfactant Equations


PlantCpAI=C30/KcpAI Plus Surfactant Equations

Verification Through Uptake Model


Controlled Release of AIsProduct Design & Analysis - 1

Controlled Release of AIsCoreMembrane

Release

ro

ri

CrCd

I. Product Design

II. Process for manufacture of i l

medium

Toxic concentration

men

t

Overdosing

microcapsulesCore: AI solid/liquid, pure/solution

(or dispersion) + additives (solvent, l ifi )

Min. Eff. concentrationn

in e

nviro

nm

Min. Eff. Controlled emulsifier,...)

Coating: polymer membrane (rate-controlling) porous/non-porousUnder dosing

Con

cent

ratio

Under dosing

Controlledrelease

Needed property models develpedIII. Evaluation of performance

Time

Nuria Muro-Sune, PhD-thesis, 2005


Nuria Muro Sune, PhD thesis, 2005

Case study: Permethrin microcapsule

Permethrin microcapsule: Insecticide encapsulated Permethrin microcapsule: Insecticide encapsulated Reduce toxicity & longer bilogical effectivity

n-hexanewater

Cl

O

O

Cl

CoreMembrane

n hexanewater

O

Releasemedium

rori

CrCd

polyesterPermethrin

Compound Function

Solubility of Permethrin in

Solubility of Permethrin Ω

(2) Function solvent (ppm) 6

in solvent (w1)

Ω1

n-Hexane core solvent 106 0.5 2.0

Kp/solv

2.083Water release

medium0.006 6.0·10-9 1.66·108

PBMA polymer wall - - 0.96*

1.73*108


y

Model based solution strategies: Examples

Km/d = 2.67 Km/r = 0.12D = 8.9 E-20 ÷ 1.8 E-17 [m2/s]D 8.9 E 20 1.8 E 17 [m /s]

δ = 32 MPa1/2 Target property

1) Literature search

Controlled release of AI or polymer or microcapsule

design?Match target

1) Literature search…2) GC methods3) Other models

80

100

design?

20

40

60%

release

0

20

0 1 2 3 4 5

time [hr]


Conclusions•Innovative product design needs predictive solution•Innovative product design needs predictive solution approaches where property models are used (applied) for different roles(applied) for different roles •It is necessary to understand the role of the property model and be aware of their limitations •Use of experimental data in model development,Use of experimental data in model development, validation and process-product development needs to be carefully plannedto be carefully planned•It is necessary to develop predictive models with few additional experimental data (or use datafew additional experimental data (or, use data generated through other means, for example, molecular modelling or models like the PC SAFT)


molecular modelling or models like the PC-SAFT)

Documents

Chemical Product (Formulation) Design Lecture 2Lecture 2old.chemeng.ntua.gr/seminars/download/seminar_2011-3-17_partB.pdf · Chemical Product (Formulation) Design Lecture 2Lecture