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Functionality-Based Process Design: Shifting from Mass Integration to Property Integration
Mahmoud El-Halwagi
Department of Chemical Engineering
Texas A&M University
2
Motivating Example:
How to Reduce Cost of Solvents via Design Improvements?
Challenge: Without Employing Component Material Balances
Targeted Properties for Solvents
Density, Vapor Pressure, Viscosity
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Metal
Recycled Solvent
Degreased
Metal
Solvent 2
36.6 kg/min
4.4 kg/min Evaporated Solvent
Off-Gas to Flare
Solvent 1
Waste
Gaseous
Waste
3
Alternative: Recovery and Recycle
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Metal
Recycled Solvent
Degreased
Metal
Solvent 2
Evaporated
Solvent
Off-Gas
Solvent 1
Solvent
Recovery
Lean
Off-Gas
Waste
Gaseous
Waste
4
Alternative: Recovery and Recycle
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Metal
Recycled Solvent
Degreased
Metal
Solvent 2
Evaporated
Solvent
Off-Gas
Solvent 1
Solvent
Recovery
Lean
Off-Gas
Waste
Gaseous
Waste
5
Alternative: Recovery and Recycle
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Metal
Recycled Solvent
Degreased
Metal
Solvent 2
Evaporated
Solvent
Off-Gas
Solvent 1
Solvent
Recovery
Lean
Off-Gas
Waste
Gaseous
Waste
6
Alternative: Recovery and Recycle
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Metal
Recycled Solvent
Degreased
Metal
Solvent 2
Evaporated
Solvent
Off-Gas
Solvent 1 Lean
Off-Gas
Condensation
Waste
Gaseous
Waste
7
Alternative: Recovery and Recycle
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Metal
Recycled Solvent
Degreased
Metal
Solvent 2
Evaporated
Solvent
Off-Gas
Solvent 1 Lean
Off-Gas
Pervaporation
Waste
Gaseous
Waste
8
Alternative: Recovery and Recycle
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Metal
Recycled Solvent
Degreased
Metal
Solvent 2
Evaporated
Solvent
Off-Gas
Solvent 1
Lean
Off-Gas
Pervaporation
Condensation
Waste
Gaseous
Waste
9
Alternative: Recovery and Recycle
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Metal
Recycled Solvent
Degreased
Metal
Solvent 2
Evaporated
Solvent
Off-Gas
Solvent 1
Lean
Off-Gas
Condensation
Pervaporation
Waste
Gaseous
Waste
10
Alternative: Recovery and Recycle
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
Metal
Recycled Solvent
Degreased
Metal
Solvent 2
Evaporated
Solvent
Off-Gas
Solvent 1 Lean
Off-Gas
Solvent
Recovery
Lean
Off-Gas Solvent
Recovery
Waste
Gaseous
Waste
11
And the
optimum
solution is ...
And so on …..
There are INFINITE alternatives!
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Metal
Recycled Solvent
Degreased
Metal
Solvent 2 6.8 kg/s
Evaporated
Solvent
Off-Gas
Solvent 1 4.4 kg/s
Lean
Off-Gas
Condensation
247 K
$874,000/yr
(43% cost reduction)
Waste
Gaseous
Waste
12
OBSERVATIONS
Numerous alternatives
Intuitively non-obvious solutions
Need a systematic methodology to extract
optimum solution
Design specifications may be based on
solvent properties, not chemical
components
Process must be treated as an integrated
system
13
KEY QUESTIONS
Can we identify performance benchmarks
(targets) ahead of detailed design?
How to systematically synthesize
alternatives to achieve these targets?
Can we optimize process design based on
properties not chemicals/mass?
14
OUTLINE
o Motivating Example and Observations
Overview of Process Integration and Design
o Mass Integration
o Property Integration
o Clustering
o Visualization Tools
o Simultaneous Process and Molecular Design
o Conclusions
15
PROCESS INTEGRATION
A holistic approach to process design and operation that
emphasizes the unity of the process and optimizes its
design and operation
16
BIG PICTURE FIRST,
DETAILS LATER FIRST, understand
the global picture
of the process and
develop system insights
LATER, think equipment,
detailed simulation, and
process details.
Overall Philosophy
17
TARGETING
Examples of Specific Performance Targets:
Minimum heating and cooling utilities: (e.g., Linnhoff and Hindmarsh, 1983)
Process cogeneration/CHP: (e.g., Shang and Kokossis, 2005)
Refrigeration systems (e.g., Wu and Zhu, 2002)
Maximum usage of process MSAs/minimum cost of MSAs for mass-
exchange networks (e.g., El-Halwagi and Manousiouthakis, 1989)
Wastewater minimization (e.g., Wang and Smith, 1994)
Hydrogen management (e.g., Alves and Towler, 2002)
Maximum recycle of process resources (e.g., El-Halwagi et al., 2004;
Kazantzi and El-Halwagi, 2005)
Minimum usage of fresh resources (El-Halwagi et al., 2004)
Reactors and reactive separators (e.g., Linke and Kokossis, 2002)
Maximum process yield (Al-Otaibi and El-Halwagi, 2006)
Identification of performance benchmarks
for the whole process AHEAD of detailed design
18
Categories of Process Integration
Energy Integration
Mass Integration Process Integration +
Property Integration +
Process
Energy
Mass
Property
Focus
19
OUTLINE
o Motivating Example and Observations
o Overview of Process Integration and Design
Mass Integration
o Property Integration
o Clustering
o Visualization tools
o Simultaneous Process and Molecular Design
o Conclusions
20
Mass Integration A systematic methodology that provides
fundamental understanding of the global flow of mass within a process and employs
this understanding in identifying performance targets and optimizing
the generation and routing of species throughout the process.
21
Species
Interception
Network
MSAs and ESAs
MSAs and ESAs (to Regeneration and Recycle)
.
.
.
# 1
# 2
.
.
.
Sources Segregated
Sources
Sinks/
Generators
Sources (Back to
Process)
Process from a Species Perspective
(Process
Streams
With
Targeted
Species)
(Units)
N sinks
22
MASS INTEGRATION STRATEGIES
Modest Sink/Generator Manipulation
(e.g. Moderate Changes in Operating Conditions)
Minor Structural Modifications
(Segregation, Mixing, Recycle, etc.)
Material Substitution
(Solvent, Catalyst, etc.)
Equipment Addition/Replacement
(Interception/Separation devices, etc.)
Technology Changes (New Chemistry, New Processing
Technology, etc.)
Target
No Cost/ Low Cost Strategies
Moderate-Cost Modifications
New Technologies
CO
ST
, IM
PA
CT
AC
CE
PT
AB
ILIT
Y
23 23
Sources Segregated
Sources
Waste?
Sinks Constraints on feed flowrate
and composition
?
EXAMPLE OF A MASS INTEGRATION PROBLEM:
DIRECT RECYCLE
Source: A stream which contains the targeted species
Sink: An existing process unit/equipment that can accept a source
Fresh?
24
OPTIMIZATION FORMULATION
21 0
2121 0
sconstraint negativity-Non
21
sconstraintSink
21
21
sinks tofeeds of Mixing
21
:sources of Splitting
:subject to min
sinks
sinks,
sinks
maxmin
1
sinks,
sinks
1
,
1
,,
1
sin
sin
, ..., N,for jF
, ..., N,j and for ,...,N,for iw
, ..., N,for jzzz
, ..., N,for jywzG
, ..., N,for jwFG
,...,N,for iwwW
F
j
sourcesji
j
in
jj
N
i
iji
in
jj
N
i
jijj
N
j
sourceswasteijii
N
j
j
sources
sources
ks
ks
25
PARAMETRIC DERIVATION OF OPTIMALITY
CONDITIONS USING DYNAMIC PROGRAMMING
Sink
j=1
F1
R0 R1 Sink
j
Fj
Rj-1 Rj Sink
j=Nsinks
RNsink-1 RNsinks
Sources
j
j
jiiji
jNjijjj
,..., N, for iwWR
RRRRRsources
21
,,,,
1
1
,,
,,,2,1
FNsink
Bellman’s principle of optimality “an optimal policy has the property that, whatever
the initial state and the initial decision are, the remaining decisions must constitute
an optimal policy with regard to the state resulting from the first decision.”
Stage: sink
Return function: fresh
State: Unused source
Resulting optimality conditions are used as the basis
for the material recovery pinch diagram Derivation available in: El-Halwagi, M. M., F. Gabriel, and D. Harell, “Rigorous Graphical Targeting for Resource
Conservation via Material Recycle/Reuse Networks”, Ind. Eng. Chem. Res., 42, 4319-4328 (2003)
26
Load
Flowrate
Sink
Composite
Curve
max,
1
SinkM
max,
2
SinkM
max,
3
SinkM
G1 G2 G3
maxin
j z 0 jzmaxmax,
jj
Sink
j zGM
Sink Composite Diagram
Rank in ascending
order of composition
27
Load
Flowrate
SourceM1
SourceM 2
SourceM3
W1 W2 W3
Source
Composite
Curve
Source Composite Diagram
ii
Source
i yWM
Rank in ascending
order of composition
28
Load
Flowrate
Material
Recycle
Pinch
Point
Sink
Composite
Curve
Source
Composite
Curve
Integrating Source and Sink Composites
29
Rigorous targets ahead
of detailed design
Load
Flowrate Minimum
Waste Minimum
Fresh
Maximum
Recycle
Material
Recycle
Pinch
Point
Sink
Composite
Curve
Source
Composite
Curve
Material Recycle Pinch Diagram
(pure fresh)
El-Halwagi, M. M., F. Gabriel, and D. Harell, “Rigorous Graphical Targeting for Resource
Conservation via Material Recycle/Reuse Networks”, Ind. Eng. Chem. Res., 42, 4319-4328 (2003)
30
•No flowrate should be passed through the pinch
(i.e. the two composites must touch)
•No waste should be discharged from sources
below the pinch
•No fresh should be used in any sink above the
pinch
Useful Design Rules
For Material Recycle
Pinch Diagram
Minimum
Waste Minimum
Fresh
Maximum
Recycle
Material
Recycle
Pinch
Point
Sink
Composite
Curve
Source
Composite
Curve
31 31
Feedstock Washer
Scrubber
Processing
Facility
Condensate I
Condensate II
Main Product
Byproducts
Wash
Water
Scrubbing
Water
Offgas
Solid
Waste
Example: Food Processing Facility
Two sources (Condensate I and II), Two Sinks (Washer and Scrubber)
El-Halwagi, M., “Process Integration”, Elsevier (2006)
32 32
Sink Data for the Food Processing Example
Sink
Flowrate
kg/hr
Maximum
Inlet
Mass Fraction
Maximum
Inlet
Load, kg/hr
Washer
8,000
0.03
240
Scrubber
10,000
0.05
500
Source Data for the Food Processing Example
Source
Flowrate
kg/hr
Inlet
Mass Fraction
Inlet
Load, kg/hr
Condensate I
10,000
0.02
200
Condensate II
9,000
0.09
810
33 33
Feedstock Washer
Scrubber
Processing
Facility
Condensate I
Condensate II
Main Product
Byproducts
Wash
Water
Scrubbing
Water
Offgas
Solid
Waste
10,000 kg/hr
9,000 kg/hr
8,000 kg/hr
Critique project proposed by engineer:
Recycle Condensate I to Scrubber
Reduce fresh water to 8,000 kg/hr (down from 18,000 kg/hr)
34 34
0
250
375
500
625
750
875
1,000
Load
kg/hr
0 4 8 12 16 20 24 28 32 36 40 Flowrate, 1000 kg/hr
125
18
Washer
Scrubber
740
Sink
Composite
Curve
240 200
1,010
Fresh 2
Waste
21
Source
Composite
Curve
Condensate II
Condensate I
•Min Fresh = 2,000 kg/hr
(25% of first proposed
amount of 8,000kg/hr)
•Min Waste = 3,000 kg/hr
•Recycled water = 16,000
kg/hr
Pinch
35 35
0
250
375
500
625
750
875
1,000
Load
kg/hr
0 4 12 16 20 24 28 32 36 40
Flowrate, 1000 kg/hr
125
18
Washer
Scrubber
740
240 200
1,010
Fresh
Waste
27
Condensate II
Condensate I
Proposed
Recycle =
6,000
kg/hr
Flowrate of 6,000 kg/hr
passed through the
pinch
•6,000 kg/hr more
than fresh target
•6,000 kg/hr more waste
discharge
Do not pass flow
through the pinch
Representation of the
first proposed
network
8
Pinch Location
36
0
250
375
500
625
750
875
1,000
1,125
Load
kg/hr
0 4 8 12 16 20 24 28 32 36
Flowrate, 1000 kg/hr
125
5.3
Washer
740
240
Fresh Waste
14.3
Condensate II
If proposed project has been implemented,
Can we still use pinch analysis to reduce
Fresh usage?
Improvement after implementation of
proposed project
Fresh usage: 5,300 kg/hr (265% of target)
Big picture yields insights unseen by detailed
engineering (unit/stream based)
Short-term projects must be part of an
overall integrated strategy
37
OUTLINE
o Motivating example and observations
o Overview of Process Integration and Design
o Mass integration
Property Integration
o Clustering
o Visualization tools
o Simultaneous Process and Molecular Design
o Conclusions
38
Property-based, holistic approach to the allocation and
manipulation of streams and processing units which is
based on tracking, adjusting, assigning, and matching of
functionalities throughout the process.
WHAT IS PROPERTY INTEGRATION?
r m
po
El-Halwagi, M. M., I. M. Glasgow, M. R. Eden, and X. Qin, “Property Integration:
Componentless Design Techniques and Visualization Tools”, AIChE J., 50(8), 1854-1869 (2004)
39
WHEN TO CONSIDER PROPERTY INTEGRATION?
When process constraints are given in terms of properties
When units perform on the basis of certain properties of streams, not their chemical constituents (e.g. vapor pressure in condensation, relative volatility in distillation, Henry’s coefficient in absorption, etc)
When dealing with mixtures of numerous components (e.g., complex hydrocarbons, natural textiles, paper/pulp etc.)
When environmental regulations for process discharges involve limits on properties (e.g., pH, color, BOD, etc.)
40
PROBLEM STATEMENT
• Given
– Process sources with known properties.
– Process sinks with constraints on their
feed properties.
– Interception techniques, which can alter
property values.
• Desired
– Process objectives of optimum allocation,
recovery, and interception.
41
STRUCTURAL REPRESENTATION
PROPERTY
INTERCEPTION
NETWORK
(PIN)
S 1
S 2
S N
.
.
.
.
p 11
, p 12
, p 13
p 21
, p 22
, p 23
p N1
, p N2
, p N3
Process Sources Process Sinks
Sink 1
Sink N
Sink 2
.
.
.
.
p i, Sink 1
< p i < p
i, Sink 1
Lower Upper
p i, Sink 2
< p i < p
i, Sink 2
Lower Upper
p i, Sink N
< p i < p
i, Sink N
Lower Upper
.
.
.
.
42
Given is a process with:
- a number of process sources (streams), Ns
Each source has a given flowrate, Fi and a given property pi
- a number of process units, Nu, which accept streams with a given flowrate, Gj, and an inlet property pin
j,that satisfies the following constraint:
pminj < pin
j < pmaxj
Given is also:
- a fresh resource with known property value, pfr
EXAMPLE OF A PROPERTY INTEGRATION PROBLEM:
DIRECT RECYCLE
Kazantzi, V. and M.M. El-Halwagi, Targeting Material Reuse via Property Integration, Chem. Eng. Prog., 101 (8), 2837, 2005 .
43
For property-based direct recycle,
can we identify rigorous targets for:
- Minimum fresh usage?
- Maximum recycle of process resources?
- Minimum discharge of waste?
Steps towards the objective:
• Developing optimization formulation • Deriving optimality criteria • Developing visualization approaches • Defining targets ahead of detailed design!
OBJECTIVES
44
Choose a finite number of targeted raw properties, pi .
Describe mixing rule for each raw property in operator
form:
e.g. Operator for density,
PROPERTY MIXING
)()( ,
1
_
sii
N
s
sii pxps
sN
s
s
ss
F
Fx
1
r
r
sN
s s
sx
1_
1
s
sii pr
1
)( ,
Pi,s = ith property in sth stream
45
21 0
2121 0
21 or
:ConstraintSink
21
:MixingProperty
21
:Sinks toFeeds of Mixing
21
:Splitting Source
:Subject to
min
u,
u,
u
maxminmaxmin
1
u,,
u
1
,,
1
,,
1
,
, ..., N,for jf
, ..., N, j,...,N,for if
, ..., N,for jppp
, ..., N,for jffG
, ..., N,for jffG
,...,N,for iffF
f
jfr
sji
j
in
jjj
in
jj
N
i
frjfriji
in
jj
N
i
jijfrj
N
j
swasteijii
N
j
jfr
s
s
u
u
OPTIMIZATION FORMULATION
46
Sink
j=1
R0 R1 Sink
j
Rj-1 Rj Sink
j=Nu
RNu-1 RNu
s
j
j
jiiji
jNjijjj
,..., N, for ifFR
RRRRRs
21
,,,,
1
1
,,
,,,2,1
Parametric optimization through dynamic programming
(Bellman’s Principle of Optimality)
DERIVATION OF OPTIMALITY CONDITIONS
ffr,1 ffr,j ffr,Nu
47 Flowrate
Load
Sink
Composite
Min. Waste Min. Fresh
Material Recycle/Reuse
Property Pinch Point
Fresh
PROPERTY-BASED MATERIAL RECYCLE PINCH DIAGRAM
Source
Composite
Kazantzi, V. and M.M. El-Halwagi, Targeting Material Reuse via Property Integration, Chem. Eng. Prog., 101 (8), 2837, 2005 .
48
TARGETING FOR THREE PROPERTIES USING
THE CONCEPT OF CLUSTERING
• Clusters: Surrogate properties which allow the
conserved tracking of properties.
• Obtained by mapping properties into an equi-
dimensional domain.
• Clusters are tailored to have the attractive
features of intra-stream and inter-stream
(mixing/splitting) conservation.
C
Properties
r m m
C r
po po C
Clusters
49
DESIRED CHARACTERISTICS OF CLUSTERS
C1
C2 C3
Ci si
NC
,
1
1
Ci s, is cluster i in stream s
C Ci ss
N
i s
S_
,
1
C s1,
C s2,
C s3,
C1
C2 C3
s
s+1
s
s1
I. Intra-Stream Conservation
II. Inter-Stream Conservation:
Consistent Additive Rules (e.g., Lever Arm Rules)
50
DERIVATION OF CLUSTERS
Make each operator dimensionless by dividing
by a reference value:
Define the Augmented Property (AUP) index
ref
i
sii
si
p
)( ,
,
CN
i
sisAUP1
,
Define the property cluster:
s
si
siAUP
C,
,
Describe mixing rule for each raw property in operator
form:
)()( ,
1
_
sii
N
s
sii pxps
51
s sx AUP
AUP
s
where
where
_____
__
AUP
C ii
sN
s
ss AUPxAUP1
______
s
s
N
s
sisref
i
sii
N
s
s
ref
i
iii x
pxp
1
,
,
1
__
)()(
sN
s
sisi
AUP
xC
1_____
,_
si
N
s
si CCs
,
1
_
Ci s
i
, 111
,
1
,
s
s
s
N
i
siN
i
siAUP
AUP
AUPC
C
C
Inter-stream Conservation (Revised Lever Arm Equation
for Property Clusters)
Intra-stream Conservation
Shelley, M. D. and M. M. El-Halwagi, 2000, "Componentless Design of Recovery and Allocation Systems:
A Functionality-Based Clustering Approach", Comp. Chem. Eng., 24, 2081-2091
52
OUTLINE
o Motivating Example and Observations
o Overview of Process Integration and Design
o Mass Integration
o Property Integration
o Clustering
Visualization tools
o Simultaneous Process and Molecular Design
o Conclusions
53
)
k1C
k2Ck3C
),,( min,
min,
max, sk3sk2sk1
),,( min,
max,
max, sk3sk2sk1
),,( min,
max,
min, sk3sk2sk1 ,,( max
,min
,min
, sk3sk2sk1
),,( max,
min,
max, sk3sK2sk1
),,( max,
max,
min, sk3sk2sk1
IDENTIFYING BOUNDARIES OF FEASIBILITY REGION (BFR)
ref
i
iii
p
_
_ )(
max
,1,1
min
,1 sksksk ppp
max
,2,2
min
,2 sksksk ppp
max
,3,3
min
,3 sksksk ppp
• BFR defined uniquely defined
by six points
• Sides emanating from apexes
El-Halwagi, M. M., I. M. Glasgow, M. R. Eden, and X. Qin, “Property Integration:
Componentless Design Techniques and Visualization Tools”, AIChE J., 50(8), 1854-1869 (2004)
54
C1
C2 C3
W1
F
Sink
W2
USEFUL CHARACTERISTICS OF CLUSTERS
Mixing
Zone
55
C1
C2 C3
W
F
a
b
c
Optimum F
Sink
CostW < CostF
DETERMINATION OF OPTIMAL BLENDS
Graphical Characteristics of Clusters
56
C1
C2 C3
W1
F
F
Sink
W2
Mixing point
Graphical Characteristics of Clusters
CostW < CostF
DETERMINATION OF OPTIMAL BLENDS
57
C1
C2 C3
W
F
Sink
Wint Mixing point
Graphical Characteristics of Clusters
TARGETING MINIMUM EXTENT OF INTERCEPTION
58
OUTLINE
o Motivating Example and Observations
o Overview of Process Integration and Design
o Mass Integration
o Property Integration
o Clustering
o Visualization Tools
Simultaneous Process and Molecular Design
o Conclusions
59
INTEGRARTING PROCESS AND MOLECULAR DESIGN
How to identify
candidate components
???? How to identify
desired property values
?????
Discrete Decisions
(e.g. structural modifications)
Continuous Decisions
(e.g. operating conditions)
Given set of components
to be screened
(e.g. raw materials, MSA's)
Optimize process objectives
to meet desired performance
(e.g. recovery, yield, cost)
Process Design
Discrete Decisions
(e.g. type of compound)
Continuous Decisions
(e.g. operating conditions)
Given set of molecular
groups to be screened
(building blocks)
Optimize molecular structures to
meet given set of property values
(e.g. physical, chemical)
Molecular Design
Eljack, F. M. Eden, V. Kazantzi, X. Qin, and M. M. El-Halwagi, “Simultaneous Process and Molecular Design-
A Property Based Approach”; AIChE J., 35(5), 1232-1239 (2007)
60
REVERSE PROBLEM FORMULATION FOR
SIMULTANEOUS PROCESS AND MOLECULAR DESIGN
Discrete Decisions
(e.g. structural modifications)
Continuous Decisions
(e.g. operating conditions)
Designed components
(e.g. raw materials, MSA's)
Process Design
Discrete Decisions
(e.g. type of compound, number of functional groups)
Continuous Decisions
(e.g. operating conditions)
Given set of molecular groups to be screened
(building blocks)
Molecular Design
Desired process performance
(e.g. recovery, yield, cost)
Target for Feasibility
Region of Molecules (constraints on molecular
properties) based on desired
process performance
Eljack, F. T., A.F. Abdelhady, M. R. Eden, F. Gabriel, X. Qin, and M. M. El-Halwagi, “Targeting Optimum Resource
Allocation Using Reverse Problem Formulations & Property Clustering Techniques," Comp. Chem. Eng., 29, 2304-2317 (2005)
61
SOLVING THE REVERSE PROBLEM FORMULATION: HOW
TO OBTAIN TARGETS FOR MOLECULAR DESIGN?
Mathematical techniques:
min/max fresh properties
subject to:
process model
constraints
desired targets
Visualization techniques
- Pinch analysis
- Clustering
j=Nsinks
j=1
j=1
Waste
Sources sinks
i=1
i=2
i=Nsources
Molecular
design
Functional
Groupsg1
gNG
Attainable region
62
PROPERTY-BASED PINCH ANALYSIS
FOR REVERSE PROBLEM FORMULATION
Kazantzi, V., X. Qin, M. El-Halwagi, F. Eljack, and M. Eden, “Simultaneous Process and Molecular Design
through Property Clustering- A Visualization Tool”, Ind. Eng. Chem. Res., 46, 3400-3409 (2007)
Source
Composite
Curve
Sink
Composite
Curve
Waste
Pinch
Flowrate
Property
Load
Fresh
63
C1
C2 C3
W
Sink
CLUSTERING VISUALIZATION TECHNIQUES
FOR REVERSE PROBLEM FORMULATION
Feasibility region
for molecular design
?
Eljack, F. M. Eden, V. Kazantzi, X. Qin, and M. M. El-Halwagi, “Simultaneous Process and Molecular Design-
A Property Based Approach”; AIChE J., 35(5), 1232-1239 (2007)
64
CASE STUDY
65
Motivating Example:
How to Reduce Cost of Solvents via Design Improvements?
Targeted Properties for Solvents
Density, Vapor Pressure, Sulfur
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Spent
Organics
Metal
Recycled Solvent
Degreased
Metal
Solvent 2
36.6 kg/min
4.4 kg/min Evaporated Solvent
off-Gas to Flare
Solvent 1
Waste
Gaseous
Waste
66
Experimental Data for Condensate:
44.1
1
44.1_____
s
N
s
s RVPxRVPs
r
r
sN
s s
sx
1_
1
%)(%)(1
__
wtSxwtS s
N
s
s
s
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
270 280 290 300 310 320
Temperature (K)
Flo
wra
te (
kg
/min
)
0.00
1.00
2.00
3.00
4.00
5.00
6.00
270 280 290 300 310 320
Temperature (K)
Reid
Va
po
r P
res
su
re
(atm
)
0
100
200
300
400
500
600
700
800
900
270 280 290 300 310 320
Temperature (K)
Den
sit
y (
kg
/m 3
)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
270 280 290 300 310 320
Temperature (K)
Su
lfu
r c
on
ten
t
(weig
ht%
)
67
Degreaser
DEVELOPING OPTIMAL RECYCLE
CS
CRVP rC
260 K
270 K
255 K
245 K
240 K
250 K
0.00 0.20 0.60 0.40 0.80 1.00
0.80
0.60
0.40
0.20
Condensate
247 K optimum
Temperature for
minimum cost
240 K optimum
temperature for
minimum fresh Absrober
Fresh
No possible recycle
of condensate
Degreaser: Absrober
Shelley, M. D. and M. M. El-Halwagi, 2000, "Componentless Design of Recovery and Allocation Systems:
A Functionality-Based Clustering Approach", Comp. Chem. Eng., 24, 2081-2091
68
Ab
so
rber
Solvent
Regeneration Degreasing
Finishing
Process
To Flare
Spent
Organics
Metal
Recycled Solvent
Degreased
Metal
Solvent 2 6.8 kg/s
Evaporated
Solvent
Off-Gas
Solvent 1 4.4 kg/s
Lean
Off-Gas
Condensation
247 K
$874,000/yr
(43% cost reduction)
OPTIMUM SOLUTION
Waste
Gaseous
Waste
69
ONGOING RESEARCH
- Property-based modeling
- Overall targeting of properties for the whole process
- Property-based in-process modifications
- Simultaneous molecular design and property integration
in the functional-group domain
- Property-based scheduling
-Macroscopic tools for environmental systems
70
CONCLUSIONS
- Process integration yields insights unseen by unit-based design approaches
-Targeting sets performance benchmarks ahead of detailed design - Property integration process design based on functionalities - Clustering concept for inter- and intra-stream conservation of properties - Visualization tools for process insights and optimization - Mathematical tools for more complex and higher order problems
- Reverse problem framework for simultaneous process and molecular design
71
Acknowledgments
Property-Integration Collaborators: Drs. Mark Shelley, Vasiliki Kazantzi, Mario Eden, Fadwa Eljack,
Xiaoyun Qin, Dominic Foo, Denny Ng, Arturo Jiménez-Gutiérrez,
and José María Ponce Ortega
Financial support:
• Funding agencies
NSF, DOE, USDA, EPA, THWRC, GCHSRC, DoEd, NASA
• Industrial Funding/Applications:
Dow, Chevron, GE, Barwa, TetraPointFuels, DuPont, Byogy
• Endowment: Mr. Artie McFerrin
72
Special Acknowledgment: my graduate students
2006
2011