Introduction to chemical product design
design,
molecular design problem definition & role of
property
t modelling
d lli
Rafiqul Gani
CAPEC
D
Department
t
t off Chemical
Ch i l E
Engineering
i
i
Technical University of Denmark
DK-2800 Lyngby,
y g y Denmark
Overview - 1
P bl
Problem
Definition
D fi iti
Process Design
Raw
material
Product Design
Products
Process
Fl
Flowsheet?
h t?
Operating
condition?
Product
properties
Equipment
parameters?
Product
molecule/
mixture?
Product
functions?
Product Application Design
Product
Application
process?
Application
conditions?
Atoms or
molecules
Molecule or
mixture
synthesis?
Performance
Equipment
parameters?
Integrate Process-Product
& Application
Workshop on Product Design, NTUA, March 2011
2
Overview - 2
E
Examples
l off Product-Process
P d tP
Integration
I t
ti
Process Design
Raw
material
Product Design
Process
Fl
Flowsheet?
h t?
Operating
condition?
Products
Equipment
parameters?
Product
properties
Product
molecule/
mixture?
Product
functions?
Atoms or
molecules
Molecule or
mixture
synthesis?
•Petroleum
P t l
blends
bl d & petroleum
t l
products
d t
Routinely solved!
•Polymers & blends
Can be solved!
•Specialty chemicals (including drugs, ….)
Process role?
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Overview - 3
E
Examples
l off Product-Process
P d tP
Integration
I t
ti
For products
F
d t such
h as
drugs, pesticides, food, …
delivery and application is
important & needs to be
validated
Product Design
Product
properties
Product
molecule/
mixture?
Product
functions?
Product Application Design
Product
Application
process?
Application
conditions?
Atoms or
molecules
Molecule or
mixture
synthesis?
Performance
Equipment
parameters?
Production process
needs to be reliable – first
time right is important
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Course Content
• Different types of design problems
•Modelling issues
pp
•Solution Approaches
• Concepts
• Single molecule design
• Mixture (blend) design
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Different Types of Design Problems
1 Design of Molecule and/or Mixture
1.
Given, a set of target properties , find molecules or
mixtures that match the target properties CAMD & CAMbD
solvents,
l
t fluids,
fl id …
d
drugs,
pesticides,
ti id
aroma, ….
Low
L
Large
Value
High
Production rate
Small
Easy
Finding candidates
Difficult
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Different Types of Design Problems
1 Design of Molecule and/or Mixture
1.
CAMD & CAMbD: Example
From a list of 250 solvents, find those that can be used as an oilpaint additive characterized by 25%-, 50%- & 75%-evaporation
rate, solubility, viscosity and surface tension. Form the feasible
set, find the optimal cost solvent mixture.
• Sy
Synthesis
es s method
e od for
o mixtures
u es
• Property models for solvents
• Evaporation models for solvents
• Cost = f(Ci, xi)
Note: Mixture design similar to oil-blend (petroleum,
(petroleum
edible oil, ….), polymer blend, formulations, ….
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Different Types of Design Problems
1 Design of Molecule and/or Mixture
1.
Given, a set of target properties , find molecules or
mixtures that match the target properties CAMD & CAMbD
solvents,
l
t fluids,
fl id …
d
drugs,
pesticides,
ti id
aroma, ….
Issues & Needs
• Generate feasible candidates (synthesis method & tool)
• Estimate
E ti t target
t
t properties
ti (predictive
( di ti property
t models)
d l)
• Evaluate performance (application process models)
A h i ett al,
Achenie
l Computer
C
t Aided
Aid d M
Molecular
l
l D
Design:
i
Th
Theory & Practice,
P
ti
CACE
CACE-12,
12 2003
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Different Types of Design Problems
2 Product
2.
Product-Process
Process Evaluation
Given, a list of feasible candidates (product and/or process), the
objective
bj ti is
i to
t identify/select
id tif / l t the
th mostt appropriate
i t product-process
d t
based on a set of performance criteria.
For product design,
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 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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Different Types of Design Problems
2 Product
2.
Product-Process
Process Evaluation
Simple Example: From the derivatives of Barbituric acid, identify the
candidate
did t that
th t is
i required
i d in
i the
th smallest
ll t amountt (C) tto produce
d
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 Problems
2 Product
2.
Product-Process
Process Evaluation
Process
Analytical
Technology
– PAT
To ensure
final product
p
quality!
Estimate
critical
iti l
scientific &
regulatory
parameters
early!
A. S. Hussain,
CDER, FDA, 2002
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Different Types of Design Problems
3 Design of Product
3.
Product-centric
centric Process
Problem Characteristics
•
•
•
•
•
Products are consumer
products such as soap,
detergents, fragrances, food,
skin-care
ki
…
Complex chemical systems
involved
Life of the product is getting
shorter
Volume/cost relationship
p
needs to be improved by
selling more
Low-cost chemical routes are
needed (better catalysts,
faster reactions, increased
yields, …)
Issues & Needs
Algorithm for generation of
process flowsheet
fl
h t or
batch recipe
Process-operation models for
verification by simulation
Property models for
product/process analysis
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Framework for Integrated Approach - 1
Integration of Product-Product
Product Product Design
hwax
hcuticle
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Role of property modelling
Prediction
P
di ti and
d measurementt off
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
mosquito,
it h
has a pleasant
l
t smell,
ll a good
d skin
ki
feeling and is a water-based product (active
ingredient water
ingredient,
water, solvents
solvents, additives)
Properties?The above product attributes,
when translated,
translated means that target values
on the following properties need to be
p
time,, phase
p
split,
p ,
matched: 90% evaporation
solubility, viscosity, molar volume, toxicity,
etc., & cost
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Motivation – II: Chemical processes
E
Example-2:
l 2 Design
D i
off a chemical
h i l 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 manner
manner.
Properties? Wide range of properties needed to
establish
t bli h the
th chemical
h i l product
d t (pure
(
compound
d and/or
d/
mixture properties) and the process (how the chemical
properties change under conditions of temperature,
pressure and/or composition).
Monomer A
Separator
effluent
Monomer B
Solvent (S)
Initiator (I)
MIXER
Transfer
agent (T)
Inhibitor (Z)
REACTOR
SEPARATOR
Copolymer
product
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Challenges & Issues
Wide range of products & processes must be handled
Nutrition
.....
Fertilizers
Vitamins
Healthcare
Electronics
Chemical
Processes
Communications,
Entertainment
Fuel
Mobility
Pharmaceuticals
Plastics
Colorants
and
Detergents
coatings
Clothing
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Housing
17
Challenges & Issues
How tto provide
H
id the
th necessary property
t values
l
in
i
a fast, efficient and reliable way?
Software
tools:
models; algorithms;
databases
Chemical
systems:
organic; polymers;
electrolytes; ..
Properties:
pure compound
& mixture
i t
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Challenges & Issues
Properties for product-process design*
Databases: collection
of measured data
(there are gaps in the
database)
Property models: fillin 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
Property models
Monomer A
Separator
effluent
Monomer B
Log Pi = Ai + [Bi/(Ci + T)]
Property models
are embedded into
process models
Process models
Solvent (S)
Initiator (I)
MIXER
dm
dt
T
Transfer
f
agent (T)
Inhibitor (Z)
REACTOR
i

f in ,i 
f o u t ,i  r ( m , T , P )V ; i  1 , N C
SEPARATOR
Operation models
Process models
Property-kinetics
models
Cost models
M
Models
s for
su
ustaina
ability
metrics
Models fo
or
ntal
envirronmen
im
mpact
Copolymer
product
Workshop on Product Design, NTUA, March 2011
Formulation
process
model
Product
evaluation
model
20
Property Modelling Concept
Predictive: Group contribution plus atom
connectivity
y for p
primary
yp
pure component
p
properties and mixture properties (GE-models*,
viscosity,
y surface tension, EOS ((PC-SAFT)) ...))
Correlated: Secondary pure component
properties (Antoine
(Antoine, DIPPR
DIPPR-functions,
functions equations
of state, ....) and mixture properties (GE-models**,
viscosity surface tension
viscosity,
tension, EOS (SRK
(SRK, PR)
PR), ...))
* UNIFAC ((different versions))
** NRTL, UNIQUAC, Wilson, ....
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Property Model Development – GC concept
Organic Chemical & Polymer Properties
1st-order: CH3 , CH2, CH,
OH, COOH, CH3CO, ….
3
5
1
2nd-order: (CH3)2CH,
CH ..
3rd-order: HOOC-(CHn)mCOOH (m>2, n in 0..2)
groups
g
p
4
2
v
0
v
1
v
2
connectivity
y index
REPRESENTING DATA SET AS GROUPS & ATOMS
EXPERIMENTAL DATA SET COLLECTION
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Property Model Development
Organic Chemical & Polymer Properties
DETERMINING ATOM or
GROUP CONTRIBUTIONS
MARRERO
O / GANI
G
GC-method
GC et od (2001)
( 00 )
f(y) = ∑niCi + w∑mjDj + z∑okEk + F
MINIMIZATION OF SUM OF
SQUARED RESIDUALS
( experimental – estimated)
Atom-CI
to C Model
ode (2006)
( 006)
f(y)=∑aiAi + b(vχ0) + 2c(vχ1) +
2c(vχ2) + D
Note: f(y) is function of property x; example f(y) = Exp(Tb /tbo)
MODEL DEVELOPMENT
REPRESENTING DATA SET AS GROUPS & ATOMS
EXPERIMENTAL DATA SET COLLECTION
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Property prediction (pure component)
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Property prediction (pure component)
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Property prediction (pure component)
Equations of
state: SRK,
PR, PCSAFT, ..
SAFT
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Property prediction: organic chemicals & polymers
Modelling options for primary properties
GC model + connectivity indices
Gcplus concept
f (Y )   NiCi  f (Y )  higher order
*
i
Generation
G
ti off
missing groups for
pure compound GC
models[1]
GC+ models
GC model +
experimental data
Group contribution based
models
GC model (MG, CG, JR, W, …)
Gani, R.; Harper, P.M.; Hostrup, M. Automatic Creation of
Missing Groups through Connectivity Index for Pure-Component
Property Prediction.
Prediction Ind
Ind. Eng
Eng. Chem.
Chem Res.,
Res 2005,
2005 44,
44 7269
7269.
GC model + force field (MM2)
Higher order models for
structure distinction (cistrans isomers)
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GC+ concept for mixture property prediction
UNIFAC GC models
Compound 1
Compound 2
lni ln
COM
i
lni lni
RES
RES2
1. Given: Compounds, compositions & temp.
2. Represent molecules with 1st-order
1st order & 2nd2nd
order groups
3. Retrieve group parameters
4. Calculate activity coefficients in liquid
phase
5. .......
Equlibrium
q
systems:
y
VLE, LLE, SLE, VLLE, SLLE, SLVE
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The UNIFAC-CI Concept
Methodology to fill the blanks in the UNIFAC matrix
A lot of gaps in
th UNIFAC matrix!
the
ti !
VLE, SLE, LLE
experiments to
collect data
Filling the UNIFAC
matrix jjust
with the published
VLE, SLE and LLE
data
GC model for Atom and bond
Based information
Mixtures
(UNIFAC) (connectivity indices)
Sometimes time
consuming and
expensive and
even not possible
By using just atom and
bond information
Of the groups
describing
g the
molecules
•Without any extra experimental data
•With reasonable accuracyy
UNIFAC-CI
Gonzales et al , Prediction of Missing UNIFAC Group-interaction, Parameters through connectivity
indices, AIChE J (2006); FPE (2010-submitted)
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Predict Missing UNIFAC Group Interaction Parameters
CH2
C=C
C≡C
ACH
ACCH2
OH
CH30H
ACOH
DOH
H2O
CH2CO
CHO
Furfural
CCOO
HCOO
CH2O
COOH
CNH2
CNH
(C)N3
CCN
ACNH2
Pyridine
CNO2
ACNO2
DMF
ACRY
CF2
ACF
CCl
CCl2
CCl3
CCl4
ClCC
ACCl
Br
I
CS2
CH3SH
DMSO
COO
SiH2
SiO
NMP
CClF
CON
OCCOH
CH2S
Morpholine
Thiophene
C,O,H atoms (current state)
adding N atoms
adding F atoms
adding Cl atoms
adding Br atoms
adding I atoms
adding S atoms
adding Si atoms
Potentially all the
group
g
interactions
parameters
could be covered
using
UNIFAC-CI !
adding Cl & F atoms
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Property prediction: UNIFAC-CI (VLE)
MethylcyclopentaneBenzene 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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Property prediction software
S it d ffor product-process
Suited
d t
d i /
design/analysis
l i
MoT
Introduce
new models
to the
system
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Database development
*K
Knowledge
l d representation
t ti
(developed
(d
l
d ontology)
t l
)
•Classes ((chemicals;; AIs;; Aromas;; Solvents;; ....))
•Sub-classes (organic, polymers, inorganic, ..)
•Instances
I t
(alcohols,
( l h l ketones,
k t
paraffins,
ffi
..))
•Objects-A
j
(property
(p
p y types:
yp
primary,
p
y, ...))
•Frames (property values: Tb, Tc, Tm, ...)
•Objects-B (applications: solvents, ....)
* Search engine
g
for data-retrieval ((forward and/or
reverse)
R. Singh et al. ”Design of an efficient ontological knowledge based system for selection of
process monitoring and analysis tools”,
” C
Computers & C
Chemical Engineering, 2010
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Database: Contents & features
• Pure compound data: primary properties
• Binaryy mixture data: VLE and infinite dilution ((in
water)
• Solid solubility data (C4 – C60)
• Special databases
• Solvents
• Active ingredients
• Aroma
o a
• Agents (neutralizers, emulsifiers, ....)
• Search engines & data retrieval interface
Example of a lipid database and associated property models
(Diaz-Tovar
(Diaz
Tovar et al
al., FPE
FPE, 2010)
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Hierarchy of property models
U a more predictive
Use
di ti model
d l tto predict
di t th
the missing
i i parameter
t
Correlations
Pi = Ai + [Bi/(Ci + T)]
Molecular
CH3-; -CH2-; -OH; …..
Zc = (Pc*Vc)/(83.14*Tc)
Groups
C, H, O, N, S, ….
Tb = 222.543*log(Sum.Groups.I +
Sum.Groups.II + Sum.Groups.III)
Atoms
Use the smaller scale (atoms)
model to predict the missing
parameters of the larger scale (GC)
P=∑niPi + b(vχ0) + 2c(vχ1)
Micro
Accuracy (verification)
Predictive power (design)
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Increasing application range of property models
Accuracy
y (q
(quantitative))
Pi = Ai
+ Bi
+ Ci
+ Di
Property
Models
E
Electrolyte
(simple small molecules
& mixtures)
Higher-order models (plus
Poly
ymers
gg more complex molecules & mixtures))
bigger
GC+ models (plus isomers & more complex
GC
molecules & systems)
Orrganic
M lti
Multi-scale
l models
d l (plus more complex systems and
structured chemicals)
A li ti range (qualitative)
Application
(
lit ti )
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Molecular Design (single molecule)
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Chemical product design framework
Essential,
E
ti l
desirable
and EH&S
properties
Build
molecules &
mixtures;
verify their
properties
Product &
process
evaluation
Identify processes
that can
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 MINLP
MINLP means Mixed Integer Non Linear Programming –
optimization problems formulated as MINLP contains
integer
g ((Y)) and continuous variables ((x, y) as optimization
p
variables, and, at least the objective function and/or one of
the constraints is non-linear.
Fobj = min {CTY + f(x, y, u, d, θ ) + Se + Si + Ss + Hc + Hp}
( , x,, y, d,, u,, θ))
P = P(f,
Process*/product
p
model
0 = h1(x, y)
Process/product constraints
0  g1(x,
( u, d)
“Other” (selection)
constraints
0  g2(x, y)
B x + CTY  D
Alternatives (molecules; unit
operations; mixtures;
fl
flowsheets;
h t ….))
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2. Product Design: CAMD Framework
• Solutions of
CAMD problems
are iterative
iterative.
Candidate
• Problem
selection
formulation
controls the
success.
• Essential
E
ti l
qualities vs.
Finish
((un)desirable
)
qualities.
• Connectivity with
external
t
l tools,
t l
data and
methods.
(final verification/
comparison
p
using rigorous
simulation and
experimental
procedures)
Start
No suitable
solutions (due to
performance,
economic or
safety concerns)
Promising
candidates have
been identified
Problem
formulation
(identify the
goals of the
design operation)
Method and
constraint
selection
Result
analysis and
verification
(specify the
design criteria
based on the
problem
formulation)
(analyse the
suggested
compounds
using external
tools)
Workshop on Product Design, NTUA, March 2011
CAMD
Solution
(identify
compound
having the
desired
properties)
41
CAMD General Problem Solution
3
3-stage
Iterative
i Solution
S
i Approach
A
1. Set Goals ((Pre-Design
g Stage)
g ) – Define Needs
•
•
•
Essential properties
Desirable properties
Safety Environmental
Safety,
Environmental, etc.
etc
2. Design/Selection (Design Stage) – Generate
Alternatives
•
•
•
Search
S
h through
th
h database
d t b
Iteratively build molecules/mixtures, comparing to goals
• Generate set of feasible molecules/mixtures
Simultaneously build and evaluate molecules/mixtures
• Identify optimal molecule/mixture
• Identify
y feasible set
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 have
19.5 > Sol Par < 20.5
Solution:
S
l ti
U a search
Use
h engine
i within
ithi a database
d t b
tto
identify the set of feasible molecules
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CAMD Problem Solution: Enumeration
Multi-level generate & test according to a predefined sequence
Pre-design
g
"I want acyclic
alcohols, ketones,
aldeh des and ethers
aldehydes
with solvent properties
similar to Benzene"
Interpretation to
input/constraints
Design
g ((Start))
A set of building blocks:
CH3, CH2, CH, C, OH,
CH3CO, CH2CO, CHO,
CH3O CH2O
CH3O,
CH2O, CH
CH-O
O
+
A set of numerical
constraints
A collection of group
vectors like:
3 CH3, 1 CH2, 1 CH,
1 CH2O
All group vectors
satisfy constraints
Design (Higher levels)
2.order
group
CH2
CH3
CH2
O
CH3
CH
CH3
CH3
Group from
other GCA
method
CH2
CH3
CH3
CH
O
CH2
Refined property
estimation. Ability to
estimate additional
properties or use
alternative methods.
Rescreening against
constraints.
Workshop on Product Design, NTUA, March 2011
Start of Post-design
CH2
CH3
CH2
O
CH3
CH
CH3
CH3
CH2
CH3
CH3
CH
O
CH2
44
Example of problem solution with ProCAMD
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Integrated Product-Process Design
Problem: A process stream of 50 mole% Acetone and 50
mole% Chloroform at 300K, is to be separated.
Separation
p
techniques
q
considered:
h
Adsorption (liquid, gas)
Crystallization
Desublimation
Distillation – simple
Distillation – extractive
Distillation with decanter
Liquid-liquid extraction
Flash/evaporation
Membrane (g
(gas,, liquid)
q )
Microfiltration
Partial condensation
N external
No
t
l medium
di
known
k
Binary ratios of properties
identify the following
alternatives
Separation techniques:
Distillation – simple
Distillation – extractive
Distillation – azeotropic
Liquid extraction
Pressure swing
Note: Acetone-chloroform forms a high boiling azeotrope
that is ppressure sensitive
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Pressure dependence
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Solvent design sub-problem
•
•
•
•
•
CAMD problem:
340 < Tb < 420
Selectivity
y > 3.5
Solvent power> 2.0
No azeotropes
Solution:
1-Hexanal
y
p
y ether
Methyl-n-pentyl
(Benzene)
– Number of compounds designed: 47792
Number of compounds selected: 53
– Number of isomers designed: 528
Number of isomer selected: 23
– Total time used to design: 57.01 s
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Phase behaviour
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Problem formulation & Solution
Objective function:
Maximize
Profit = Earnings
– Solvent cost
– Energy costs
Constraints:
Results:
Acetone purity > 0.99
S l
Solvent
t
S l
Solvent
t
Chloroform purity
0.98
flow>rate
1-hexanal 0.082 kmol/hr
Reflux
R
fl
Reb. 1
0.45
Reflux
R
fl
Reb. 2
0.65
Workshop on Product Design, NTUA, March 2011
Objective
Obj
ti
function
2860.51 $/hr
50
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:
gy Define CAMD p
problem to ggenerate solvent
candidates; verify performance through solubility calculations;
check database to verify predictions
Workshop on Product Design, NTUA, March 2011
51
Product Recovery: Crystallization
Chemical product: Ibuprofen
Consider
C
id cooling
li as well
ll as
drowning-out crystallization
Find solvents, anti-solvents & their mixture that satisfy the
following:
Potential recovery > 80%
Solubility parameter > 18 MPA1/2 (or > 30)
H d
Hydrogen
b di solubility
bonding
l bilit parameter
t > 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
Workshop on Product Design, NTUA, March 2011
52
Product Recovery: Crystallization
Chemical product: Ibuprofen
Consider
C
id cooling
li as well
ll as
drowning-out crystallization
Workshop on Product Design, NTUA, March 2011
53
Product Recovery: Crystallization
Chemical product: Ibuprofen
Consider cooling as well as
drowning-out crystallization
Workshop on Product Design, NTUA, March 2011
54
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 =  [(Pi – Pi*)/Pi*]2
Fobj
s.t.
Tg  373 K
Density  1.5
1 5 g/cm3
Water absorption = 0.005 g H2O/g polymer)
-CH2- ; -CO- ; -COO- ; -O- ; -CONH- ; -CHOH-; -CHCL-
Workshop on Product Design, NTUA, March 2011
55
Case Study – Polymer Design: MINLP Applied to CAMD
Fobj =  [(Pi – Pi*)/Pi*]2
select property models
st
s.t.
Tg = [ (Yknk)]/ [ (Mknk)]  (373  20) K
Density = [ (Mknk)]/ [ (Vknk)]  (1.5  0.1) g/cm3
W t absorption
Water
b
ti = [ (Hknk)]/ [ (Mknk)]
 (0.005 0.0005)) g H2O/gg p
polymer)
y
)
-CH2- ; -CO- ; -COO- ; -O- ; -CONH- ; -CHOH-; -CHCLNote: objective function and constraints are non linear; nk,
the optimization variables are integer (0-9)
Workshop on Product Design, NTUA, March 2011
56
Case Study – Polymer Design: MINLP Applied to CAMD
Fobj =  [(Pi – Pi*)/Pi*]2
st
s.t.
353 K < Tg = [ (Yknk)]/ [ (Mknk)] < 393 K
1.4 <  = [ (Mknk)]/ [ (Vknk)] < 1.5 g/cm3
0 0045 < W = [ (Hknk)]/ [ (Mknk)] < 0.0055)
0.0045
0 0055) g H2O/g polymer)
2  yj  3
2  yknk  9
;
yj : 0 or 1 for j=1,7
add structural constraints
-CH2CH2 ; -COCO ; -COOCOO ; -OO ; -CONHCONH ; -CHOH-;
CHOH ; -CHCLCHCL
Note: objective function and constraints are non linear; nk, the
optimization variables are integer (0-9)
(0 9)
Workshop on Product Design, NTUA, March 2011
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Case study – Polymer Design: Reformulate - I
Fobj =  [(Pi – Pi*)/Pi*]2
st
s.t.
353 K < Tg = [ (Yknk)]/ [ (Mknk)] < 393 K
1.4 <  = [ (Mknk)]/ [ (Vknk)] < 1.5 g/cm3
0 0045 < W = [ (Hknk)]/ [ (Mknk)] < 0.0055)
0.0045
0 0055) g H2O/g polymer)
50 <  (Mknk) < 100
2  yj  3
add a new constraint
;
yj : 0 or 1 for j=1,7
2  yknk  9
-CH2- ; -CO- ; -COO- ; -O- ; -CONH- ; -CHOH-; -CHCLWorkshop on Product Design, NTUA, March 2011
58
Case study – Polymer Design: Reformulate – II (MILP)
reformulate Fobj & constraints
Min Fobj = M
st
s.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 = [ (Hknk)] < 0.0055*100
0.0045*50
0 0055*100 g H2O/g polymer)
LB1  yj  UB1
;
yj : 0 or 1 for j=1,7
LB2  yknk  UB2
-CH2CH2 ; -COCO ; -COOCOO ; -OO ; -CONHCONH ; -CHOH-;
CHOH ; -CHCLCHCL
14
; 28 ;
44
; 16 ;
43
;
30
Workshop on Product Design, NTUA, March 2011
; 48.5
59
MINLP Applied to CAMD: Exercise 2
G
Generate
t polymers
l
that
th t match
t h the
th ffollowing
ll i target:
t
t
Total number of
different main
groups:
5 for Problem 1
4 for Problem 2
4 for Problem 3
2  yjnj  5
;
yj : 0 or 1 for j=1,4
;
yk: 0 or 1 for k=5,8
m=(v
( j -2)n
) j
0  yknk  m
Workshop on Product Design, NTUA, March 2011
60
MINLP Applied to CAMD: Exercise 3
G
Generate
t molecules
l l that
th t match
t h the
th following
f ll i target:
t
t
Min f=Hfus
180 < Tm < 225 K
400 < Tb < 440 K
-CH3 ; -CH2NO2; -CH3CO; -OH; -CH2=CH
-CH2-CHCH
yjnj = 4
;
yj : 0 or 1 for j=1,7
2  vjnj  3
;
yj : 0 or 1 for j=1,5
0  yknk  2
;
yk: 0 or 1 for k
k=6
6
0  ylnl  1
;
yl: 0 or 1 for l=7
Workshop on Product Design, NTUA, March 2011
61
MINLP Applied to CAMD: Exercise-4
G
Generate
t molecules
l l that
th t match
t h the
th following
f ll i target:
t
t
Min f= Cixi
LB1 < Tm = Tmi xi < UB2
LB2 < Vb = f(T,
f(T Tc, Pc, Zc, x) < UB2
Comp1, Comp2, Comp3, …………..CompN
yj= 2
;
yj : 0 or 1 for jj=1
1,N
N
0 < x1 < 1
0 < x2 < 1
x1 + x2 = 1
Workshop on Product Design, NTUA, March 2011
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Exercise Problems
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63
Tutorial Exercises
• Analyze the drug Ibuprofen (define the
needs
d ffor iimproving
i process efficiency
ffi i
in
i
extracting the product from solution in
the reactor) – use ProPred
• Use ProCamd to match the list of solvents
and anti-solvents for Ibuprofen
• Solve
S l the
h organic
i synthesis
h i problems
bl
with
ih
ProCamd
See slides 53-55
Workshop on Product Design, NTUA, March 2011
64
Exercises
• Analyze the drug Ibuprofen (define the
needs for improving
p
gp
process efficiency
y in
extracting the product from solution in
the reactor) – use ProPred
– Search compound in database
• start ICAS
• click on M, select basic search, search with CASnumber
b (015687
(015687-27-1),
27 1) copy SMILES
– Start ProPred
• import SMILES, analyze properties
Workshop on Product Design, NTUA, March 2011
65
Exercises
• Use ProCamd to match the list of solvents
and anti-solvents for Ibuprofen
– Formulate problems (solvent & anti
anti-solvent)
solvent)
see slides 53-55)
• Start ProCamd
– Specify problem (ProCamd manual)
– Start execution
– Analyze results
Workshop on Product Design, NTUA, March 2011
66
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 (binding
to bovine serum albumin)
Barbituric Acid
(000077-02-1)
Workshop on Product Design, NTUA, March 2011
67
Other chemical product design problems – 1b
Problem Statement
Statement-1:
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 coefficient
of the candidate compounds: Log10(1/C) = 0.58 log10P + 0.239
Workshop on Product Design, NTUA, March 2011
68
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
p
with protein
p
(binding
(
g to bovine serum albumin))
Barbituric Acid (000077-02-1)
(000077 02 1)
Calculate C as a function of octanol-water partition coefficient of the
candidate compounds: Log10(1/C) = 0.58
0 58 log10P + 0.239
0 239
Question: How is the backbone (barbituric acid) identified and how is
the relation ((drugg activity)
y) between C vs log
g10P ffound?
Workshop on Product Design, NTUA, March 2011
69
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
p
with protein
p
(binding
(
g to bovine serum albumin))
Barbituric Acid (000077-02-1)
(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
Solution Steps: Generate candidates; calculate LogP; calculate C; order
solutions w.r.t.
w r t C to identify the best candidate; check for solubility in water
Workshop on Product Design, NTUA, March 2011
70
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
p
with protein
p
(binding
(
g to bovine serum albumin))
Solution Steps
•Generate candidates (check database)
•Use ProPred to calc LogP and LogWs
•Calculate C
Barbituric Acid (000077-02-1)
(000077 02 1)
•Order solutions with respect
p to C
Calculate C as a function of octanol-water partition coefficient of the
candidate compounds: Log10(1/C) = 0.58 log10P + 0.239
Candidates (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
Workshop on Product Design, NTUA, March 2011
71
Other chemical product design problems – 2a
Problem 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 to
ProCamd, where terminated structures are generated (see page 53 of tutorial document)
Step 1: Start ProPred from ICAS
Step 2: Click on (database) , click on “Find CAS”, type the CAS Number for
Corticosterone (00005-22-6), select the compound by clicking on OK
Step 3: ProPred draws the molecule and predicts the properties
Step 4: Remove the OH group connection from the molecular structure at the
two locations
Step 5: Launch ProCamd from ProPred from the tools menu in ProPred.
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.
Workshop on Product Design, NTUA, March 2011
72
Other chemical product design problems: 2b
Problem Statement
Statement-2
2 (Construct backbones & terminate with
ProCamd): Use Corticosterone as an example
Step 2
St
2: Cli
Click
k on (d
(database)
t b
) , click
li k on “Find
“Fi d CAS”,
CAS” ttype
the CAS Number for Corticosterone (00005-22-6), select
the compound by clicking on OK
Workshop on Product Design, NTUA, March 2011
73
Other chemical product design problems: 2c
Problem
P
bl
Statement-2
St t
t2
(Construct backbones &
terminate with ProCamd):
Use Corticosterone as an
example
Step 3: ProPred draws
the molecule and
predicts the properties
Workshop on Product Design, NTUA, March 2011
74
Other chemical product design problems: 2d
Problem
P
bl
Statement-2
St t
t2
(Construct backbones &
terminate with ProCamd):
Use Corticosterone as an
example
Step
p 4: Remove the OH
group connection from
the molecular structure at
the two locations
Backbone with two-free attachments.
Note that as ProCamd generates
g
molecular 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
Workshop on Product Design, NTUA, March 2011
75
Other chemical product design problems: 2e
Problem
P
bl
Statement-2
St t
t2
(Construct backbones &
terminate with ProCamd):
Use Corticosterone as an
example
Step 6: Fill-out the
necessary problem
definition pages in
ProCamd (general
problem
bl
control,
t l nontemperature dependent
properties)
Workshop on Product Design, NTUA, March 2011
76
Other chemical product design problems: 2f
Problem
P
bl
Statement-2
St t
t2
(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 properties
ProPred
ti and
d database
d t b
search cannot be used.
Workshop on Product Design, NTUA, March 2011
77
Other chemical product design problems: 3a
Problem Statement
Statement-3
3 (Generate & terminate backbones):In this problem,
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
o ecu a st
structure
uctu e a
and
d work
o o
on tthe
e st
structure
uctu e without
t out terminating
te
at g the
t e
structure. 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 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.
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.
ProCamd
Workshop on Product Design, NTUA, March 2011
78
Other chemical product design problems: 3b
Problem Statement3 (Generate
backbone with
ProCamd &
terminate manually
with ProPred)
Step 1: Start
P C d and
ProCamd
d
specify the
following
backbone
generation
p
problem.
Note: at this stage specification of any other property is not necessary
Workshop on Product Design, NTUA, March 2011
79
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
alternatives.
l
i
Note
N
that
h for
f
the backbone generation,
“isomer” generation is not
allowed. For the problem
formulated in step 1, the
following
g result is obtained.
Workshop on Product Design, NTUA, March 2011
80
Other chemical product design problems: 3d
Problem Statement-3
(Generate backbone
with ProCamd &
terminate manually
with ProPred)
Step 3: Click on
ProPred to
transfer the
backbone
structure. ProPred
d
draws
the
th
structure and
calculates all
properties, as
shown below.
Workshop on Product Design, NTUA, March 2011
81
Other chemical product design problems: 3e
Problem Statement-3
(Generate backbone
with ProCamd &
t
terminate
i t manually
ll
with ProPred)
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.
Workshop on Product Design, NTUA, March 2011
82
Other chemical product design problems: 3f
Problem Statement-3
(Generate backbone
with ProCamd &
terminate manually
with ProPred)
ProCamd generates 74 terminated
structures out of which
compound
d 1 is
i Ibuprofen
Ib
f
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.
Workshop on Product Design, NTUA, March 2011
83
Other chemical product design problems: 4a
Problem Statement-4 (Design large molecules):
Design a large molecule having the following
p p
properties,
Mw > 300
Tb > 400 K
See page 61 of tutorial
document
Tm > 300 K
Solve the problem with ProCamd and then switch to
ProPred and further investigate the properties of the
l
large
molecule,
l
l including
i l di further
f h increase
i
off the
h size
i
of the molecule. Only the “general problem control”
and the “non-temperature
non-temperature depd props”
props need to be
specified.
Workshop on Product Design, NTUA, March 2011
84
Other chemical product design problems: 4b
Problem Statement-4 Design a large
molecule having the following
p p
properties,
Mw > 300; Tb > 400 K; Tm > 300 K
Solve the problem with
ProCamd and then switch
to ProPred.
Repeat the problem for acyclic
compounds
d and
d cyclic
li
compounds
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85
Exercises: summary
• Solve example problem 1 (find best AI)
• Solve example problem 2 (backbone
termination)
• Solve example problem 3 (backbone
generation & termination)
• Solve example problem 4 (generate large
molecules, pass to ProPred and then
analyze and further develop )
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86
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 be
applied correctly and efficiently
• Problems related to low
low-value
value products are easier
to solve than the high-value products
– The limiting factors for these problems are availability
of data and appropriate models
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