Introduction to chemical product design Introduction to chemical
Transcript of 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?
Equipment parameters?
& Application
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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?
Equipment parameters?
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?
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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?
Equipment parameters?
needs to be reliable – first time right is important
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Course Content
• Different types of design problems
•Modelling issues •Solution Approachespp
• ConceptsSingle molecule design• Single molecule design
• Mixture (blend) design
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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
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Different Types of Design Problems1 Design of Molecule and/or Mixture1. Design of Molecule and/or Mixture
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, ….
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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, ….
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
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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
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operation
Different Types of Design Problems2 Product Process Evaluation2. Product-Process Evaluation
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?
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Different Types of Design Problems2 Product Process Evaluation2. Product-Process Evaluation
Process Analytical Technology – PATPAT
To ensure final product pquality!
Estimate iti lcritical
scientific & regulatory parameters early! A. S. Hussain,
CDER, FDA, 2002
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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, …)
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Integration of Product Product Design
Framework for Integrated Approach - 1Integration of Product-Product Design
hwax
hcuticle
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Role of property modelling
P di ti d t fPrediction and measurement of properties for product design
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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
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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
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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
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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; ..
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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)
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)
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
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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, ....
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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
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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
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Property prediction (pure component)
Equations ofEquations of state: SRK, PR, PC-SAFTSAFT, ..
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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
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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. .......
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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
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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
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NMPCClFCONOCCOHCH2SMorpholineThiophene
Property prediction: UNIFAC-CI (VLE)
Methylcyclopentane-Benzene at 298 K
Ethanol-Hexane at 473 K
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Property prediction (mixture): UNIFAC-CI
Example: Generation of missing parameters for solubility predictions of pharmaceutical active ingredients
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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
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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
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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)
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(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)
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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 )
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Or Application range (qualitative)
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
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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.
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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)
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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
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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
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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
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p
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
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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
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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
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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
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check database to verify predictions
Product Recovery: Crystallization
Chemical product: Ibuprofen
C id li llConsider cooling as well as drowning-out crystallization
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Product Recovery: Crystallization
Chemical product: Ibuprofen
Consider cooling as well as drowning-out crystallization
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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-
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Case Study – Polymer Design: MINLP Applied to CAMD
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 )
-CH2- ; -CO- ; -COO- ; -O- ; -CONH- ; -CHOH-; -CHCL-
Note: objective function and constraints are non linear; nk, the optimization variables are integer (0-9)
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Case Study – Polymer Design: MINLP Applied to CAMD
Fobj = [(Pi – Pi*)/Pi
*]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
-CH2- ; -CO- ; -COO- ; -O- ; -CONH- ; -CHOH-; -CHCL-
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)
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optimization variables are integer (0-9)
Case study – Polymer Design: Reformulate - I
Fobj = [(Pi – Pi*)/Pi
*]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
-CH2- ; -CO- ; -COO- ; -O- ; -CONH- ; -CHOH-; -CHCL-
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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
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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
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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
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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
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1 2
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
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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
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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
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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)
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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
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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?
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( g y) g10 f
Other chemical product design problems – 1d
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: 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
Workshop on Product Design, NTUA, March 2011 70
solutions w.r.t. C to identify the best candidate; check for solubility in water
Other chemical product design problems – 1e
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 )
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
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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
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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
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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
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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
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Other chemical product design problems: 2eP bl St t t 2Problem Statement-2
(Construct backbones & terminate with ProCamd):Use Corticosterone as an
example
Step 6: Fill-out the necessary problem definition pages in ProCamd (general
bl t lproblem control, non-temperature dependent properties)
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Other chemical product design problems: 2fP bl St t t 2Problem Statement-2
(Construct backbones & terminate with ProCamd):Use Corticosterone as an
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.
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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
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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
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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
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Other chemical product design problems: 3d
Problem Statement-3 (Generate backbone
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.
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Other chemical product design problems: 3e
Problem Statement-3 (Generate backbone
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.
Step 5: Complete the backbone termination problem with ProCamd.
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Other chemical product design problems: 3f
Problem Statement-3 (Generate backbone
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.
Step 5: Complete the backbone termination problem with ProCamd.
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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.
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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
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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 )
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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
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