1. 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 28, 2013

2. Property Prediction 1:2 Motivation Group Contribution Methods
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.

3. Property Prediction 2:2
Examples and Software

4. CAMD 1:3

5. CAMD 2:3

6. 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

7. Aniline Case Study 1:7 Problem Description Conventional Approach
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 ppm to 2 ppm.
Conventional Approach
Single stage distillation.
Reduces aniline content to 500 ppm.
Energy usage: MJ
No data is available for the subsequent downstream processing steps.

8. Aniline Case Study 2:7 Objective Reported Aniline Solvents
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

9. 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

10. Also, higher and branched alkanes were identified as candidates
Aniline Case Study 4:7
Results of Solvent Search
No high boiling solvents found
Also, higher and branched alkanes were identified as candidates

11. Aniline Case Study 5:7
Process Simulation

12. 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

13. Aniline Case Study 7:7
Validation of Minimum Cost Solution

14. 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.
Volatile
Flammable
Carcinogen

15. 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 < (MPa)½

16. 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.

17. 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

18. Property Clusters 1:2
Property clusters are conserved surrogate properties described by property operators, which have linear mixing rules, even if the operators themselves are nonlinear.
R.H.S.

19. Match clustering target
Property Clusters 2:2
Feed Constraint
Feasibility Region
Analysis has shown that region boundary can be described by 6 unique points.
Feasibility
Necessary Condition
Match clustering target
Sufficient Condition
Match AUP value of sink

20. 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

21. Molecular Clusters 1:5

22. Molecular Clusters 2:5

23. Molecular Clusters 3:5 2
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0,9 C3 C2 C1 M1 G1 G2 G3 G4
Feasibility
Region
b1, the visualization arm, corresponds to the location of G1-G2 molecule

24. Molecular Clusters 4:5 1: CH3 Molecular Synthesis 2: CH2 3: CH3N
4: COOH
CH3-(CH2)2-CH3N-COOH

25. Molecular Clusters 4:5 1: CH3 Molecular Synthesis 2: CH2 3: CH3N
4: COOH
5: CH3N-COOH
CH3-(CH2)2-CH3N-COOH

26. Molecular Clusters 4:5 1: CH3 Molecular Synthesis 2: CH2 3: CH3N
4: COOH
5: CH3N-COOH
6: CH3-CH2
CH3-(CH2)2-CH3N-COOH

27. Molecular Clusters 4:5 1: CH3 Molecular Synthesis 2: CH2 3: CH3N
4: COOH
5: CH3N-COOH
6: CH3-CH2
7: CH3-(CH2)2
CH3-(CH2)2-CH3N-COOH

28. Molecular Clusters 4:5 1: CH3 Molecular Synthesis 2: CH2 3: CH3N
4: COOH
5: CH3N-COOH
6: CH3-CH2
7: CH3-(CH2)2
CH3-(CH2)2-CH3N-COOH

29. Formulation of Butyl methyl ether
Molecular Clusters 5:5
The location of each molecular formulation is unique and independent of group addition path
Formulation of Butyl methyl ether
CH3-CH2-CH2-CH2-CH3O

30. 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
19.8

31. 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)
Molecular Property Operators
,Y ref = 20
,Y ref = 100
,Y ref = 7

32. Blanket Wash Solvent 2:7

33. Blanket Wash Solvent 3:7

34. Blanket Wash Solvent 4:7

35. Blanket Wash Solvent 5:7 Feasible formulations from Visual Synthesis
Application of feasibility conditions
All formulations satisfy the first two necessary conditions
M9-M11 fail to satisfy the AUP range of the sink

36. Blanket Wash Solvent 6:7 Feasible formulations from Visual Synthesis
Application of feasibility conditions
Checking property values with sink including Non-GC properties (VP, solubility), the sufficient conditions are satisfied for remaining formulations

37. Blanket Wash Solvent 7:7 Cyclical compound Ethers Ethers MEK
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 compound
Ethers
Ethers
MEK

38. Integrated Design Approach
Stream Properties &
Unit Constraints
Process Design
Clusters
Process/
Product Design Calculations
Property Targets
ClustersM
Molecular Formulations
Molecular Design
Molecular Design

39. Example – Integrated Design Stream Characterization
Objective
To maximize the use of off-gas condensate and to minimize fresh solvent use to the degrease
Stream Characterization
Sulfur Content (S)
Molar Volume (Vm)
Vapor Pressure (VP)

40. 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
, S ref = 0.5 wt%
, Vmref = 80 cm3/mol
, VP ref = 760 mmHg

41. Visualization of Process Design Problem
Metal Degreasing 2:9
Visualization of Process Design Problem
VOC Condensation Data
Sulfur content, density and vapor pressure data given for temperature range 480K-515K

42. Visualization of Process Design Problem
Metal Degreasing 3:9
Visualization of Process Design Problem
Conditions
Condenser 500 K
Feed Solvent must have zero sulfur content C 2
0.1
0.9
0.2
0.8
POINT A
0.3
0.7
Point A & B
dictate property constraint targets
0.4
0.6
0.5
0.5
0.6
0.4
DEGREASER
0.7
0.3
0.8
0.2
POINT B
490 K
495 K
500 K
0.9
510 K
0.1
480 K
485 K
505 K
CONDENSATE
515 K C C 3 1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9

43. Metal Degreasing 4:9 Values from Process Design Visual Solution
(%) Vm
(cm3/mol) VP
(mmHg)
102.09
720.75
Molecular Property Constraints Hv
(kJ/mol) Vm
(cm3/mol) VP
(mmHg) 50
102.09
100
720.75

44. Property Prediction (GCM) Molecular Property Operators
Metal Degreasing 5:9
Property Prediction (GCM)
Molecular Property Operators 20 , =
ref y
100 , =
ref y 7 , =
ref y
Non-GC Property

45. Visualization of Molecular Design Problem
Metal Degreasing 6:9
Visualization of Molecular Design Problem
Molecular
Fragments
G1: CH3
G2: CH2
G3: CH2O
G4: CH2N
G5: CH3N
G6: CH3CO
G7: COOH

46. Visualization of Molecular Design Problem
Metal Degreasing 7:9
Visualization of Molecular Design Problem
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

47. Metal Degreasing 8:9 Formulations from Visual Design
AUP
Tb (K)
Hv (kJ/mol)
Vm (cm3/mol)
VP (mmHg) M1
5.06
450.58
53.19
156.85 M2
4.71
448.54
54.13
118.03 M3
5.11
437.29
49.35
189.41 M4
4.86
438.97
63.29
93.39 M5
4.02
413.20
43.88
121.14 M6
4.19
428.11
44.22
127.66 M7
5.71
485.01
70.24
112.52
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

48. Visualization of Process Design Solution Maximization of Condensate
Metal Degreasing 9:9
Solutions to Molecular Design Problem
Visualization of Process Design Solution
Maximization of Condensate
17.44 kg/min of condensate recycle is utilized
19.36 kg/min of 2,5-hexadione as fresh solvent

49. 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

50. Other Business Next Lecture – April 4 Progress Report No. 3
Product engineering and Six Sigma
SSLW pp
Progress Report No. 3
Friday April 5
Remember to fill out the team evaluation forms