Property Prediction and CAMD
CHEN 4470 – Process Design Practice
Dr. Mario Richard Eden
Department of Chemical Engineering
Auburn University
Lecture No. 21 – Property Prediction and Computer Aided Molecular Design
March 26, 2013
Property Prediction 1:2
•
Motivation
–
–
–
•
Experiments are time-consuming and expensive.
How do we identify the components to investigate?
Components of similar molecular structure have been
found to have similar properties.
Group Contribution Methods
–
–
–
Predominant means of predicting physical properties for
new components.
Based on UNIFAC group descriptions
Large amounts of experimental property data has been
fitted to obtain the contributions of individual groups.
Property Prediction 2:2
•
Examples and Software
 H v  hvo   ni  hv1i
i
Vm  d   ni  v1i
i
Tbp  t bo  ln  ni  t b1
i
 Tbp
log10 VP  5.58  2.7
 T
1 .7



CAMD 1:3
Property prediction:
Given:
Information on
compound structure.
Obtained:
Properties of the
compound.
Computer Aided Molecular Design:
Given:
Information on
desired properties &
type of compound.
Obtained:
Compound structures
having the desired
properties.
CAMD 2:3
CAMD 3:3
•
Application Examples
–
Water/phenol system: Toluene replacement
–
Separation of Cyclohexane and Benzene
–
Separation of Acetone and Chloroform
–
Refrigerants for heat pump systems
–
Heat transfer fluids for heat recovery and storage
–
and many others
Aniline Case Study 1:7
•
Problem Description
–
•
During the production of a pharmaceutical, aniline is
formed as a byproduct. Due to strict product
specifications the aniline content of an aqueous solution
has to be reduced from 28000 ppm to 2 ppm.
Conventional Approach
–
–
–
–
Single stage distillation.
Reduces aniline content to 500 ppm.
Energy usage: 4248.7 MJ
No data is available for the subsequent downstream
processing steps.
Aniline Case Study 2:7
•
Objective
–
•
Investigate the possibility of using liquid-liquid
extraction as an alternative unit operation by
identification of a feasible solvent
Reported Aniline Solvents
–
Water, Methanol, Ethanol, Ethyl Acetate, Acetone
Property
CAS No.
Boiling Point (K)
Solubility Parameter (MPa ½ )
Aniline
62–53–3
457.15
24.12
Water
7732–18–5
373.15
47.81
Aniline Case Study 3:7
•
Performance of Solvent
–
–
–
–
–
–
•
Liquid at ambient temperature
Immiscible with water
No azeotropes between solvent & aniline and/or water
High selectivity with respect to aniline
Minimal solvent loss to water phase
Sufficient difference in boiling points for recovery
Structural and EH&S Aspects
–
–
–
No phenols, amines, amides or polyfunctional
compounds.
No compounds containing double/triple bonds.
No compounds containing Si, F, Cl, Br, I or S
Aniline Case Study 4:7
•
Results of Solvent Search
–
No high boiling solvents found
Also, higher and
branched alkanes
were identified as
candidates
Solvent
n-Octane
2-Heptanone
3-Heptanone
CAS No.
111–65–9
110–43–0
106–35–4
Aniline Case Study 5:7
Process Simulation
Aniline Laden Solvent
1
S1
S6
2
1
2
3
4
5
6
7
8
9
10
11
12
13
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
14
15
23
Regeneration Column
Aniline Laden W ater
Recovered Solvent
(25 Stages)
S3
Extraction Column
(15 Stages)
•
24
S2
Solvent
T1
T2
25
S5
S4
W ater (2 ppm Aniline)
Recovered Aniline
Aniline Case Study 6:7
•
Performance Targets and Results
–
–
–
Countercurrent extraction and simple distillation.
Terminal concentration of 2 ppm aniline in water phase.
Highest possible purity during solvent regeneration
Design Parameter
n-Octane 2-Heptanone 3-Heptanone
Solvent amount (mole)
2488.8
1874.0
1873.5
Solvent amount (kg)
284.3
214.0
213.9
Solvent amount (liter)
402.6
261.2
260.9
Solvent amount in water phase (mol)
0.0341
161.2
161.2
Solvent amount in water phase (ppm)
1
429
429
Aniline product purity (weight%)
100.00
100.00
100.00
Recovery of aniline from solvent (%)
100.00
99.95
99.99
Solvent loss (% on a mole basis)
0.00098
8.60
8.60
Energy consumption for solvent recovery
2.223
2.245
2.009
Aniline Case Study 7:7
Validation of Minimum Cost Solution
Solvent Usage
Energy Consumption
430
2300
410
2200
2100
370
350
2000
330
1900
310
1800
290
1700
270
250
1600
230
1500
150
10
30
50
70
90
Number of stages
110
130
Energy consumption (MJ)
390
Solvent usage (liter)
•
Oleic Acid Methyl Ester 1:3
•
Problem Description
–
Fatty acid used in a variety of applications, e.g. textile
treatment, rubbers, waxes, and biochemical research
–
Reported solvents: Diethyl Ether, Chloroform
Volatile
Flammable
•
Carcinogen
Goal
–
Identify alternative solvents with better safety and
environmental properties.
Oleic Acid Methyl Ester 2:3
•
Solvent Specification
–
–
–
•
Liquid at normal (ambient) operating conditions.
Non-aromatic and non-acidic (stability of ester).
Good solvent for Oleic acid methyl ester.
Constraints
–
–
–
–
–
Melting Point (Tm) < 280K
Boiling Point (Tb) > 340K
Acyclic compounds containing no Cl, Br, F, N or S
Octanol/Water Partition coefficient (logP) < 2
15.95 (MPa)½ < δ < 17.95 (MPa)½
Oleic Acid Methyl Ester 3:3
•
Database Approach (2 Candidates)
–
–
•
2-Heptanone
Diethyl Carbitol
CAMD Approach (1351 Compounds Found)
–
–
–
–
–
Maximum of two functional groups allowed, thus
avoiding complex (and expensive) compounds.
Formic acid 2,3-dimethyl-butyl ester
3-Ethoxy-2-methyl-butyraldehyde
2-Ethoxy-3-methyl-butyraldehyde
Calculation time approximately 45 sec on standard PC.
Property Based Design
•
Why Design Based on Properties?
–
–
–
–
–
•
Many processes driven by properties NOT components
Performance objectives often described by properties
Often objectives can not be described by composition
Product/molecular design is based on properties
Insights hidden by not integrating properties directly
Property Clusters
–
–
–
–
–
Extension to existing composition based methods
Reduces dimensionality of problem
Enables visualization of problem
Property estimation in molecular design via GC
Unifying framework for simultaneous solution
 j (PjM )   xs  j (Pjs )
Property Clusters 1:2
s 1
Normalized Property Operators
 j (Pjs ) by
Property clusters are conserved surrogate properties described
js  if ref
property operators, which have linear mixing rules,even
the operators
 j (Pjs )
themselves are nonlinear.
Property Operators
Property Operators
Ns
Ns
 j (PjM )   xs  j (Pjs )
 j (PjM )  
x  j (Pjs )
s 1 s
s 1
Normalized Property Operators
Normalized Property Operators
 j (Pjs )
js  refj (Pjs )
js   ref (P )
 jj (Pjsjs )
Augmented Property Index
Augmented Property Index
NP
NP
AU Ps    js
AU Ps  
 js
j 1
Augmented Property Index
R.H.S.
NP
AU Ps    js
j 1
Property Clusters
 js
C js 
AU Ps
Property Clusters 2:2
Feasibility
Feed Constraint
max
Pjmin
,sink  Pjs  Pj ,sink
Feasibility Region
Necessary Condition
Match clustering target
C
C2
Analysis has shown
that region boundary
can be described by 6
unique points.
0,1
2
0,2
Sufficient Condition
0.9
Match AUP value of
0.8
sink
0.1
0,9
0.2
0,8
0,3
0.3
0,7
0.7
0,6 0.4
0,4
0.5
0,5
0,6
True
Feasibility
Region
0,7
0.6
S2
0,5
0.6
0.4
0,4
0.7
S MIX
0,3
0.8
0,8
0.5
0.3
Sink
0,2
0.2
S1
0.9
0,9
C3
0,1
0,2
0,3
0,4
C 30,5
0.1
0,1
0.1
0,6
0.2
0,7
0.3
0,8
0.4
0,9
0.5
C1
0.6
0.7
0.8
0.9
(1min , 2min , 3max )
min
Linear Expression
for Mixing
2 Ternary Clusters
(1min , 2max , 3max
) (1min , max
2 , 3 )
( ,  ,  )
( ,  ,  )
max
1
max
2
min
3
max
1
min
2
min
3
(
C
max
1
j MIX
Ns
min
2
s 1
,,s3max
 C) js
, j  1,2,3
C1
Group Contribution Methods
• Group Contribution Methods (GCM) allow for prediction
of physical properties from structural information
• 1st order, 2nd order, and 3rd order groups are utilized to
increase the accuracy of the predicted properties
1st order
2nd order
3rd order
f ( X )   NiCi   M j Dj  Ok Ek
i
Property
Function
j
k
Group Contribution Terms
Molecular Clusters 1:5
Product
Process
Non-GC Based
Properties
GC Based
Properties
PropertyM Cluster
Framework
Property Targets
Molecular Property
Clusters
Molecular Property
Constraints
Molecular Design
Candidate Molecules
Molecular Clusters 2:5
M olecular Property
Operators
Process Property Operators
Ng
Ns
   xs  Pjs
P
j
 Mj   ng  Pjg
j
 j  ref
j
s 1
Li near Expressi on for
M i xi ng 2 Ternary
Cl usters
g 1
G 1 and G 2, are added l i nearl y
on the ternary di agram. The
l ocati on of 1, corresponds to
the l ocati on of G1-G 2 mol ecul e
Nj
AU P    j
j 1
j
Ns
C jMIX    s  C js
Cj 
s 1
1 
AUP
C2
0,1
0,9
0,2
0,8
0,3
0,7
0,4
0,6
0,5
0,5
0,6
0,4
True Feasibility
Region
0,7
0,3
0,8
0,2
0,9
C3
0,1
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
C1
n1  AUP1
n1  AUP1  n2  AUP2
Molecular Clusters 3:5
C2
1, the visualization arm,
corresponds to the location
of G1-G2 molecule
n1  AUP1
1 
n1  AUP1  n 2  AUP2
0,1
Feasibility
Necessary Conditions
1. Free bond number is zero.
2. Match clustering target
3. Match AUP range of sink
0,9
0,2
0,8
0,3
0,7
0,4
0,6
0,5
Sufficient Condition
Check property value with sink
including Non-GC properties
0,5
0,6
0,4
G2
0,7
0,3
Feasibility
Region
0,8
0,2
G1
0,9
M1
G4
0,1
2
G3
C3
C1
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
Molecular Clusters 4:5
Molecular
Synthesis
1:
2:
3:
4:
C2
0.1
0.9
0.2
0.8
0.3
0.7
0.4
0.6
3
0.5
0.5
2
0.6
TARGET
1
0.7
4
0.4
0.3
CH
3-(CH2)2-CH3N-COOH
0.8
0.2
0.9
C3
CH3
CH2
CH3N
COOH
0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
C1
Molecular Clusters 4:5
Molecular
Synthesis
1:
2:
3:
4:
5:
C2
0.1
0.9
0.2
0.8
0.3
0.7
0.4
CH3
CH2
CH3N
COOH
CH3N-COOH
0.6
3
0.5
0.5
5
2
0.6
TARGET
1
0.7
4
0.4
0.3
CH3-(CH2)2-CH3N-COOH
0.8
0.2
0.9
C3
0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
C1
Molecular Clusters 4:5
Molecular
Synthesis
1:
2:
3:
4:
5:
6:
C2
0.1
0.9
0.2
0.8
0.3
0.7
0.4
0.6
3
0.5
0.5
TARGET
1
0.7
5
2
6
0.6
4
0.4
0.3
CH3-(CH2)2-CH3N-COOH
0.8
0.2
0.9
C3
CH3
CH2
CH3N
COOH
CH3N-COOH
CH3-CH2
0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
C1
Molecular Clusters 4:5
Molecular
Synthesis
1:
2:
3:
4:
5:
6:
7:
C2
0.1
0.9
0.2
0.8
0.3
0.7
0.4
0.6
3
0.5
0.5
6
0.6
TARGET
1
0.7
5
7 2
4
0.4
0.3
CH3-(CH2)2-CH3N-COOH
0.8
0.2
0.9
C3
CH3
CH2
CH3N
COOH
CH3N-COOH
CH3-CH2
CH3-(CH2)2
0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
C1
Molecular Clusters 4:5
Molecular
Synthesis
1:
2:
3:
4:
5:
6:
7:
C2
0.1
0.9
0.2
0.8
0.3
0.7
0.4
0.6
3
0.5
0.5
6
0.6
TARGET
1
0.7
5
7 2
4
CH3
CH2
CH3N
COOH
CH3N-COOH
CH3-CH2
CH3-(CH2)2
0.4
0.3
CH3-(CH2)2-CH3N-COOH
0.8
0.2
0.9
0.1
C3
C1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1
Molecular Clusters 5:5
0.9
0.1
0.2
0.9
0.2
0.8
0.3
0.8
0.3
0.7
0.7
The
location of each 0.4
molecular
0.6
formulation is unique and
CH3O-CH2-CH2-CH2[-1]
CH3-CH2-CH2-CH2[-1]
0.5 addition
independent
of group
path
0.5
0.4
C2
CH3-CH2-CH2[-1]
0.1
0.9
0.8
0.3
0.7
0.7
CH3[-1]
CH2[-2]
0.2
0.9
CH3O[-1]
0.5
CH2[-2]
C3
0.6
0.2
0.7
0.8
0.4
0.5
0.6
0.7
0.8
0.9
CH3-CH2[-1]
0.4
CH3[-1]
0.7
CH3O[-1]
0.8
0.1
C0.3
1
0.2
0.4
0.5
0.3
CH2[-2]
0.6
0.2
0.7
0.8
0.9
0.9
C1
0.3
0.5
0.1
0.3
C3
0.2
0.3
CH2[-2]0.6
0.6
CH3-CH2-CH2-CH2-CH3O[0]
0.1
0.1
CH3[-1]
CH3-CH2-CH2-CH2[-1]
0.5
CH3O-CH2-CH2-CH2-CH3[0]
0.4
CH3[-1]
0.4
0.7
CH3-CH2-CH2[-1]
CH3O-CH2[-1]
0.8
0.3
0.8
CH3O[-1]
0.4
0.8
0.5
CH3O-CH2-CH2[-1]
0.7
0.9
CH3-CH2[-1]
0.4
0.3
0.3
0.6
CH3O-CH2-CH2-CH2[-1]
CH3O[-1]
0.9
0.2
0.4
0.6
0.1
0.6
CH3-CH2-CH2-CH2-CH3O[0]
CH3O-CH2-CH2-CH2-CH3[0]
0.4
0.2
0.5
0.5
C2
CH3O-CH2-CH2[-1]
CH3O-CH2[-1]
0.6
0
0.1
C3
C1
0.1
0.2
0.3
0.4
Formulation of Butyl methyl ether
CH3-CH2-CH2-CH2-CH3O
0.5
0.6
0.7
0.8
0.9
Example: Molecular Synthesis
• Blanket Wash Solvent Design
– Solved as MINLP by Sinha and Achenie (2001)
• Problem Statement
– Design blanket wash solvent for phenolic resin printing ink
– Molecules designed from 7 possible groups, with a max.
chain length of 7 groups
Property
Lower Bound
Upper Bound
Hv (kJ/mol)
20
60
Tb (K)
350
400
Tm (K)
150
250
VP (mmHg)
100
---
Rij
0
19.8
Blanket Wash Solvent 1:7
• Visualization limits problem to three properties
• Heat of vaporization, boiling and melting
temperatures are used, with vapor pressure and
solubility used as final screening properties
Property Prediction (GCM)
H v  hvo   g i  hvi
i
Tb  tbo  ln  g i  tb1i
i
Tm  t mo  ln  g i  t m1i
i
Molecular Property Operators
H v  hvo   ng  hv1
i
 Tb  N g
exp     ng  tb1
 tbo  g 1
 Tm  N g
exp     ng  t m1
 t mo  g 1
,Y ref = 20
,Y ref = 100
,Y ref = 7
Blanket Wash Solvent 2:7
C2
0.1
0.9
0.2
0.8
0.3
0.7
0.4
0.6
0.5
0.5
0.6
0.4
0.7
0.3
Feasibility
Region
0.8
0.2
0.9
0.1
C3
C1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Blanket Wash Solvent 3:7
C2
Molecular
Groups
0.1
0.9
0.2
G1: CH3
G2: CH2
G3: CH2O
G4: CH3O
G5: CH2CO
G6: CH3CO
G7: COOH
0.8
0.3
0.7
0.4
0.6
0.5
0.5
0.6
0.4
G1
0.7
G3
G5
0.8
0.3
G4 G2
G6
0.2
G7
0.9
0.1
C3
C1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Blanket Wash Solvent 4:7
C2
0.1
Candidate Molecules
0.9
0.2
0.8
0.3
0.7
0.4
0.6
0.5
M1 : CH2O-CH2-(CH2O)2
M2 : CH3-CH2-CH2O-CH3O
M3 : CH3-CH2-(CH2O)2-CH3
M4 : CH3O-(CH2)3-CH3
M5 : CH3O-CH2O-CH3O
M6 : CH2O-(CH2O)2-CH2O
M7 : CH3-CH2CO-CH3
M8 : CH3-(CH2)5-CH3
M9 : CH3CO-COOH
M10: CH3CO-(CH2)2-COOH
M11: CH3-CH2-(CH2O)2
0.5
M2
M3
0.6
0.4
M1
0.7
0.3
M6
M8
0.8
0.2
M4
M5
0.9
M11
M10
M9
0.1
M7
C3
C1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Blanket Wash Solvent 5:7
• Feasible formulations from Visual Synthesis
Formulations
AUP
Hv
kJ/mol
Tb
K
Tm
K
VP
mmHg
Rij
MPa
M1
M2
M3
M4
M5
M6
M7
M8
M9
M10
M11
3.20
3.08
3.10
3.17
3.47
3.61
3.17
3.28
6.79
7.79
7.83
33.91
33.99
34.67
35.81
36.15
36.74
35.10
38.31
68.87
78.17
74.75
359.14
355.34
364.49
363.09
370.61
382.51
354.80
379.07
457.92
494.54
535.55
201.24
189.86
183.38
186.26
211.95
216.49
193.13
175.55
286.76
297.68
292.08
1117.85
1240.95
963.57
1001.85
811.54
578.05
1259.28
638.03
56.69
16.55
3.86
10.88
15.39
12.83
18.09
15.82
11.31
16.84
19.77
13.83
12.68
11.33
• Application of feasibility conditions
– All formulations satisfy the first two necessary
conditions
– M9-M11 fail to satisfy the AUP range of the sink
Blanket Wash Solvent 6:7
• Feasible formulations from Visual Synthesis
Formulations
AUP
Hv
kJ/mol
Tb
K
Tm
K
VP
mmHg
Rij
MPa
M1
M2
M3
M4
M5
M6
M7
M8
M9
M10
M11
3.20
3.08
3.10
3.17
3.47
3.61
3.17
3.28
6.79
7.79
7.83
33.91
33.99
34.67
35.81
36.15
36.74
35.10
38.31
68.87
78.17
74.75
359.14
355.34
364.49
363.09
370.61
382.51
354.80
379.07
457.92
494.54
535.55
201.24
189.86
183.38
186.26
211.95
216.49
193.13
175.55
286.76
297.68
292.08
1117.85
1240.95
963.57
1001.85
811.54
578.05
1259.28
638.03
56.69
16.55
3.86
10.88
15.39
12.83
18.09
15.82
11.31
16.84
19.77
13.83
12.68
11.33
• Application of feasibility conditions
– Checking property values with sink including Non-GC
properties (VP, solubility), the sufficient conditions
are satisfied for remaining formulations
Blanket Wash Solvent 7:7
Candidate molecules M1-M7
Cyclical
identified visually by the
developedcompound
method correspond
to solutions found by the MINLP
approach used by Sinha and
Achenie (2001)
Although valid formulation,
heptane (M8) is flammable
hence not an ideal solvent
C2
Valid Formulations
0.1
0.9
0.2
0.8
0.3
0.7
Ethers
0.4
0.6
0.5
M1
M2
M3
M4
M5
M6
M7
M8
0.5
: CH2O-CH2-(CH2O)2
: CH3-CH2-CH2O-CH3O
: CH3-CH2-(CH2O)2-CH3
: CH3O-(CH2)3-CH3
: CH3O-CH2O-CH3O
: CH2O-(CH2O)2-CH2O
: CH3-CH2CO-CH3
: CH3-(CH2)5-CH3
M2
M3
0.6
0.4
M1
0.7
MEK
0.3
M6
M8
0.8
0.2
M4
M5
0.9
0.1
M7
C3
C1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Integrated Design Approach
Stream Properties &
Unit Constraints
Clusters
Process Design
Process/
Product Design
Calculations
Property Targets
M
Clusters
Molecular Formulations
Molecular
Design
Molecular Design
Example – Integrated Design
To Flare
4.4 kg/min
ABSORBER
Fresh Solvent 2
Objective
To maximize the use of offgas condensate and to
minimize fresh solvent use
to the degrease
Stream
Characterization
Sulfur Content (S)
Molar Volume (Vm)
Vapor Pressure (VP)
Spent Organics
for incineration
Evaporated Solvent Offgas to Flare or
Condensation for Reuse
Metal
SOLVENT
REGENERATION
FINSH
PROCESSING
DEGREASING
Recycle Solvent
Fresh Solvent 1
36.6 kg/min
Degreased Metal
Metal Degreasing 1:9
• Degreaser Feed Constraints
Property
Lower Bound
Upper Bound
S (%)
0.00
1.00
Vm (cm3/mol)
90.09
487.80
VP (mmHg)
1596
3040
• Property Operator Mixing Rules
Ns
S M   xs  S s
, S ref = 0.5 wt%
s 1
Ns
Vm M   xs Vm s
s 1
, Vmref = 80 cm3/mol
Ns
1.44
M
VP
  xs  VPs1.44
s 1
, VP ref = 760 mmHg
Metal Degreasing 2:9
Visualization
of Process
Design
Problem
VOC Condensation
Data
C2
0.1
Sulfur content, density
and vapor pressure data
given for temperature
range 480K-515K
0.9
0.2
0.8
0.3
0.7
0.4
0.6
0.5
0.5
0.6
0.7
0.4
DEGREASER
0.3
0.8
0.2
495 K
490 K
0.9
480 K
485 K
500 K
510 K
505 K
CONDENSATE
0.1
515 K
C3
C1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Metal Degreasing 3:9
Visualization
of Process
Design
Problem
Conditions
C2
0.1
0.9
0.2
0.8
POINT A
0.3
Point A & B
dictate property
constraint targets
Condenser operates @
500 K
Feed Solvent must
have zero sulfur
content
0.7
0.4
0.6
0.5
0.5
0.6
0.7
0.4
DEGREASER
0.3
0.8
0.2
POINT B
495 K
490 K
0.9
480 K
500 K
510 K 0.1
505 K
515 K
485 K
CONDENSATE
C3
C1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Metal Degreasing 4:9
Product
Process
Non-GC Based
fromProperties
Process
Values
Design Visual Solution
S
(%)
Vm
(cm3/mol)
0
102.09
GC1825.37
Based
Properties
0
720.75
3878.66
Property Targets
VP
(mmHg)
PropertyM Cluster
Framework
Molecular Property
Clusters
Molecular Property
Constraints
Molecular Property
Molecular Design
Constraints
Hv
(kJ/mol)
Vm
(cm3/mol)
VP
(mmHg)
50
102.09
1825.37
100
Candidate Molecules
720.75
3878.66
Metal Degreasing 5:9
Property
Prediction (GCM)
 H v  hvo   ni  hv1i
Ng
 H v  hvo   n g  hv1g , ref  20
i
Vm  d   ni  v1i
g 1
Ng
i
Tbp  t bo  ln  ni  t b1
Vm  d   n g  v1g
, ref  100
 Tbo  N g
   n g  t b1g
exp 
 t bo  g 1
, ref  7
g 1
i
 Tbp
log10 VP  5.58  2.7
 T
Molecular Property
Operators
1 .7



Non-GC Property
Metal Degreasing 6:9
Visualization
of Molecular
Design
Problem
Molecular
Fragments
C2
0.1
0.9
0.2
G1:
G2:
G3:
G4:
G5:
G6:
G7:
0.8
0.3
0.7
0.4
0.6
0.5
0.5
0.6
0.4
G5
G3
0.7
0.3
G2
G1
G4
0.8
G6
G7
0.2
0.9
0.1
C3
C1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
CH3
CH2
CH2O
CH2N
CH3N
CH3CO
COOH
Metal Degreasing 7:9
Visualization
of Molecular
Design
Problem
Candidate
Molecules
C2
0.1
0.9
0.2
0.8
0.3
0.7
0.4
0.6
0.5
0.6
M6
0.7
M3
M1
0.8
M1
M2
M3
M4
M5
0.5
M6
M7
0.4
0.3
M7
M5M2
CH3-(CH2)5-CH3CO
CH3CO-(CH2)2-CH3CO
(CH3)3-(CH2)5-CH2N
CH3-(CH2)2-COOH
(CH3)2-CH3CO-CCL
-(CH2O)5- ring
CH3-(CH2)2-CH3N-COOH
M4
0.2
0.9
0.1
C3
C1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Metal Degreasing 8:9
• Formulations from Visual Design
O
HO
butanoic acid
(M1)
O
Formulation
AUP
Tb (K)
Hv
(kJ/mol)
Vm
(cm3/mol)
VP
(mmHg)
M1
5.06
450.58
53.19
156.85
2078.98
M2
4.71
448.54
54.13
118.03
2163.90
M3
5.11
437.29
49.35
189.41
2692.07
M4
4.86
438.97
63.29
93.39
2606.12
M5
4.02
413.20
43.88
121.14
4241.48
M6
4.19
428.11
44.22
127.66
3208.12
M7
5.71
485.01
70.24
112.52
1037.99
O
2,5-hexadione
(M2)
O
2-octanone (M4)
•
Application of Feasibility Conditions
–
–
–
–
All formulations satisfy first two necessary conditions
M5 and M6 fail to satisfy sink AUP range
M3 and M7 did not match Non-GC property value
M1, M2 and M4 are valid solvent candidates
Metal Degreasing 9:9
Maximization
of
Visualization
Condensate
of Process
Solutions to
Molecular
Design Problem
C2
17.44
kg/min of
Design
condensate recycle is
Solution
utilized
0.9
0.1
0.8
0.2
O
0.7
0.3
19.36 kg/min of 2,5hexadione as fresh
solvent
HO
butanoic acid
(M1)
0.6
0.4
0.5
0.5
O
DEGREASER
0.6
O
2,5-hexadione
(M2)
0.4
M1
0.3
0.7
M2
0.2
0.8
M4
500 K
O
2-octanone (M4)
0.1
0.9
CONDENSATE
C1
C3
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Summary
•
Property Prediction and CAMD
–
–
–
–
–
–
–
–
Can generate data for the simulation software in order
to solve novel problems
Allows for development of environmentally benign
designs and components
Systematic approaches, do not rely on rules of thumb.
Utilizes process/product knowledge at selection level.
Expands solution space for solvent design/selection
Capable of identifying novel compounds not included in
databases and/or literature
Methodology has been proven through numerous
application studies
Powerful tool when used in an integrated framework
Other Business
•
Next Lecture – March 28
–
–
•
Product engineering and Six Sigma
SSLW pp. 662-678
Progress Report No. 3
–
–
Friday April 5
Remember to fill out the team evaluation forms