property prediction and camd
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Property Prediction and CAMD
CHEN 4470 – Process Design Practice
Dr. Mario Richard EdenDepartment 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
ibibobp
iim
ivivov
tntT
vndV
hnhH
i
i
1
1
1
ln
7.1
10 7.258.5log
T
TVP bp
Given:Information on compound structure.
Obtained:Properties of the compound.
Given:Information on desired properties & type of compound.
Obtained:Compound structures having the desired properties.
Property prediction:
Computer Aided Molecular Design:
CAMD 1:3
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 Aniline Water CAS No. 62–53–3 7732–18–5
Boiling Point (K) 457.15 373.15 Solubility Parameter (MPa½) 24.12 47.81
• 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 3:7
Aniline Case Study 4:7
• Results of Solvent Search– No high boiling solvents found
Also, higher and branched alkanes were identified as
candidates
Solvent CAS No. n-Octane 111–65–9
2-Heptanone 110–43–0 3-Heptanone 106–35–4
Aniline Case Study 5:7
• Process Simulation
1
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15
T2
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1
25T1
S1
S3
S4
S2
S5
S6Aniline Laden Water
Solvent
Water (2 ppm Aniline)
Aniline Laden Solvent
Recovered Aniline
Recovered Solvent
Ext
ract
ion
Col
umn
Reg
ener
atio
n C
olum
n
(15
Sta
ges)
(25
Sta
ges)
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
regenerationDesign 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
230
250
270
290
310
330
350
370
390
410
430
10 30 50 70 90 110 130 150
Number of stages
So
lve
nt
us
ag
e (
lite
r)
1500
1600
1700
1800
1900
2000
2100
2200
2300
En
erg
y c
on
su
mp
tion
(MJ
)
Solvent Usage Energy Consumption
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
• Goal– Identify alternative solvents with better safety
and environmental properties.
VolatileFlammabl
e
Carcinogen
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.
• 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
Property Based Design
Property clusters are conserved surrogate properties described by property operators, which have linear mixing rules, even if the operators
themselves are nonlinear.
Property Clusters 1:2
R.H.S.
Property Clusters
s
jsjs PAU
C
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0,8
0,9
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9
0,9
C3
C2
C1
0,8
0,7
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0,1
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0,9
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9
0,9
C3
C2
C1
0,8
0,7
0,6
0,5
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0,1
0,1
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0,5
0,6
0,7
0,8
0,9
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9
0,9
C3
C2
C1
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,1
0,2
0,3
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0,5
0,6
0,7
0,8
0,9
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9
0,9
C3
C2
C1C3
C2
C1
TrueFeasibility
Region
min min max min max max min max min1 2 3 1 2 3 1 2 3
max max min max min min max min max1 2 3 1 2 3 1 2 3
( , , ) ( , , ) ( , , )
( , , ) ( , , ) ( , , )
Property Clusters 2:2
Feed Constraint
Feasibility Region
Analysis has shown that region
boundary can be described by 6 unique points.
min max,sink ,sinkj js jP P P
1,2,3j , 1
s
MIX
N
sjssj CC
Linear Expression for Mixing 2 Ternary Clusters
1,2,3j , 1
s
MIX
N
sjssj CC
Linear Expression for Mixing 2 Ternary Clusters
0.8
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0.9
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.9
C2
S 1
S 2
SMIX
Sink
C1C3
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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.9
C2
S 1
S 2
SMIX
Sink
C1C3
Feasibility
Necessary Condition
Match clustering target
Sufficient Condition
Match AUP value of sink
j k
kkjji
ii EODMCNXf )(
1st order 2nd order 3rd order
Group Contribution TermsPropertyFunction
• 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
Group Contribution Methods
Property Targets
Molecular Property Clusters
Molecular Design
GC BasedProperties
Non-GC BasedProperties
Process Product
Molecular Property Constraints
Candidate Molecules
PropertyM Cluster Framework
Molecular Clusters 1:5
Molecular Clusters 2:5
Process Property OperatorsMolecular Property
Operators
Linear Expression for Mixing 2 Ternary
Clusters
sN
sjss
Pj Px
1
gN
gjgg
Mj Pn
1
1
sN
sjssjMIX CC
refj
jj
jN
jjPAU
1
AUPC j
j
G1and G2, are added linearly on the ternary diagram. The location of 1, corresponds to the location of G1-G2molecule
2211
111 AUPnAUPn
AUPn
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0,7
0,8
0,9
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9
0,9
True Feasibility Region
C3
C2
C1
0,8
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0,1
0,1
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0,9
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9
0,9
True Feasibility Region
C3
C2
C1
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0,7
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0,1
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0,9
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9
0,9
True Feasibility Region
C3
C2
C1C3
C2
C1
22
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0,7
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0,1
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0,9
0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9
0,9
C3
C2
C1
M1
G1
G2
G3G4
FeasibilityRegion
1, the visualization arm, corresponds to the location of G1-G2
molecule
2211
111 AU PnAU Pn
AU Pn
Feasibility
Necessary Conditions1. Free bond number is zero.
2. Match clustering target3. Match AUP range of sink
Sufficient ConditionCheck property value with sinkincluding Non-GC properties
Molecular Clusters 3:5
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0.9
C3
C2
C1
1
2
3
4TARGET
1: CH3
2: CH2
3: CH3N4: COOH
Molecular Synthesis
CH3-(CH2)2-CH3N-COOH
Molecular Clusters 4:5
1: CH3
2: CH2
3: CH3N4: COOH5: CH3N-COOH
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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.9
C3
C2
C1
1
2
3
4
5
TARGET
Molecular Synthesis
CH3-(CH2)2-CH3N-COOH
Molecular Clusters 4:5
1: CH3
2: CH2
3: CH3N4: COOH5: CH3N-COOH6: CH3-CH2
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0.9
C3
C2
C1
1
2
3
4
5
6TARGET
Molecular Synthesis
CH3-(CH2)2-CH3N-COOH
Molecular Clusters 4:5
1: CH3
2: CH2
3: CH3N4: COOH5: CH3N-COOH6: CH3-CH2
7: CH3-(CH2)2
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C3
C2
C1
1
2
3
4
5
67
TARGET
Molecular Synthesis
CH3-(CH2)2-CH3N-COOH
Molecular Clusters 4:5
1: CH3
2: CH2
3: CH3N4: COOH5: CH3N-COOH6: CH3-CH2
7: CH3-(CH2)2
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C3
C2
C1
1
2
3
4
5
67
TARGET
Molecular Synthesis
CH3-(CH2)2-CH3N-COOH
Molecular Clusters 4:5
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0.9
C3
C2
C1
CH3[-1]
CH2[-2]CH3O[-1]
CH3-CH2-CH2-CH2-CH3O[0]
CH3-CH2-CH2-CH2[-1]
CH3-CH2-CH2[-1]
CH3-CH2[-1]
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C3
C2
C1
CH3[-1]
CH2[-2]CH3O[-1]
CH3O-CH2-CH2-CH2[-1]
CH3O-CH2-CH2-CH2-CH3[0]CH3O-CH2[-1]
CH3O-CH2-CH2[-1]
0.4
0.1
0.2
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0.5
0.6
CH3[-1]
CH2[-2]CH3O[-1]
CH3O-CH2-CH2-CH2[-1]
CH3O-CH2-CH2-CH2-CH3[0]CH3O-CH2[-1]
CH3O-CH2-CH2[-1]
0.7
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0.5
CH3[-1]
CH2[-2]CH3O[-1]
CH3-CH2-CH2-CH2-CH3O[0]
CH3-CH2-CH2-CH2[-1]
CH3-CH2-CH2[-1]
CH3-CH2[-1]
The location of each molecular formulation is unique and
independent of group addition path
Formulation of Butyl methyl ether
CH3-CH2-CH2-CH2-CH3O
Molecular Clusters 5:5
• 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
Example: Molecular Synthesis
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
imimom
ibibob
iviivov
i
i
tgtT
tgtT
hghH
1
1
ln
ln
Property Prediction (GCM) Molecular Property Operators
11
11
1
exp
exp
m
N
gg
mo
m
b
N
gg
bo
b
ivgvov
tnt
T
tnt
T
hnhH
g
g
ref = 100
ref = 20
ref = 7
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0.9
C 3
C 2
C 1
Feasibility Region
Blanket Wash Solvent 2:7
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C 3
C 2
C 1
G1 G2 G3 G4
G5 G6 G7
Molecular Groups
G1: CH3
G2: CH2
G3: CH2OG4: CH3OG5: CH2COG6: CH3COG7: COOH
Blanket Wash Solvent 3:7
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0.9
C 3
C 2
C 1
M1
M8
M9 M10 M11 M7
M3
M6
M4 M5
M2
Candidate Molecules
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
Blanket Wash Solvent 4:7
Formulations AUP Hv
kJ/mol Tb K
Tm K
VP mmHg
Rij
MPa
M1
3.20 33.91
359.14
201.24
1117.85
10.88
M2 3.08 33.99 355.34 189.86 1240.95 15.39 M3 3.10 34.67 364.49 183.38 963.57 12.83 M4 3.17 35.81 363.09 186.26 1001.85 18.09 M5 3.47 36.15 370.61 211.95 811.54 15.82 M6 3.61 36.74 382.51 216.49 578.05 11.31 M7 3.17 35.10 354.80 193.13 1259.28 16.84 M8 3.28 38.31 379.07 175.55 638.03 19.77 M9 6.79 68.87 457.92 286.76 56.69 13.83 M10 7.79 78.17 494.54 297.68 16.55 12.68 M11 7.83 74.75 535.55 292.08 3.86 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
• Feasible formulations from Visual Synthesis
Blanket Wash Solvent 5:7
Formulations AUP Hv
kJ/mol Tb K
Tm K
VP mmHg
Rij
MPa
M1
3.20 33.91
359.14
201.24
1117.85
10.88
M2 3.08 33.99 355.34 189.86 1240.95 15.39 M3 3.10 34.67 364.49 183.38 963.57 12.83 M4 3.17 35.81 363.09 186.26 1001.85 18.09 M5 3.47 36.15 370.61 211.95 811.54 15.82 M6 3.61 36.74 382.51 216.49 578.05 11.31 M7 3.17 35.10 354.80 193.13 1259.28 16.84 M8 3.28 38.31 379.07 175.55 638.03 19.77 M9 6.79 68.87 457.92 286.76 56.69 13.83 M10 7.79 78.17 494.54 297.68 16.55 12.68 M11 7.83 74.75 535.55 292.08 3.86 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
• Feasible formulations from Visual Synthesis
Blanket Wash Solvent 6:7
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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.9
C 3
C 2
C 1
M1
M8
M7
M3
M6
M4 M5
M2
Valid Formulations
M1 : CH2O-CH2-(CH2O)2M2 : CH3-CH2-CH2O-CH3OM3 : CH3-CH2-(CH2O)2-CH3
M4 : CH3O-(CH2)3-CH3
M5 : CH3O-CH2O-CH3OM6 : CH2O-(CH2O)2-CH2OM7 : CH3-CH2CO-CH3
M8 : CH3-(CH2)5-CH3
Candidate molecules M1-M7 identified visually by the
developed 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
Cyclical compoun
d
EthersEthers
MEK
Blanket Wash Solvent 7:7
Integrated Design Approach
Stream Properties &Unit Constraints
Clusters
Property Targets
ClustersM
Molecular Formulations
Process Design
Molecular Design
Process/Product Design
Calculations
Molecular
Design
Degreased Metal
4.4 kg/min
Fresh Solvent 2
FINSHPROCESSING
To Flare
Spent Organicsfor incineration
Recycle Solvent
Metal
Evaporated Solvent Off-gas to Flare or
Condensation for Reuse
AB
SO
RB
ER
SOLVENTREGENERATION DEGREASING
Fresh Solvent 136.6 kg/min
Example – Integrated Design
Stream CharacterizationSulfur Content (S)
Molar Volume (Vm)Vapor Pressure (VP)
Objective
To maximize the use of off-gas condensate and
to minimize fresh solvent use to the
degrease
Ns
sssM VPxVP
1
44.144.1
Ns
sssM SxS
1
Ns
ssmsm VxV
M1
, S ref = 0.5 wt%
, Vmref = 80 cm3/mol
, VP ref = 760 mmHg
• Property Operator Mixing Rules
• Degreaser Feed Constraints
Property Lower Bound Upper Bound
S (%) 0.00 1.00
Vm (cm3/mol) 90.09 487.80
VP (mmHg) 1596 3040
Metal Degreasing 1:9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.9
C3
C2
C1
505 K
500 K495 K490 K
485 K480 K
510 K
515 K
DEGREASER
CONDENSATE
Visualization of Process
Design Problem
VOC Condensation Data
Sulfur content, density and vapor
pressure data given for temperature
range 480K-515K
Metal Degreasing 2:9
Point A & Bdictate
property constraint
targets
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.9
C3
C2
C1
505 K
500 K495 K490 K
485 K480 K510 K
515 K
DEGREASER
CONDENSATE
POINT A
POINT B
Conditions
Condenser operates @ 500
KFeed Solvent must
have zero sulfur content
Visualization of Process
Design Problem
Metal Degreasing 3:9
Property Targets
Molecular Property Clusters
Molecular Design
GC BasedProperties
Non-GC BasedProperties
Process Product
Molecular Property Constraints
Candidate Molecules
PropertyM Cluster Framework
Values from Process Design Visual Solution S
(%)Vm
(cm3/mol)VP
(mmHg)
0 102.09 1825.37
0 720.75 3878.66
Hv
(kJ/mol)Vm
(cm3/mol)VP
(mmHg)
50 102.09 1825.37
100 720.75 3878.66
Molecular Property
Constraints
Metal Degreasing 4:9
Property Prediction
(GCM)
ibibobp
iim
ivivov
tntT
vndV
hnhH
i
i
1
1
1
ln
7.1
10 7.258.5log
T
TVP bp
Non-GC Property
Molecular Property
Operators
7, ref
100, ref
20, ref
g
g
N
gvgvov hnhH
11
g
g
vndVN
ggm 1
1
g
g
N
gbg
bo
bo tnt
T
11exp
Metal Degreasing 5:9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.9
C3
C2
C1
G1G2
G3
G6
G5
G4
G7
Molecular Fragments
G1: CH3
G2: CH2
G3: CH2OG4: CH2NG5: CH3NG6: CH3COG7: COOH
Metal Degreasing 6:9
Visualization of Molecular
Design Problem
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.9
C3
C2
C1
M1M2
M3
M4M5
M6 M7
Candidate Molecules
M1 CH3-(CH2)5-CH3CO
M2 CH3CO-(CH2)2-CH3CO
M3 (CH3)3-(CH2)5-CH2N
M4 CH3-(CH2)2-COOH
M5 (CH3)2-CH3CO-CCL
M6 -(CH2O)5- ring
M7 CH3-(CH2)2-CH3N-COOH
Metal Degreasing 7:9
Visualization of Molecular
Design Problem
• Formulations from Visual DesignFormulation 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
• 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 8:9
HO
O
O
O
O
2,5-hexadione (M2)
2-octanone (M4)
butanoic acid (M1)
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0.9
C3
C2
C1
500 K
DEGREASER
CONDENSATE
M1
M2
M4
Solutions to Molecular
Design Problem
Maximization of Condensate
17.44 kg/min of condensate
recycle is utilized
19.36 kg/min of 2,5-hexadione as
fresh solvent
HO
O
O
O
O
2,5-hexadione (M2)
2-octanone (M4)
butanoic acid (M1)
Metal Degreasing 9:9
Visualization of Process
Design Solution
• 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
Summary
• 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
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