introduction to chemical product design introduction to chemical

Introduction to chemical product designIntroduction to chemical product design, molecular design problem definition & role of

t d lliproperty modelling

Rafiqul GaniCAPEC

D t t f Ch i l E i iDepartment of Chemical EngineeringTechnical University of Denmark

DK-2800 Lyngby, Denmarky g y

Overview - 1P bl D fi itiProblem Definition

Process Design Product Design

Process Fl h t?

Raw material Products Product

molecule/Product properties

Atoms or molecules

Flowsheet?Operating condition?

Equipment parameters?

molecule/ mixture?

Product Molecule orcondition? parameters?functions?

Molecule or mixture synthesis?Product Application Design

Applicationprocess?

Product Performance

Integrate Process-ProductApplication conditions?


& Application

Workshop on Product Design, NTUA, March 2011 2

Overview - 2E l f P d t P I t tiExamples of Product-Process Integration

Process Design Product Design

Process Fl h t?

Raw material Products Product

molecule/Product properties

Atoms or molecules

Flowsheet?Operating condition?


molecule/ mixture?

Product Molecule orcondition? parameters?functions?

Molecule or mixture synthesis?

P t l bl d & t l d t•Petroleum blends & petroleum products

•Polymers & blendsRoutinely solved!

Can be solved!

•Specialty chemicals (including drugs, ….) Process role?


Overview - 3E l f P d t P I t tiExamples of Product-Process Integration

Product DesignF d t h

Product molecule/

Product properties

Atoms or molecules

For products such as drugs, pesticides, food, …delivery and application is molecule/

mixture?Product Molecule or

delivery and application is important & needs to be validated

functions?Molecule or mixture synthesis?Product Application Design

Applicationprocess?

Product Performance

Production process Application conditions?


needs to be reliable – first time right is important


Course Content

• Different types of design problems

•Modelling issues •Solution Approachespp

• ConceptsSingle molecule design• Single molecule design

• Mixture (blend) design


Different Types of Design Problems1 Design of Molecule and/or Mixture1. Design of Molecule and/or Mixture

Given, a set of target properties , find molecules orGiven, a set of target properties , find molecules or mixtures that match the target properties CAMD & CAMbD

l t fl id d ti idsolvents, fluids, … drugs, pesticides, aroma, ….

ValueLow HighL Production rate

Finding candidates

Large SmallEasy Difficult



CAMD & CAMbD: ExampleFrom a list of 250 solvents, find those that can be used as an oil-paint additive characterized by 25%-, 50%- & 75%-evaporation rate, solubility, viscosity and surface tension. Form the feasiblerate, solubility, viscosity and surface tension. Form the feasible set, find the optimal cost solvent mixture.

• Synthesis method for mixturesSy es s e od o u es• Property models for solvents• Evaporation models for solvents• Cost = f(Ci, xi)

Note: Mixture design similar to oil-blend (petroleumNote: Mixture design similar to oil-blend (petroleum, edible oil, ….), polymer blend, formulations, ….



Given, a set of target properties , find molecules orGiven, a set of target properties , find molecules or mixtures that match the target properties CAMD & CAMbD

l t fl id d ti idsolvents, fluids, … drugs, pesticides, aroma, ….

Issues & Needs• Generate feasible candidates (synthesis method & tool)

E ti t t t ti ( di ti t d l )• Estimate target properties (predictive property models)• Evaluate performance (application process models)

A h i t l C t Aid d M l l D i Th & P ti CACE 12 2003


Achenie et al, Computer Aided Molecular Design: Theory & Practice, CACE-12, 2003

Different Types of Design Problems2 Product Process Evaluation2. Product-Process Evaluation

Given, a list of feasible candidates (product and/or process), the bj ti i t id tif / l t th t i t d tobjective is to identify/select the most appropriate product-process

based on a set of performance criteria.

For product design this problem is similar to CAMD but without theFor product design, this problem is similar to CAMD but without the generation step; also, similar to CAMbD where additives are added to a product to significantly enhance the performance of the product

Examples• Select the optimal combination of pesticide and

surfactants that match the needs of specific plantssurfactants that match the needs of specific plants• Select the optimal combination of drug/pesticide,

solvent and polymeric microcapsule for controlled release of the product

• Increase the yield of a product by hybrid process operation


operation


Simple Example: From the derivatives of Barbituric acid, identify the did t th t i i d i th ll t t (C) t dcandidate that is required in the smallest amount (C) to produce a

1:1 complex with protein (binding to bovine serum albumin) –calculated as a function of octanol-water partition coefficient of the candidate compounds: Log10(1/C) = 0.58 log10P + 0.239

• Synthesis tool for isomer generation• Property model for Log10P• Verify solubility in water

Note: How is the backbone (barbituric acid) identified and how is the relation (drug activity) between C vs log10P found?



Process Analytical Technology – PATPAT

To ensure final product pquality!

Estimate iti lcritical

scientific & regulatory parameters early! A. S. Hussain,

CDER, FDA, 2002


Different Types of Design Problems3 Design of Product centric Process3. Design of Product-centric Process

Issues & NeedsProblem Characteristics

Issues & Needs

Algorithm for generation of fl h t

• Products are consumer products such as soap, detergents, fragrances, food, ki process flowsheet or

batch recipeskin-care …

• Complex chemical systems involved

Process-operation models for verification by simulation

• Life of the product is getting shorter

• Volume/cost relationship Property models for

product/process analysis

pneeds to be improved by selling more

• Low-cost chemical routes are needed (better catalysts, faster reactions, increased yields, …)


Integration of Product Product Design

Framework for Integrated Approach - 1Integration of Product-Product Design

hwax

hcuticle


Role of property modelling

P di ti d t fPrediction and measurement of properties for product design


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


Motivation – II: Chemical processesE l 2 D i f h i lExample-2: Design of a chemical process

What does it involve? Combine different unit operations to convert raw materials (chemicals) to desired products (chemicals) in a sustainable, efficient and cost effective mannereffective manner.

Properties? Wide range of properties needed to t bli h th h i l d t ( d d/establish the chemical product (pure compound and/or

mixture properties) and the process (how the chemical properties change under conditions of temperature,properties change under conditions of temperature, pressure and/or composition). Monomer A

Monomer B

Solvent (S)

Initiator (I)MIXER

Separatoreffluent

Transferagent (T)

Inhibitor (Z)

MIXER

REACTOR

SEPARATOR

Copolymerproduct


Challenges & IssuesWide range of products & processes must be handledWide range of products & processes must be handled

Fertilizers ElectronicsNutrition

Communications,Entertainment

. . . . .

Vitamins Fuel

EntertainmentChemical Processes

Colorants andPlasticsPharmaceuticals

Mobility

Colorants andcoatings

DetergentsHealthcare

ClothingHousing


Challenges & Issues

H t id th t l iHow to provide the necessary property values in a fast, efficient and reliable way?

Software tools:

models; algorithms; databases

Properties:Chemical Properties:

pure compound & i t

systems:organic; polymers;

& mixtureelectrolytes; ..


Challenges & Issues

Properties for product-process design*Databases: collection of measured data (there are gaps in the

)database)

Property models: fill-in the gaps (available models are not perfect )

*Synthesis: generation-screening of alternatives (fast, qualitatively correct predictive models are needed)

*Design/analysis: selection-validation (reliable, quantitatively correct models are needed)


)

Role of property models

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

Property modelsMonomer A

Monomer B

Solvent (S)

Initiator (I)

T f

MIXER

Separatoreffluent

d m

Process modelsProperty models are embedded into process models

Transferagent (T)

Inhibitor (Z)

REACTOR

SEPARATOR

Copolymerproduct

, , ( , , ) ; 1 ,ii n i o u t i

d m f f r m T P V i N Cd t

or

ntal

MsuOperation models Formulation process

dels

foro

nmen

mpa

ct

Models

ustainam

etriProperty-kinetics Process models

process model

Product

Mo

envi

r im

s for ability csmodels

Cost models

Product evaluation model


Property Modelling Concept

Predictive: Group contribution plus atom connectivity for primary pure component y p y p pproperties and mixture properties (GE-models*, viscosity, surface tension, EOS (PC-SAFT) ...)y ( ) )

Correlated: Secondary pure component properties (Antoine DIPPR functions equationsproperties (Antoine, DIPPR-functions, equations of state, ....) and mixture properties (GE-models**, viscosity surface tension EOS (SRK PR) )viscosity, surface tension, EOS (SRK, PR), ...)

* UNIFAC (different versions)( )

** NRTL, UNIQUAC, Wilson, ....


Property Model Development – GC concept

Organic Chemical & Polymer Properties3

Organic Chemical & Polymer Properties1st-order: CH3 , CH2, CH,

2

15

4

OH, COOH, CH3CO, ….

2nd-order: (CH ) CH 2

0v 1v 2v

2 -order: (CH3)2CH, ..

3rd-order: HOOC-(CHn)m-

groups connectivity index

COOH (m>2, n in 0..2)

REPRESENTING DATA SET AS GROUPS & ATOMS

g p y

EXPERIMENTAL DATA SET COLLECTION


Property Model DevelopmentOrganic Chemical & Polymer PropertiesOrganic Chemical & Polymer Properties

DETERMINING ATOM or MINIMIZATION OF SUM OF SQUARED RESIDUALSGROUP CONTRIBUTIONS SQUARED RESIDUALS

( experimental – estimated)

MARRERO / GANI GC-method (2001) Atom-CI Model (2006)O / G GC et od ( 00 )

f(y) = ∑niCi + w∑mjDj + z∑okEk + F

to C ode ( 006)

f(y)=∑aiAi + b(vχ0) + 2c(vχ1) +

2c(vχ2) + D2c( χ ) + D

Note: f(y) is function of property x; example f(y) = Exp(Tb /tbo)

MODEL DEVELOPMENT

EXPERIMENTAL DATA SET COLLECTION

REPRESENTING DATA SET AS GROUPS & ATOMS


Property prediction (pure component)



Equations ofEquations of state: SRK, PR, PC-SAFTSAFT, ..


Property prediction: organic chemicals & polymersModelling options for primary propertiesModelling options for primary properties

G ti fGcplus concept GC model + connectivity indices

Generation of missing groups for pure compound GC

models[1]

*( ) ( ) higher order i ii

f Y N C f Y

GC+ models

models[1]

GC models

Group contribution based models

GC model + experimental data

Higher order models for

models

GC model + force field (MM2)GC model (MG, CG, JR, W, …)

Higher order models for structure distinction (cis-

trans isomers)Gani, R.; Harper, P.M.; Hostrup, M. Automatic Creation of Missing Groups through Connectivity Index for Pure-Component Property Prediction Ind Eng Chem Res 2005 44 7269


Property Prediction. Ind. Eng. Chem. Res., 2005, 44, 7269.

GC+ concept for mixture property prediction

UNIFAC GC models

2ln ln ln lnCOM RES RESi i i i Compound 1 Compound 2

1. Given: Compounds, compositions & temp.

2. Represent molecules with 1st-order & 2nd-2. Represent molecules with 1st order & 2ndorder groups

3. Retrieve group parameters

4. Calculate activity coefficients in liquid phase

Equlibrium systems: VLE, LLE, SLE, VLLE, SLLE, SLVE

5. .......


q y

The UNIFAC-CI Concept

A lot of gaps inth UNIFAC t i !

Methodology to fill the blanks in the UNIFAC matrix

VLE, SLE, LLE Sometimes time the UNIFAC matrix! VLE, SLE, LLE

experiments to collect data

consuming and expensive and

even not possible

By using just atom and

Filling the UNIFAC matrix just By using just atom and

bond information Of the groups describing the

jwith the published VLE, SLE and LLE

dataAtom and bond g

molecules

•Without any extra experimental data•With reasonable accuracy

Atom and bondBased information

(connectivity indices)

GC model forMixtures(UNIFAC)

yUNIFAC-CI

Gonzales et al , Prediction of Missing UNIFAC Group-interaction, Parameters through connectivity


indices, AIChE J (2006); FPE (2010-submitted)

CH2C=C

Predict Missing UNIFAC Group Interaction ParametersC≡CACHACCH2 C,O,H atoms (current state)OHCH30H adding N atomsACOHDOH adding F atoms

Potentially all the group

H2OCH2CO adding Cl atomsCHOFurfural adding Br atomsCCOOHCOO adding I atomsCH2O

ginteractions parameters

could be covered COOH adding S atomsCNH2CNH adding Si atoms(C)N3CCN adding Cl & F atomsACNH2Pyridine

using UNIFAC-CI !

PyridineCNO2ACNO2DMFACRYCF2ACFCClCClCCl2CCl3CCl4ClCCACClBrIICS2CH3SHDMSOCOOSiH2SiONMP


NMPCClFCONOCCOHCH2SMorpholineThiophene

Property prediction: UNIFAC-CI (VLE)

Methylcyclopentane-Benzene at 298 K

Ethanol-Hexane at 473 K


Property prediction (mixture): UNIFAC-CI

Example: Generation of missing parameters for solubility predictions of pharmaceutical active ingredients


S it d f d t d i / l i

Property prediction softwareSuited for product-process design/analysis

MoT

Introduce new modelsnew models to the system


Database development

* K l d t ti (d l d t l )* Knowledge representation (developed ontology)•Classes (chemicals; AIs; Aromas; Solvents; ....)( ; ; ; ; )

•Sub-classes (organic, polymers, inorganic, ..)I t ( l h l k t ffi )•Instances (alcohols, ketones, paraffins, ..)

•Objects-A (property types: primary, ...)j (p p y yp p y, )•Frames (property values: Tb, Tc, Tm, ...)

•Objects-B (applications: solvents, ....)* Search engine for data-retrieval (forward and/or g (reverse)

R. Singh et al. ”Design of an efficient ontological knowledge based system for selection of ” C & C


process monitoring and analysis tools”, Computers & Chemical Engineering, 2010

Database: Contents & features

• Pure compound data: primary properties• Binary mixture data: VLE and infinite dilution (in y (

water)• Solid solubility data (C4 – C60) 4 60• Special databases

• Solvents• Active ingredients• Aromao a• Agents (neutralizers, emulsifiers, ....)

• Search engines & data retrieval interfaceSearch engines & data retrieval interfaceExample of a lipid database and associated property models

(Diaz-Tovar et al FPE 2010)


(Diaz Tovar et al., FPE, 2010)

U di ti d l t di t th i i t

Hierarchy of property modelsUse a more predictive model to predict the missing parameter

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

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

Groups Tb = 222.543*log(Sum.Groups.I + Sum.Groups.II + Sum.Groups.III)

CH3-; -CH2-; -OH; …..

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

Use the smaller scale (atoms)

Micro

Use the smaller scale (atoms) model to predict the missing

parameters of the larger scale (GC)

Accuracy (verification)

Predictive power (design)


Increasing application range of property modelsAccuracy (quantitative)

Property Pi = Ai + Bi + Ci + Di

y (q )

Models (simple small molecules

& mixtures)

Higher-order models (plus bigger more complex molecules & mixtures)El

ectr

olyt

e

gg )

GC+ models (plus isomers & more complex

Eym

ers

GC models (plus isomers & more complex molecules & systems)

M lti l d l

Poly

Multi-scale models (plus more complex systems and structured chemicals)

rgan

ic

A li ti ( lit ti )


Or Application range (qualitative)

Molecular Design (single molecule)


Chemical product design frameworkE ti lEssential, desirable and EH&Sand EH&S properties Product &

process

Build Identify processes

process evaluation

molecules & mixtures; verify their

Identify processes that can manufacture theverify their

properties

manufacture the product sustainably

1. Define needs; 2. Design products; 3. Design processes; 4. Evaluate process-product


General Product Design Problem as MINLPMINLP means Mixed Integer Non Linear ProgrammingMINLP means Mixed Integer Non Linear Programming –optimization problems formulated as MINLP contains integer (Y) and continuous variables (x, y) as optimization g ( ) ( y) pvariables, and, at least the objective function and/or one of the constraints is non-linear.

Process*/product model

Fobj = min {CTY + f(x, y, u, d, θ ) + Se + Si + Ss + Hc + Hp}

P = P(f, x, y, d, u, θ) p

Process/product constraints

( , , y, , , )

0 = h1(x, y)

0 ( d) “Other” (selection) constraints

0 g1(x, u, d)

0 g2(x, y)Alternatives (molecules; unit operations; mixtures; fl h t )

B x + CTY D

flowsheets; ….)40Workshop on Product Design, NTUA, March 2011

2. Product Design: CAMD Framework

Start• Solutions of CAMD problems are iterative

Candidateselection

(final verification/comparison

Problemformulation

(identify the

No suitablesolutions (due toperformance,economic or

safety concerns)

are iterative.• Problem

formulation

Method andResult

pusing rigoroussimulation andexperimentalprocedures)

(identify thegoals of the

design operation)Promisingcandidates havebeen identified

controls the success.E ti l Method and

constraintselection(specify the

design criteriabased on the

Resultanalysis andverification(analyse thesuggestedcompounds

Finish

• Essential qualities vs. (un)desirable

problemformulation)

CAMDSolution

using externaltools)

( )qualities.

• Connectivity with t l t l Solution

(identifycompoundhaving the

desiredproperties)

external tools, data and methods.


CAMD General Problem Solution3 i S i A

1. Set Goals (Pre-Design Stage) – Define Needs

3-stage Iterative Solution Approach ( g g )

• Essential properties• Desirable properties• Safety Environmental etcSafety, Environmental, etc.

2. Design/Selection (Design Stage) – Generate Alternatives

S h th h d t b• Search through database• Iteratively build molecules/mixtures, comparing to goals

• Generate set of feasible molecules/mixtures• Simultaneously build and evaluate molecules/mixtures

• Identify optimal molecule/mixture• Identify feasible sety

3. Analyze, verify and select (Post-Design Stage) - Final Selection (process/operation constraints)


CAMD Problem Solution: Database Search

Problem: Find solvents that haveProblem: Find solvents that have 19.5 > Sol Par < 20.5S l ti U h i ithi d t b tSolution: Use a search engine within a database to identify the set of feasible molecules


CAMD Problem Solution: Enumeration

Pre-design Design (Start)

Multi-level generate & test according to a predefined sequence

"I want acyclicalcohols, ketones,

aldeh des and ethers

A set of building blocks:CH3, CH2, CH, C, OH,CH3CO, CH2CO, CHO,

CH3O CH2O CH O

A collection of groupvectors like:

3 CH3, 1 CH2, 1 CH,1 CH2O

g g ( )

Interpretation toinput/constraints

aldehydes and etherswith solvent properties

similar to Benzene"

CH3O, CH2O, CH-O+

A set of numericalconstraints

1 CH2O

All group vectorssatisfy constraints

Refined propertyCH

CH2

OCH2

CHCH3CH2 CH2 CH3

2.ordergroup

Design (Higher levels) Start of Post-design

estimation. Ability toestimate additionalproperties or use

alternative methods. CH3

CH3 O CH

CH3

CH3

CH3 O CH

CH3

group

Rescreening againstconstraints.

CH3

CH2

OCH

CH2

CH3

CH3

CH2

OCH

CH2

CH3Group fromother GCA

method


Example of problem solution with ProCAMD


Integrated Product-Process DesignProblem: A process stream of 50 mole% Acetone and 50Problem: A process stream of 50 mole% Acetone and 50 mole% Chloroform at 300K, is to be separated.Separation techniques considered:

N t l di kp qAdsorption (liquid, gas)CrystallizationDesublimation

No external medium knownBinary ratios of properties identify the following

Distillation – simpleDistillation – extractiveDistillation with decanter

identify the following alternatives

Liquid-liquid extractionFlash/evaporationMembrane (gas, liquid)

Separation techniques:Distillation – simpleDistillation – extractive

h

(g , q )MicrofiltrationPartial condensation

Distillation extractiveDistillation – azeotropicLiquid extractionPressure swingPressure swing

Note: Acetone-chloroform forms a high boiling azeotrope that is pressure sensitive


p

Pressure dependence


Solvent design sub-problem

Solution:• CAMD problem:340 T 420 1-Hexanal

Methyl-n-pentyl ether• 340 < Tb < 420• Selectivity > 3.5 y p y

(Benzene)y

• Solvent power> 2.0• No azeotropes• No azeotropes

Number of compounds designed: 47792– Number of compounds designed: 47792Number of compounds selected: 53

– Number of isomers designed: 528Number of isomer selected: 23Number of isomer selected: 23

– Total time used to design: 57.01 s


Phase behaviour


Problem formulation & Solution

Objective function:

Maximize

Profit = EarningsProfit = Earnings

– Solvent cost

– Energy costs

Constraints:Constraints:

Acetone purity > 0.99S l t S l t R fl R fl Obj ti

Results:Chloroform purity > 0.98Solvent Solvent

flow rateRefluxReb. 1

RefluxReb. 2

Objectivefunction

1-hexanal 0.082 kmol/hr 0.45 0.65 2860.51 $/hr


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 solvent gy p gcandidates; verify performance through solubility calculations; check database to verify predictions


Product Recovery: Crystallization

Chemical product: Ibuprofen

C id li llConsider cooling as well as drowning-out crystallization

Find solvents, anti-solvents & their mixture that satisfy the following:Potential recovery > 80%Potential recovery > 80%Solubility parameter > 18 MPA1/2 (or > 30)H d b di l bilit t > 9 MPA1/2 ( > 24)Hydrogen bonding solubility parameter > 9 MPA1/2 (or > 24)Tm < 270 K; Tb > 400 K; -log (LC50) < 3.5

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


check database to verify predictions



C id li llConsider cooling as well as drowning-out crystallization




Consider cooling as well as drowning-out crystallization


Case Study – Polymer Design: MINLP Applied to CAMD

Polymer design problem where polymer repeat units with 2-4 different groups and 2-9 total number of groups in the repeat unit structure are being sought

F bj [(P P *)/P *]2Fobj = [(Pi – Pi*)/Pi

*]2

s.t.

Tg 373 K

Density 1 5 g/cm3Density 1.5 g/cm3

Water absorption = 0.005 g H2O/g polymer)

-CH2- ; -CO- ; -COO- ; -O- ; -CONH- ; -CHOH-; -CHCL-



Fobj = [(Pi – Pi*)/Pi

*]2

s tselect property models

s.t.

Tg = [ (Yknk)]/ [ (Mknk)] (373 20) K

Density = [ (Mknk)]/ [ (Vknk)] (1.5 0.1) g/cm3

W t b ti [ (H )]/ [ (M )]Water absorption = [ (Hknk)]/ [ (Mknk)]

(0.005 0.0005) g H2O/g polymer) ( ) g 2 g p y )


Note: objective function and constraints are non linear; nk, the optimization variables are integer (0-9)




*]2

s ts.t.

353 K < Tg = [ (Yknk)]/ [ (Mknk)] < 393 K

1.4 < = [ (Mknk)]/ [ (Vknk)] < 1.5 g/cm3

0 0045 < W = [ (H n )]/ [ (M n )] < 0 0055) g H O/g polymer)0.0045 < W = [ (Hknk)]/ [ (Mknk)] < 0.0055) g H2O/g polymer)

2 yj 3 ; yj : 0 or 1 for j=1,7

2 yknk 9


add structural constraints

CH2 ; CO ; COO ; O ; CONH ; CHOH ; CHCL

Note: objective function and constraints are non linear; nk, the optimization variables are integer (0 9)


optimization variables are integer (0-9)

Case study – Polymer Design: Reformulate - I


*]2

s ts.t.

353 K < Tg = [ (Yknk)]/ [ (Mknk)] < 393 K

1.4 < = [ (Mknk)]/ [ (Vknk)] < 1.5 g/cm3

0 0045 < W = [ (H n )]/ [ (M n )] < 0 0055) g H O/g polymer)0.0045 < W = [ (Hknk)]/ [ (Mknk)] < 0.0055) g H2O/g polymer)

50 < (Mknk) < 100 add a new constraint

2 yj 3 ; yj : 0 or 1 for j=1,7

2 yknk 92 yknk 9



Case study – Polymer Design: Reformulate – II (MILP)

Min Fobj = M

s t

reformulate Fobj & constraintss.t.

18.7 < TgM= [ (Yknk)] < 37.3 K g/mol

50/1.4 < M/ = [ (Vknk)] < 100/1.5 g/cm3

0 0045*50 < W M = [ (H n )] < 0 0055*100 g H O/g polymer)0.0045*50 < W M = [ (Hknk)] < 0.0055*100 g H2O/g polymer)

LB1 yj UB1 ; yj : 0 or 1 for j=1,7

LB2 yknk UB2

-CH2- ; -CO- ; -COO- ; -O- ; -CONH- ; -CHOH-; -CHCL-CH2 ; CO ; COO ; O ; CONH ; CHOH ; CHCL

14 ; 28 ; 44 ; 16 ; 43 ; 30 ; 48.5


MINLP Applied to CAMD: Exercise 2G t l th t t h th f ll i t tGenerate polymers that match the following target:

Total number of different main groups:

5 for Problem 1

4 for Problem 24 for Problem 2

4 for Problem 3

2 yjnj 5 ; yj : 0 or 1 for j=1,4

m=(vj -2)nj( j ) j

0 yknk m ; yk: 0 or 1 for k=5,8


MINLP Applied to CAMD: Exercise 3G t l l th t t h th f ll i t tGenerate molecules that match the following target:

Min f=Hfus

180 < Tm < 225 K

400 < T < 440 K400 < Tb < 440 K

-CH3 ; -CH2NO2; -CH3CO; -OH; -CH2=CH

-CH2-

-CH-yjnj = 4 ; yj : 0 or 1 for j=1,7

CH2 vjnj 3 ; yj : 0 or 1 for j=1,5

0 yknk 2 ; yk: 0 or 1 for k=60 yknk 2 ; yk: 0 or 1 for k 6

0 ylnl 1 ; yl: 0 or 1 for l=7


MINLP Applied to CAMD: Exercise-4G t l l th t t h th f ll i t tGenerate molecules that match the following target:

Min f= Cixi

LB1 < Tm = Tmi xi < UB2

LB < V = f(T T P Z x) < UBLB2 < Vb = f(T, Tc, Pc, Zc, x) < UB2

Comp1, Comp2, Comp3, …………..CompN

yj= 2 ; yj : 0 or 1 for j=1 Nyj 2 ; yj : 0 or 1 for j 1,N

0 < x1 < 1

0 < x2 < 1

x1 + x2 = 1


1 2

Exercise Problems


Tutorial Exercises

• Analyze the drug Ibuprofen (define the d f i i ffi i ineeds for improving process efficiency in

extracting the product from solution in the reactor) – use ProPred

• Use ProCamd to match the list of solventsUse ProCamd to match the list of solvents and anti-solvents for IbuprofenS l h i h i bl i h• Solve the organic synthesis problems with ProCamd

See slides 53-55


Exercises

• Analyze the drug Ibuprofen (define the needs for improving process efficiency in p g p yextracting the product from solution in the reactor) – use ProPredthe reactor) – use ProPred– Search compound in database

• start ICAS• click on M, select basic search, search with CAS-

b (015687 27 1) SMILESnumber (015687-27-1), copy SMILES

– Start ProPred• import SMILES, analyze properties


Exercises

• Use ProCamd to match the list of solvents and anti-solvents for Ibuprofen– Formulate problems (solvent & anti-solvent)Formulate problems (solvent & anti solvent)

see slides 53-55)• Start ProCamdStart ProCamd

– Specify problem (ProCamd manual)– Start execution– Analyze results


Other chemical product design problems – 1a

Problem Statement-1: From the derivatives of Barbituric acid, identify the candidate that is required in the smallest amount (C) to produce a 1:1 complex with protein (bindingamount (C) to produce a 1:1 complex with protein (binding to bovine serum albumin)

Barbituric Acid (000077-02-1)


Other chemical product design problems – 1bProblem Statement 1: From the derivatives of BarbituricProblem Statement-1: From the derivatives of Barbituric acid, identify the candidate that is required in the smallest amount (C) to produce a 1:1 complex with protein (binding

)to bovine serum albumin)

Barbituric Acid (000077-02-1)

Calculate C as a function of octanol-water partition coefficientof the candidate compounds: Log10(1/C) = 0.58 log10P + 0.239


Other chemical product design problems – 1c

Problem Statement-1: From the derivatives of Barbituric acid, identify the candidate that is required in the smallest amount (C) to produce a 1:1 complex with protein (binding to bovine serum albumin) p p ( g )

Barbituric Acid (000077 02 1)Barbituric Acid (000077-02-1)

Calculate C as a function of octanol-water partition coefficient of the candidate compounds: Log (1/C) = 0 58 log P + 0 239candidate compounds: Log10(1/C) = 0.58 log10P + 0.239Question: How is the backbone (barbituric acid) identified and how is the relation (drug activity) between C vs log10P found?


( g y) g10 f

Other chemical product design problems – 1d


Barbituric Acid (000077 02 1)Barbituric Acid (000077-02-1)

Calculate C as a function of octanol-water partition coefficient of the candidate compounds: Log10(1/C) = 0.58 log10P + 0.239Solution Steps: Generate candidates; calculate LogP; calculate C; order solutions w r t C to identify the best candidate; check for solubility in water


solutions w.r.t. C to identify the best candidate; check for solubility in water

Other chemical product design problems – 1e


Solution Steps

•Generate candidates (check database)

•Use ProPred to calc LogP and LogWs

Barbituric Acid (000077 02 1)

•Calculate C

•Order solutions with respect to CBarbituric Acid (000077-02-1)

Calculate C as a function of octanol-water partition coefficient of the candidate compounds: Log10(1/C) = 0.58 log10P + 0.239

p

candidate compounds: Log10(1/C) 0.58 log10P + 0.239Candidates (CAS Numbers) : 061346-87-0; 000076-94-8; 091430-64-7;

001953-33-9; 007391-69-7; 090197-63-0; 017013-41-1; 027653-63-0


001953-33-9; 007391-69-7; 090197-63-0; 017013-41-1; 027653-63-0

Other chemical product design problems – 2aProblem Statement-2 (Design of backbones & termination of backbones): In thisProblem Statement-2 (Design of backbones & termination of backbones): In this

problem, we will start with ProPred, take a known molecule (for example, Corticosterone), use the features in ProPred to create free attachments in the

molecule (that is, create a backbone). The Backbone is then transferred tomolecule (that is, create a backbone). The Backbone is then transferred to ProCamd, where terminated structures are generated (see page 53 of tutorial document)

Step 1: Start ProPred from ICASStep 2: Click on (database) , click on “Find CAS”, type the CAS Number for Corticosterone (00005-22-6), select the compound by clicking on OKStep 3: ProPred draws the molecule and predicts the properties Step 4: Remove the OH group connection from the molecular structure at the two locationsStep 5: Launch ProCamd from ProPred from the tools menu in ProPredStep 5: Launch ProCamd from ProPred from the tools menu in ProPred.Step 6: Fill-out the necessary problem definition pages in ProCamd (general problem control, non-temperature dependent properties)Step 7: Terminate the backbone by clicking on “GO”. Step 8: Save the backbone-termination problem as “save ProPred backbone problem” under the file menu. To use this file later, the saved backbone file


must be loaded from the file menu.

Other chemical product design problems: 2bProblem Statement 2 (Construct backbones & terminate withProblem Statement-2 (Construct backbones & terminate with

ProCamd): Use Corticosterone as an example

St 2 Cli k (d t b ) li k “Fi d CAS” tStep 2: Click on (database) , click on “Find CAS”, type the CAS Number for Corticosterone (00005-22-6), select the compound by clicking on OKthe compound by clicking on OK


Other chemical product design problems: 2c

P bl St t t 2Problem Statement-2 (Construct backbones &

terminate with ProCamd):Use Corticosterone as anUse Corticosterone as an

example

Step 3: ProPred draws the molecule and predicts the properties


Other chemical product design problems: 2dP bl St t t 2Problem Statement-2

(Construct backbones & terminate with ProCamd):Use Corticosterone as an

example

Step 4: Remove the OH pgroup connection from the molecular structure at the two locations

Backbone with two-free attachments. Note that as ProCamd generates gmolecular structures only with the Constantinou & Gani groups, it must be possible for the Constantinou & Gani method to represent the backbone. Otherwise, ProPred will not launch ProCamd


Other chemical product design problems: 2eP bl St t t 2Problem Statement-2


example

Step 6: Fill-out the necessary problem definition pages in ProCamd (general

bl t lproblem control, non-temperature dependent properties)


Other chemical product design problems: 2fP bl St t t 2Problem Statement-2


example

Step 7: Terminate the backbone by clicking on “GO”.

Note that for backbone termination step, the options for P P d ti d d t bProPred properties and database search cannot be used.


Other chemical product design problems: 3aProblem Statement-3 (Generate & terminate backbones):In this problem weProblem Statement 3 (Generate & terminate backbones):In this problem, we will first generate a backbone with ProCamd using only the C & H atoms and

with 1 free-attachment in the backbone. We will then go to ProPred to draw the molecular structure and work on the structure without terminating the o ecu a st uctu e a d o o t e st uctu e t out te at g t estructure. We will then launch ProCamd from ProPred to find the final

terminated structure through ProCamd (see pages 53-60 in tutorial document)

Step 1: Start ProCamd and specify the following backbone generationStep 1: Start ProCamd and specify the following backbone generation problem.

Step 2: Run ProCamd to generate the backbone alternatives. Note that for the backbone generation, “isomer” generation is not allowed. For the problem formulated in step 1, the following result is obtained.

Step 3: Click on ProPred to transfer the backbone structure Propred draws theStep 3: Click on ProPred to transfer the backbone structure. Propred draws the structure and calculates all properties, as shown below.

Step 4: Launch ProCamd from ProPred from the “tools” menu

A new ProCamd application is opened. ProPred sends back the same backbone structure that ProCamd generated.

Step 5: Complete the backbone termination problem with ProCamd


Step 5: Complete the backbone termination problem with ProCamd.

Other chemical product design problems: 3b

Problem Statement-3 (Generate

backbone with ProCamd &

terminate manually with ProPred)with ProPred)

Step 1: Start P C d dProCamd and specify the following backbone generation problem. p

Note: at this stage specification of any other property is not necessary


Note: at this stage specification of any other property is not necessary

Other chemical product design problems: 3c

Problem Statement-3 (Generate backbone

with ProCamd & terminate manually

with ProPred)

Step 2: Run ProCamd to generate the backbone l i N h falternatives. Note that for

the backbone generation, “isomer” generation is not allowed. For the problem formulated in step 1, the following result is obtained.g


Other chemical product design problems: 3d


with ProCamd &with ProCamd & terminate manually

with ProPred)

Step 3: Click on ProPred to transfer thetransfer the backbone structure. ProPred d thdraws the structure and calculates all properties, as shown below.


Other chemical product design problems: 3e


with ProCamd & t i t llterminate manually

with ProPred)

Step 4: LaunchStep 4: Launch ProCamd from ProPred from the “tools” menu

A new ProCamdA new ProCamd application is opened. ProPred sends back the same backbone structure that ProCamd generated.



Other chemical product design problems: 3f


with ProCamd &

ProCamd generates 74 terminated structures out of which

d 1 i Ib fterminate manually with ProPred)

Step 4: Launch

compound 1 is Ibuprofen

Step 4: Launch ProCamd from ProPred from the “tools” menu

A new ProCamdA new ProCamd application is opened. ProPred sends back the same backbone structure that ProCamd generated.



Other chemical product design problems: 4a

Problem Statement-4 (Design large molecules): Design a large molecule having the following properties,p p

Mw > 300

Tb > 400 KSee page 61 of tutorial

documentTb 400 K

Tm > 300 Kdocument

Solve the problem with ProCamd and then switch to ProPred and further investigate the properties of the l l l i l di f h i f h ilarge molecule, including further increase of the size of the molecule. Only the “general problem control” and the “non-temperature depd props” need to beand the non-temperature depd props need to be specified.


Other chemical product design problems: 4b

Problem Statement-4 Design a large molecule having the following properties,p p

Mw > 300; Tb > 400 K; Tm > 300 K

Solve the problem with ProCamd and then switchProCamd and then switch to ProPred.

Repeat the problem for acyclic d d licompounds and cyclic

compounds


Exercises: summary

• Solve example problem 1 (find best AI)• Solve example problem 2 (backbone

termination)termination)• Solve example problem 3 (backbone

generation & termination)• Solve example problem 4 (generate largeSolve example problem 4 (generate large

molecules, pass to ProPred and then analyze and further develop )analyze and further develop )


Summary

• For computer aided product-process design, models are necessaryy– If appropriate models are available, almost all

problems can be solved• Solution of different product-process design

problems need a good understanding of the problems so that the available tools can beproblems so that the available tools can be applied correctly and efficientlyProblems related to low value products are easier• Problems related to low-value products are easier to solve than the high-value products

The limiting factors for these problems are availability– The limiting factors for these problems are availability of data and appropriate models


introduction to chemical product design introduction to chemical

Documents