Functionality-Based Process Design - Σχολή … NTUA_El...3 Alternative: Recovery and Recycle...

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


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


Ab

so

rber

Solvent


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


Ab

so

rber

Solvent


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


Ab

so

rber

Solvent


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


Ab

so

rber

Solvent


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


Ab

so

rber

Solvent


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


Ab

so

rber

Solvent


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


Ab

so

rber

Solvent


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


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 Overview of Process Integration and Design

Mass Integration


o Clustering

o Visualization tools


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 Mass integration

Property Integration

o Clustering

o Visualization tools


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 Mass Integration


o Clustering

Visualization tools


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


CostW < CostF

DETERMINATION OF OPTIMAL BLENDS

57

C1

C2 C3

W

F

Sink

Wint Mixing point


TARGETING MINIMUM EXTENT OF INTERCEPTION

58

OUTLINE



o Mass 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)



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)



Designed components

(e.g. raw materials, MSA's)

Process Design

Discrete Decisions

(e.g. type of compound, number of functional groups)



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


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


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

73

Ευχαριστώ πολύ

Questions?

http://www.omniglot.com/soundfiles/greek/thanksvm_el.mp3



Functionality-Based Process Design - Σχολή … NTUA_El...3 Alternative: Recovery and Recycle...

Documents

Transcript of Functionality-Based Process Design - Σχολή … NTUA_El...3 Alternative: Recovery and Recycle...