1. Norwegian University of Science and Technology
Department of Energy and Process Engineering
Energy Efficiency in Process Plants with emphasis on Heat
Exchanger Networks
Optimization, Thermodynamics and Insight
Supervisor Candidate
Prof. Truls Gundersen Rahul Anantharaman
6th December 2011

2. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

3. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

4. Energy Integration
Energy integration is defined as systematic methods for generating
integrated energy recovery systems.

5. Energy Integration
Energy integration is defined as systematic methods for generating
integrated energy recovery systems.

6. Energy Integration
Energy integration is defined as systematic methods for generating
integrated energy recovery systems.

7. Energy Integration

8. Energy Integration

9. Energy Integration

10. Energy Integration

11. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

12. Objectives
Develop a systematic methodology based on thermodynamic
principles to integrate energy intensive processes while serving as a
screening tool for subsequent heat integration.
Develop a mathematical programming based approach using
thermodynamics and insight for solving industrial sized HENS
problems while including industrial realism and avoiding heuristics
and simplifications.
Develop a semi-automatic design tool that allows significant user
interaction to identify near-optimal and practical networks.

13. Objectives
Develop a systematic methodology based on thermodynamic
principles to integrate energy intensive processes while serving as a
screening tool for subsequent heat integration.
Develop a mathematical programming based approach using
thermodynamics and insight for solving industrial sized HENS
problems while including industrial realism and avoiding heuristics
and simplifications.
Develop a semi-automatic design tool that allows significant user
interaction to identify near-optimal and practical networks.

14. Objectives
Develop a systematic methodology based on thermodynamic
principles to integrate energy intensive processes while serving as a
screening tool for subsequent heat integration.
Develop a mathematical programming based approach using
thermodynamics and insight for solving industrial sized HENS
problems while including industrial realism and avoiding heuristics
and simplifications.
Develop a semi-automatic design tool that allows significant user
interaction to identify near-optimal and practical networks.

15. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

16. Motivation
Need a tool for energy integration of energy intensive plants like
methanol production where there is a large interplay between thermal,
mechanical and chemical energy
Pinch Analysis
Developed for heat recovery networks and later expanded to entire sites
Powerful graphical tool
Deals only with heat recovery: pressure, composition changes are not
considered
Exergy Analysis
Identifies major causes of thermodynamic imperfection
Lacks simple representations

17. Motivation
Need a tool for energy integration of energy intensive plants like
methanol production where there is a large interplay between thermal,
mechanical and chemical energy
Pinch Analysis
Developed for heat recovery networks and later expanded to entire sites
Powerful graphical tool
Deals only with heat recovery: pressure, composition changes are not
considered
Exergy Analysis
Identifies major causes of thermodynamic imperfection
Lacks simple representations

18. Motivation
Need a tool for energy integration of energy intensive plants like
methanol production where there is a large interplay between thermal,
mechanical and chemical energy
Pinch Analysis
Developed for heat recovery networks and later expanded to entire sites
Powerful graphical tool
Deals only with heat recovery: pressure, composition changes are not
considered
Exergy Analysis
Identifies major causes of thermodynamic imperfection
Lacks simple representations

19. Motivation
Need a tool for energy integration of energy intensive plants like
methanol production where there is a large interplay between thermal,
mechanical and chemical energy
Pinch Analysis
Developed for heat recovery networks and later expanded to entire sites
Powerful graphical tool
Deals only with heat recovery: pressure, composition changes are not
considered
Exergy Analysis
Identifies major causes of thermodynamic imperfection
Lacks simple representations

20. Motivation
Need a tool for energy integration of energy intensive plants like
methanol production where there is a large interplay between thermal,
mechanical and chemical energy
Pinch Analysis
Developed for heat recovery networks and later expanded to entire sites
Powerful graphical tool
Deals only with heat recovery: pressure, composition changes are not
considered
Exergy Analysis
Identifies major causes of thermodynamic imperfection
Lacks simple representations

21. Motivation
Need a tool for energy integration of energy intensive plants like
methanol production where there is a large interplay between thermal,
mechanical and chemical energy
Pinch Analysis
Developed for heat recovery networks and later expanded to entire sites
Powerful graphical tool
Deals only with heat recovery: pressure, composition changes are not
considered
Exergy Analysis
Identifies major causes of thermodynamic imperfection
Lacks simple representations

22. Motivation
Need a tool for energy integration of energy intensive plants like
methanol production where there is a large interplay between thermal,
mechanical and chemical energy
Pinch Analysis
Developed for heat recovery networks and later expanded to entire sites
Powerful graphical tool
Deals only with heat recovery: pressure, composition changes are not
considered
Exergy Analysis
Identifies major causes of thermodynamic imperfection
Lacks simple representations

23. Objective
Develop a new methodology for enery intergration of process involving
heat and pressure exchange
Thermodynamic approach
Incorporates pressure and composition changes together with
temperature levels
Graphical representation
Allows visualization of energy transfer between process units and
streams

24. Search for a quality parameter

25. Search for a quality parameter

26. Search for a quality parameter

27. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

28. Energy level as quality parameter
Ishida and co-workers defined direction factor D as
T0 ∆S
D=
∆H
D can lead to negative values, hence Ishida and co-workers defined
availability factor as
∆E T0 ∆S
A= =1−
∆H ∆H
Feng and Zhu defined energy level as
exergy
Ω=
energy

29. Energy level as quality parameter
Ishida and co-workers defined direction factor D as
T0 ∆S
D=
∆H
D can lead to negative values, hence Ishida and co-workers defined
availability factor as
∆E T0 ∆S
A= =1−
∆H ∆H
Feng and Zhu defined energy level as
exergy
Ω=
energy

30. Energy level as quality parameter
Ishida and co-workers defined direction factor D as
T0 ∆S
D=
∆H
D can lead to negative values, hence Ishida and co-workers defined
availability factor as
∆E T0 ∆S
A= =1−
∆H ∆H
Feng and Zhu defined energy level as
exergy
Ω=
energy

31. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

32. R. Anantharaman et al. / Applied Thermal Engineering 26 (2006) 1378–1384 1381
The graphical representation also shows the perfor- This is the case in a flash unit, where the quantity of en-
mance of the individual process units indicating the ex- ergy (enthalpy) is a constant, whereas the energy quality
Energy integration of a methanol plant
ergy gain/loss trends. is reduced at the outlet of the unit. Such units do not
It may not always be possible to transfer energy from represent energy sources or sinks and illustrate the fact
Process at higher energy value to a unit at lower energy
a unit that not all energy level changing units can be used for
value. Such integration may be limited by certain pro- energy integration.
cess parameters or unavoidable exergy losses in the sys- When steam is present at a high energy level in
tem. A decrease in energy level for a unit can be caused the plant, it can be considered to go through an imagi-
by a decrease in exergy while enthalpy remains constant. nary process to reach a lower energy level. The process
Fig. 2. HYSYS simulation case study—methanol process.
0.5
Sec Reformer Product Cooler
0.45

33. Energy integration of a methanol plant
ELCC
Fig. 2. HYSYS simulation case study—methanol process.
0.5
Sec Reformer Product Cooler
0.45
Sec Reformer Product Cooler, Water Jacket Steam
0.4
0.35
Sec Reformer Product Cooler, Water Jacket Steam,
MeOH Raw Product Cooler
Energy Level
0.3
Steam Generator
Sec Reformer Product Cooler, MeOH
0.25 Raw Product Cooler
MeOH Raw Product Cooler Steam Generator, Syngas Compressor, MeOH Reactor Feed Preheater
0.2
0.15 Steam Generator, Syngas Compressor
Steam Generator, MeOH Recycle Compressor, Syngas Compressor
0.1
Steam Generator MeOH Recycle Compressor
0.05 Energy Level Increasing
Steam Generator Energy Level Decreasing
0
0 50 100 150 200 250 300 350 400
Enthalpy (MW)
Fig. 3. ELCCs for the methanol process case study.

34. Energy integration of a methanol plant
ELCC - Analysis
Integrate Secondary Reformer Product Cooler with MeOH Reactor
Feed Preheater
Integrate Secondary Reformer Product Cooler with Steam Generator
Integrate the raw product from MeOH reactor with SynGas
Compressor and MeOH Recycle Compressor by expanding Raw
Product Vapor stream to generate electric power
Run the steam generated from MeOH Reactor Water Jacket
through a turbine to produce electricity
Energy targeting is required to evaluate potential savings.

35. Energy integration of a methanol plant
Integration results
Process Unit Energy Consumption (MW)
Before Integration Target After Integration
Sec reformer Product Cooler 265,7 64,7
Syn Gas Compressor 11,45 11,5
Steam Generator 196,7 196,7
MeOH Reactor Feed Preheater 4,3 4,3
MeOH Recycle Compressor 14,3 14,3
Raw Product Cooler 70,8 46,4
Water Jacket Steam Turbine - 1,8
Raw Product Expander - 24,4
Hot Utility/Fuel 201 0 0
Cold Utility 336,5 113 111,1
Electricity Import 25,7 -2 -0,5

36. Energy integration of a methanol plant
Integration results
Process Unit Energy Consumption (MW)
Before Integration Target After Integration
Sec reformer Product Cooler 265,7 64,7
Syn Gas Compressor 11,45 11,5
Steam Generator 196,7 196,7
MeOH Reactor Feed Preheater 4,3 4,3
MeOH Recycle Compressor 14,3 14,3
Raw Product Cooler 70,8 46,4
Water Jacket Steam Turbine - 1,8
Raw Product Expander - 24,4
Hot Utility/Fuel 201 0 0
Cold Utility 336,5 113 111,1
Electricity Import 25,7 -2 -0,5

37. Energy integration of a methanol plant
Integration results
Process Unit Energy Consumption (MW)
Before Integration Target After Integration
Sec reformer Product Cooler 265,7 64,7
Syn Gas Compressor 11,45 11,5
Steam Generator 196,7 196,7
MeOH Reactor Feed Preheater 4,3 4,3
MeOH Recycle Compressor 14,3 14,3
Raw Product Cooler 70,8 46,4
Water Jacket Steam Turbine - 1,8
Raw Product Expander - 24,4
Hot Utility/Fuel 201 0 0
Cold Utility 336,5 113 111,1
Electricity Import 25,7 -2 -0,5

38. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

39. Conclusions and further work
Conclusions
A new energy integration methodology that can be applied to a
wide range of processes has been developed
Synergy of Exergy Analysis and composite curves of Pinch Analysis
Pressure, Temperature and Composition effects are taken into account
First methodological attempt to represent thermal, mechanical and
chemical energy in graphical form
Energy integration of a methanol plant was performed using this
methodology

40. Conclusions and further work
Conclusions
A new energy integration methodology that can be applied to a
wide range of processes has been developed
Synergy of Exergy Analysis and composite curves of Pinch Analysis
Pressure, Temperature and Composition effects are taken into account
First methodological attempt to represent thermal, mechanical and
chemical energy in graphical form
Energy integration of a methanol plant was performed using this
methodology

41. Conclusions and further work
Conclusions
A new energy integration methodology that can be applied to a
wide range of processes has been developed
Synergy of Exergy Analysis and composite curves of Pinch Analysis
Pressure, Temperature and Composition effects are taken into account
First methodological attempt to represent thermal, mechanical and
chemical energy in graphical form
Energy integration of a methanol plant was performed using this
methodology

42. Conclusions and further work
Further work
Targeting methodology must be modified to take process heat
integration into consideration
Optimization scheme would be best suited
Substantial work required to develop a complete systematic
framework that incorporates thermal and mechanical integration
Utilization of chemical exergy in integration studies should be
explored

43. Conclusions and further work
Further work
Targeting methodology must be modified to take process heat
integration into consideration
Optimization scheme would be best suited
Substantial work required to develop a complete systematic
framework that incorporates thermal and mechanical integration
Utilization of chemical exergy in integration studies should be
explored

44. Conclusions and further work
Further work
Targeting methodology must be modified to take process heat
integration into consideration
Optimization scheme would be best suited
Substantial work required to develop a complete systematic
framework that incorporates thermal and mechanical integration
Utilization of chemical exergy in integration studies should be
explored

45. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

46. Heat Exchanger Network Synthesis
For a given set of hot and cold process streams as well as external
utilities, design a heat exchanger network that minimizes Total
Annualized Cost (TAC).
TAC = Capital Cost + Energy Cost
Sequential Framework Engine

47. Heat Exchanger Network Synthesis
Solution methods
1. Evolutionary methods such as Pinch Design Method
2. Sequential synthesis methods
3. Simultaneous synthesis methods
4. Stochastic optimization methods

48. Heat Exchanger Network Synthesis
Solution methods
1. Evolutionary methods such as Pinch Design Method
2. Sequential synthesis methods
3. Simultaneous synthesis methods
4. Stochastic optimization methods

49. Heat Exchanger Network Synthesis
Solution methods
1. Evolutionary methods such as Pinch Design Method
2. Sequential synthesis methods
3. Simultaneous synthesis methods
4. Stochastic optimization methods

50. Heat Exchanger Network Synthesis
Solution methods
1. Evolutionary methods such as Pinch Design Method
2. Sequential synthesis methods
3. Simultaneous synthesis methods
4. Stochastic optimization methods

51. Heat Exchanger Network Synthesis
Timeline

52. Heat Exchanger Network Synthesis
Timeline

53. Heat Exchanger Network Synthesis
Timeline

54. Heat Exchanger Network Synthesis
Timeline

55. Heat Exchanger Network Synthesis
Timeline

56. Heat Exchanger Network Synthesis
Timeline

57. Heat Exchanger Network Synthesis
Timeline

58. Heat Exchanger Network Synthesis
Timeline

59. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

60. HENS in the 21st century
Review
225 references published from 2000-2008
216 journal papers
48 jounals
43 countries
4 conference proceedings
10 Ph.D. theses
4 textbooks

61. HENS in the 21st century
Review
225 references published from 2000-2008
216 journal papers
48 jounals
43 countries
4 conference proceedings
10 Ph.D. theses
4 textbooks

62. HENS in the 21st century
Review
225 references published from 2000-2008
216 journal papers
48 jounals
43 countries
4 conference proceedings
10 Ph.D. theses
4 textbooks

63. HENS in the 21st century
Review
225 references published from 2000-2008
216 journal papers
48 jounals
43 countries
4 conference proceedings
10 Ph.D. theses
4 textbooks

64. HENS in the 21st century
Review
225 references published from 2000-2008
216 journal papers
48 jounals
43 countries
4 conference proceedings
10 Ph.D. theses
4 textbooks

65. HENS in the 21st century
Review
225 references published from 2000-2008
216 journal papers
48 jounals
43 countries
4 conference proceedings
10 Ph.D. theses
4 textbooks

66. HENS in the 21st century
Review
45
40
35
30
25
20
15
10
5
0
2000 2001 2002 2003 2004 2005 2006 2007 2008

67. HENS in the 21st century
Review

68. HENS in the 21st century
Review
HENS still an active area of research interest
Over 25% of references devoted to case studies
Pinch Analysis based evolutionary methods dominate
Sustained interest in simultaneous MINLP methods
Yee and Grossmann (1990) superstructure
Pressure drop and detailed HX design considerations
Small test problems
Number of references related to genetic programming and other
meta-heuristic methods increasing in frequency

69. HENS in the 21st century
Review
HENS still an active area of research interest
Over 25% of references devoted to case studies
Pinch Analysis based evolutionary methods dominate
Sustained interest in simultaneous MINLP methods
Yee and Grossmann (1990) superstructure
Pressure drop and detailed HX design considerations
Small test problems
Number of references related to genetic programming and other
meta-heuristic methods increasing in frequency

70. HENS in the 21st century
Review
HENS still an active area of research interest
Over 25% of references devoted to case studies
Pinch Analysis based evolutionary methods dominate
Sustained interest in simultaneous MINLP methods
Yee and Grossmann (1990) superstructure
Pressure drop and detailed HX design considerations
Small test problems
Number of references related to genetic programming and other
meta-heuristic methods increasing in frequency

71. HENS in the 21st century
Review
HENS still an active area of research interest
Over 25% of references devoted to case studies
Pinch Analysis based evolutionary methods dominate
Sustained interest in simultaneous MINLP methods
Yee and Grossmann (1990) superstructure
Pressure drop and detailed HX design considerations
Small test problems
Number of references related to genetic programming and other
meta-heuristic methods increasing in frequency

72. HENS in the 21st century
Review
HENS still an active area of research interest
Over 25% of references devoted to case studies
Pinch Analysis based evolutionary methods dominate
Sustained interest in simultaneous MINLP methods
Yee and Grossmann (1990) superstructure
Pressure drop and detailed HX design considerations
Small test problems
Number of references related to genetic programming and other
meta-heuristic methods increasing in frequency

73. HENS in the 21st century
Review
HENS still an active area of research interest
Over 25% of references devoted to case studies
Pinch Analysis based evolutionary methods dominate
Sustained interest in simultaneous MINLP methods
Yee and Grossmann (1990) superstructure
Pressure drop and detailed HX design considerations
Small test problems
Number of references related to genetic programming and other
meta-heuristic methods increasing in frequency

74. HENS in the 21st century
Review
HENS still an active area of research interest
Over 25% of references devoted to case studies
Pinch Analysis based evolutionary methods dominate
Sustained interest in simultaneous MINLP methods
Yee and Grossmann (1990) superstructure
Pressure drop and detailed HX design considerations
Small test problems
Number of references related to genetic programming and other
meta-heuristic methods increasing in frequency

75. HENS in the 21st century
Review
HENS still an active area of research interest
Over 25% of references devoted to case studies
Pinch Analysis based evolutionary methods dominate
Sustained interest in simultaneous MINLP methods
Yee and Grossmann (1990) superstructure
Pressure drop and detailed HX design considerations
Small test problems
Number of references related to genetic programming and other
meta-heuristic methods increasing in frequency

76. HENS in the 21st century
Review
Conclusions with a focus on Mathematical Programming
Significant developments in HENS using mathematical
programming methods.
Synthesis of large scale HENS problems without simplifications and
heuristics have been lacking.
An area that requires more research for mathematical programming
based approaches to be used in the industry.

77. HENS in the 21st century
Review
Conclusions with a focus on Mathematical Programming
Significant developments in HENS using mathematical
programming methods.
Synthesis of large scale HENS problems without simplifications and
heuristics have been lacking.
An area that requires more research for mathematical programming
based approaches to be used in the industry.

78. HENS in the 21st century
Review
Conclusions with a focus on Mathematical Programming
Significant developments in HENS using mathematical
programming methods.
Synthesis of large scale HENS problems without simplifications and
heuristics have been lacking.
An area that requires more research for mathematical programming
based approaches to be used in the industry.

79. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

80. Motivation for the Sequential Framework
Pinch based methods for network design
Improper trade-off handling
Time consuming
Several topological traps
MINLP methods for network design
Severe numerical problems
Difficult user interaction
Fail to solve large scale problems
Stochastic optimization methods for network design
Non-rigorous algorithms
Quality of solution depends on time spent on search

81. Motivation for the Sequential Framework
Pinch based methods for network design
Improper trade-off handling
Time consuming
Several topological traps
MINLP methods for network design
Severe numerical problems
Difficult user interaction
Fail to solve large scale problems
Stochastic optimization methods for network design
Non-rigorous algorithms
Quality of solution depends on time spent on search

82. Motivation for the Sequential Framework
Pinch based methods for network design
Improper trade-off handling
Time consuming
Several topological traps
MINLP methods for network design
Severe numerical problems
Difficult user interaction
Fail to solve large scale problems
Stochastic optimization methods for network design
Non-rigorous algorithms
Quality of solution depends on time spent on search

83. Motivation for the Sequential Framework
HENS techniques decompose the main problem
Pinch Design Method is sequential and evolutionary
Simultaneous MINLP methods let math considerations define the
decomposition
The Sequential Framework decomposes the problem into
subproblems based on insight of the HENS problem
Engineer acts as optimizer at the top level
Quantitative and qualitative considerations included

84. Motivation for the Sequential Framework
HENS techniques decompose the main problem
Pinch Design Method is sequential and evolutionary
Simultaneous MINLP methods let math considerations define the
decomposition
The Sequential Framework decomposes the problem into
subproblems based on insight of the HENS problem
Engineer acts as optimizer at the top level
Quantitative and qualitative considerations included

85. Motivation for the Sequential Framework
HENS techniques decompose the main problem
Pinch Design Method is sequential and evolutionary
Simultaneous MINLP methods let math considerations define the
decomposition
The Sequential Framework decomposes the problem into
subproblems based on insight of the HENS problem
Engineer acts as optimizer at the top level
Quantitative and qualitative considerations included

86. Motivation for the Sequential Framework
HENS techniques decompose the main problem
Pinch Design Method is sequential and evolutionary
Simultaneous MINLP methods let math considerations define the
decomposition
The Sequential Framework decomposes the problem into
subproblems based on insight of the HENS problem
Engineer acts as optimizer at the top level
Quantitative and qualitative considerations included

87. Ultimate Goal
Solve Industrial Size Problems
Defined to involve 30 or more streams
Include Industrial Realism
Multiple and Complex Utilities
Constraints in Heat Utilization (Forbidden matches)
Heat exchanger models beyond pure countercurrent
Avoid Heuristics and Simplifications
No global or fixed ∆Tmin
No Pinch Decomposition
Develop a Semi-Automatic Design Tool
EXCEL/VBA (preprocessing and front end)
MATLAB (mathematical processing)
GAMS (core optimization engine)
Allow significant user interaction and control
Identify near optimal and practical networks

88. Ultimate Goal
Solve Industrial Size Problems
Defined to involve 30 or more streams
Include Industrial Realism
Multiple and Complex Utilities
Constraints in Heat Utilization (Forbidden matches)
Heat exchanger models beyond pure countercurrent
Avoid Heuristics and Simplifications
No global or fixed ∆Tmin
No Pinch Decomposition
Develop a Semi-Automatic Design Tool
EXCEL/VBA (preprocessing and front end)
MATLAB (mathematical processing)
GAMS (core optimization engine)
Allow significant user interaction and control
Identify near optimal and practical networks

89. Ultimate Goal
Solve Industrial Size Problems
Defined to involve 30 or more streams
Include Industrial Realism
Multiple and Complex Utilities
Constraints in Heat Utilization (Forbidden matches)
Heat exchanger models beyond pure countercurrent
Avoid Heuristics and Simplifications
No global or fixed ∆Tmin
No Pinch Decomposition
Develop a Semi-Automatic Design Tool
EXCEL/VBA (preprocessing and front end)
MATLAB (mathematical processing)
GAMS (core optimization engine)
Allow significant user interaction and control
Identify near optimal and practical networks

90. Ultimate Goal
Solve Industrial Size Problems
Defined to involve 30 or more streams
Include Industrial Realism
Multiple and Complex Utilities
Constraints in Heat Utilization (Forbidden matches)
Heat exchanger models beyond pure countercurrent
Avoid Heuristics and Simplifications
No global or fixed ∆Tmin
No Pinch Decomposition
Develop a Semi-Automatic Design Tool
EXCEL/VBA (preprocessing and front end)
MATLAB (mathematical processing)
GAMS (core optimization engine)
Allow significant user interaction and control
Identify near optimal and practical networks

91. Sequential Framework
The engine
Tool: SeqHENS
3 way trade-off
Compromise between Pinch Design and MINLP methods

92. Sequential Framework
The engine
Tool: SeqHENS
3 way trade-off
Compromise between Pinch Design and MINLP methods

93. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

94. Example 1 - 7TP1
Stream Tin Tout mCp ∆H h
K K kW/K kW kW/m2 K
H1 626 586 9.802 392.08 1.25
H2 620 519 2.931 296.03 0.05
H3 528 353 6.161 1078.18 3.20
C1 497 613 7.179 832.76 0.65
C2 389 576 0.641 119.87 0.25
C3 326 386 7.627 457.62 0.33
C4 313 566 1.69 427.57 3.20
ST 650 650 - - 3.50
CW 293 308 - - 3.50
Exchanger cost ($) = 8,600 + 670A0.83 (A is in m2 )

95. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

96. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

97. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

98. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

99. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

100. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

101. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

102. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

103. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

104. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

105. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

106. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

107. Example 1 - 7TP1
Looping to the solution
HRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)
Umin = 8 units
Soln. No U EMAT (K) HLD TAC ($)
1 8 2.5 A 199,914
2 8 5 A 199,914
3 8 7.5 - No Soln
4 9 2.5 A 147,861
5 9 2.5 B 151,477
6 9 5 A 147,867
7 9 5 B 151,508
8 9 7.5 A 149,025
9 9 7.5 B 149,224
10 10 2.5 A 164,381
11 10 5 A 167,111
12 10 7.5 A 164,764

108. Example 1 - 7TP1
Best solution

109. Example 1 - 7TP1
Comparison of results
No. of units Area (m2 ) Cost ($)
Colberg and Morari (1990) 22 173.6
Colberg and Morari (1990) 12 188.9 177,385
Yee and Grossmann (1990) 9 217.8 150,998
Isiafade and Fraser (2007) 10 251.5 168,700
Sequential Framework 9 189.7 147, 861

110. EMAT in the Sequential Framework
Chosing EMAT is not straightforward
EMAT set too low (close to zero)
non-vertical heat transfer (m = n) will have very small ∆TLM,mn and
very large penalties in the objective function
EMAT set too high (close to HRAT)
Potentially good HLDs will be excluded from the feasible set of
solutions
∆TLM,mn is a term included in the objective function and depends
explicitly on EMAT
EMAT is an optimizing variable in this formulation
EMAT comes into play when there is an extra degree of freedom in the
system - number of units greater than Umin

111. EMAT in the Sequential Framework
Chosing EMAT is not straightforward
EMAT set too low (close to zero)
non-vertical heat transfer (m = n) will have very small ∆TLM,mn and
very large penalties in the objective function
EMAT set too high (close to HRAT)
Potentially good HLDs will be excluded from the feasible set of
solutions
∆TLM,mn is a term included in the objective function and depends
explicitly on EMAT
EMAT is an optimizing variable in this formulation
EMAT comes into play when there is an extra degree of freedom in the
system - number of units greater than Umin

112. EMAT in the Sequential Framework
Chosing EMAT is not straightforward
EMAT set too low (close to zero)
non-vertical heat transfer (m = n) will have very small ∆TLM,mn and
very large penalties in the objective function
EMAT set too high (close to HRAT)
Potentially good HLDs will be excluded from the feasible set of
solutions
∆TLM,mn is a term included in the objective function and depends
explicitly on EMAT
EMAT is an optimizing variable in this formulation
EMAT comes into play when there is an extra degree of freedom in the
system - number of units greater than Umin

113. EMAT in the Sequential Framework
Chosing EMAT is not straightforward
EMAT set too low (close to zero)
non-vertical heat transfer (m = n) will have very small ∆TLM,mn and
very large penalties in the objective function
EMAT set too high (close to HRAT)
Potentially good HLDs will be excluded from the feasible set of
solutions
∆TLM,mn is a term included in the objective function and depends
explicitly on EMAT
EMAT is an optimizing variable in this formulation
EMAT comes into play when there is an extra degree of freedom in the
system - number of units greater than Umin

114. Example 2 - 15TP1
Stream Tin Tout mCp ∆H h
( ) ( ) (kW/ ) (kW) (kW/m2 )
H1 180 75 30 3150 2
H2 280 120 60 9600 1
H3 180 75 30 3150 2
H4 140 40 30 3000 1
H5 220 120 50 5000 1
H6 180 55 35 4375 2
H7 200 60 30 4200 0.4
H8 120 40 100 8000 0.5
C1 40 230 20 3800 1
C2 100 220 60 7200 1
C3 40 290 35 8750 2
C4 50 290 30 7200 2
C5 50 250 60 12000 2
C6 90 190 50 5000 1
C7 160 250 60 5400 3
ST 325 325 1
CW 25 40 2
Exchanger cost ($) = 8,000 + 500A0.75 (A is in m2 )

115. Example 2 - 15TP1
Looping to the solution
HRAT fixed at 20.35 (Qh,min = 11539.25 kW & Qc,min = 9164.25 kW)
Umin = 14 units
Soln. No U EMAT (C) HLD TAC ($)
1 14 2.5 A 1,565,375
2 15 2.5 A 1,511,047
3 15 2.5 B 1,522,000
4 15 5 A 1,529,968
5 15 5 B 1,532,148
6 16 2.5 A 1,547,353

116. Example 2 - 15TP1
Best solution

117. Example 2 - 15TP1
Comparison of results
The solution given here with a TAC of $1,511,047, slightly lower
cost compared to the solution presented in the original paper by
Bj¨rk and Nordman (2005) (TAC $1,530,063)
o
When only one match was allowed between a pair of streams the
TAC reported by Bj¨rk & Nordman (2005) was $1,568,745
o
The Sequential Framework allows only 1 match between a pair of
streams
Unable to compare the solutions apart from cost as the paper did
not present the networks in their work

118. Example 2 - 15TP1
Comparison of results
The solution given here with a TAC of $1,511,047, slightly lower
cost compared to the solution presented in the original paper by
Bj¨rk and Nordman (2005) (TAC $1,530,063)
o
When only one match was allowed between a pair of streams the
TAC reported by Bj¨rk & Nordman (2005) was $1,568,745
o
The Sequential Framework allows only 1 match between a pair of
streams
Unable to compare the solutions apart from cost as the paper did
not present the networks in their work

119. Example 2 - 15TP1
Comparison of results
The solution given here with a TAC of $1,511,047, slightly lower
cost compared to the solution presented in the original paper by
Bj¨rk and Nordman (2005) (TAC $1,530,063)
o
When only one match was allowed between a pair of streams the
TAC reported by Bj¨rk & Nordman (2005) was $1,568,745
o
The Sequential Framework allows only 1 match between a pair of
streams
Unable to compare the solutions apart from cost as the paper did
not present the networks in their work

120. Example 2 - 15TP1
Comparison of results
The solution given here with a TAC of $1,511,047, slightly lower
cost compared to the solution presented in the original paper by
Bj¨rk and Nordman (2005) (TAC $1,530,063)
o
When only one match was allowed between a pair of streams the
TAC reported by Bj¨rk & Nordman (2005) was $1,568,745
o
The Sequential Framework allows only 1 match between a pair of
streams
Unable to compare the solutions apart from cost as the paper did
not present the networks in their work

121. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

122. Challenges
Combinatorial Explosion
Reason: Binary Variables in MILP models - Minimum Units and Stream Match
Generator sub-problems
Physical and engineering insights will mitigate, not remove, the problem
MILP models are the bottlenecks that limit problem size due to computational
time
Local optima
Reason: Non-convexities in the NLP model
Convex estimators developed for MINLP models are computationally intensive
Time to solve the basic NLP is not a problem
Sequence of MILP and NLP problems considerably easier to solve than MINLP
formulations

123. Challenges - Minimum Units MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved lower bound significantly
Integer cuts
Compulsory matches - Reduced gap
Minimum matches per stream - Results varied
Adding both cuts always reduced model gap
Model reformulation
Model reformulated as set covering problem
4 new formulations developed
Results show marginal improvment of the lower bound
Reformulated model introduce more binary variables and lead to larger
models

124. Challenges - Minimum Units MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved lower bound significantly
Integer cuts
Compulsory matches - Reduced gap
Minimum matches per stream - Results varied
Adding both cuts always reduced model gap
Model reformulation
Model reformulated as set covering problem
4 new formulations developed
Results show marginal improvment of the lower bound
Reformulated model introduce more binary variables and lead to larger
models

125. Challenges - Minimum Units MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved lower bound significantly
Integer cuts
Compulsory matches - Reduced gap
Minimum matches per stream - Results varied
Adding both cuts always reduced model gap
Model reformulation
Model reformulated as set covering problem
4 new formulations developed
Results show marginal improvment of the lower bound
Reformulated model introduce more binary variables and lead to larger
models

126. Challenges - Minimum Units MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved lower bound significantly
Integer cuts
Compulsory matches - Reduced gap
Minimum matches per stream - Results varied
Adding both cuts always reduced model gap
Model reformulation
Model reformulated as set covering problem
4 new formulations developed
Results show marginal improvment of the lower bound
Reformulated model introduce more binary variables and lead to larger
models

127. Challenges - Minimum Units MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved lower bound significantly
Integer cuts
Compulsory matches - Reduced gap
Minimum matches per stream - Results varied
Adding both cuts always reduced model gap
Model reformulation
Model reformulated as set covering problem
4 new formulations developed
Results show marginal improvment of the lower bound
Reformulated model introduce more binary variables and lead to larger
models

128. Challenges - Minimum Units MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved lower bound significantly
Integer cuts
Compulsory matches - Reduced gap
Minimum matches per stream - Results varied
Adding both cuts always reduced model gap
Model reformulation
Model reformulated as set covering problem
4 new formulations developed
Results show marginal improvment of the lower bound
Reformulated model introduce more binary variables and lead to larger
models

129. Challenges - Minimum Units MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved lower bound significantly
Integer cuts
Compulsory matches - Reduced gap
Minimum matches per stream - Results varied
Adding both cuts always reduced model gap
Model reformulation
Model reformulated as set covering problem
4 new formulations developed
Results show marginal improvment of the lower bound
Reformulated model introduce more binary variables and lead to larger
models

130. Challenges - Minimum Units MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved lower bound significantly
Integer cuts
Compulsory matches - Reduced gap
Minimum matches per stream - Results varied
Adding both cuts always reduced model gap
Model reformulation
Model reformulated as set covering problem
4 new formulations developed
Results show marginal improvment of the lower bound
Reformulated model introduce more binary variables and lead to larger
models

131. Challenges - Stream Match Generator MILP
Mitigation measures
Reduce model size
Model size increases with the number of temperature intervals
New procedure devloped for optimum number of temperature intervals
Pre-processing
Fix binary variables
The reduction in solution time is around 3%
Setting a lower bound to the objective based on Bath formula
The model solution time increased!

132. Challenges - Stream Match Generator MILP
Mitigation measures
Reduce model size
Model size increases with the number of temperature intervals
New procedure devloped for optimum number of temperature intervals
Pre-processing
Fix binary variables
The reduction in solution time is around 3%
Setting a lower bound to the objective based on Bath formula
The model solution time increased!

133. Challenges - Stream Match Generator MILP
Mitigation measures
Reduce model size
Model size increases with the number of temperature intervals
New procedure devloped for optimum number of temperature intervals
Pre-processing
Fix binary variables
The reduction in solution time is around 3%
Setting a lower bound to the objective based on Bath formula
The model solution time increased!

134. Challenges - Stream Match Generator MILP
Mitigation measures
Reduce model size
Model size increases with the number of temperature intervals
New procedure devloped for optimum number of temperature intervals
Pre-processing
Fix binary variables
The reduction in solution time is around 3%
Setting a lower bound to the objective based on Bath formula
The model solution time increased!

135. Challenges - Stream Match Generator MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved solution times by 30%
Integer cuts for compusory matches
No appreciable improvement in model solution time
Objective function modified to include binary variables
Solution time reduced by 4%
Improving efficiency of the Branch & Bound method
Setting priorities to binary variables using insight
Model solution time improved by 16%

136. Challenges - Stream Match Generator MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved solution times by 30%
Integer cuts for compusory matches
No appreciable improvement in model solution time
Objective function modified to include binary variables
Solution time reduced by 4%
Improving efficiency of the Branch & Bound method
Setting priorities to binary variables using insight
Model solution time improved by 16%

137. Challenges - Stream Match Generator MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved solution times by 30%
Integer cuts for compusory matches
No appreciable improvement in model solution time
Objective function modified to include binary variables
Solution time reduced by 4%
Improving efficiency of the Branch & Bound method
Setting priorities to binary variables using insight
Model solution time improved by 16%

138. Challenges - Stream Match Generator MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved solution times by 30%
Integer cuts for compusory matches
No appreciable improvement in model solution time
Objective function modified to include binary variables
Solution time reduced by 4%
Improving efficiency of the Branch & Bound method
Setting priorities to binary variables using insight
Model solution time improved by 16%

139. Challenges - Stream Match Generator MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved solution times by 30%
Integer cuts for compusory matches
No appreciable improvement in model solution time
Objective function modified to include binary variables
Solution time reduced by 4%
Improving efficiency of the Branch & Bound method
Setting priorities to binary variables using insight
Model solution time improved by 16%

140. Challenges - Stream Match Generator MILP
Mitigation measures
Model modification
Decreasing big M using physical insight
Improved solution times by 30%
Integer cuts for compusory matches
No appreciable improvement in model solution time
Objective function modified to include binary variables
Solution time reduced by 4%
Improving efficiency of the Branch & Bound method
Setting priorities to binary variables using insight
Model solution time improved by 16%

141. Challenges - Network generation and optimization NLP
Mitigation measures
Developed 4 starting value generators to get “good” initial networks
1. Serial/Parallel heuristic
2. H/H heuristic
3. Stream match generator based heuristic
4. Combinatorial heuristic based on insight
Combinatorial heuristic performed best by ensuring that the NLP
solved for all test cases.

142. Challenges - Network generation and optimization NLP
Mitigation measures
Developed 4 starting value generators to get “good” initial networks
1. Serial/Parallel heuristic
2. H/H heuristic
3. Stream match generator based heuristic
4. Combinatorial heuristic based on insight
Combinatorial heuristic performed best by ensuring that the NLP
solved for all test cases.

143. Challenges - Network generation and optimization NLP
Mitigation measures
Developed 4 starting value generators to get “good” initial networks
1. Serial/Parallel heuristic
2. H/H heuristic
3. Stream match generator based heuristic
4. Combinatorial heuristic based on insight
Combinatorial heuristic performed best by ensuring that the NLP
solved for all test cases.

144. Challenges - Network generation and optimization NLP
Mitigation measures
Developed 4 starting value generators to get “good” initial networks
1. Serial/Parallel heuristic
2. H/H heuristic
3. Stream match generator based heuristic
4. Combinatorial heuristic based on insight
Combinatorial heuristic performed best by ensuring that the NLP
solved for all test cases.

145. Challenges - Network generation and optimization NLP
Mitigation measures
Developed 4 starting value generators to get “good” initial networks
1. Serial/Parallel heuristic
2. H/H heuristic
3. Stream match generator based heuristic
4. Combinatorial heuristic based on insight
Combinatorial heuristic performed best by ensuring that the NLP
solved for all test cases.

146. Outline
Introduction
Process Synthesis and Energy Integration
Objectives
Energy Level Composite Curves
Background
Energy Level and Energy Level Composite Curves
Case study
Conclusions and further work
Heat Exchanger Network Synthesis
Introduction
HENS in the 21st century
Sequential Framework
Introduction
Examples
Challenges
Further work
Contributions

147. Further Work
Minimum number of units sub-problem
Develop heuristics to stop the search after an appropriate solution
time.
Optimum value is reached early in the solution process
Identify subnetworks to get initial lower bound thus tightening the
gap.
Identify “phase transition” for the sub-problem.
Stream match generator sub-problem
Develop cutoff values to be used with CPLEX for cutting parts of
the search tree.
Understand the effect of setting lower bound on the objective.

148. Further Work
Minimum number of units sub-problem
Develop heuristics to stop the search after an appropriate solution
time.
Optimum value is reached early in the solution process
Identify subnetworks to get initial lower bound thus tightening the
gap.
Identify “phase transition” for the sub-problem.
Stream match generator sub-problem
Develop cutoff values to be used with CPLEX for cutting parts of
the search tree.
Understand the effect of setting lower bound on the objective.

149. Further Work
Minimum number of units sub-problem
Develop heuristics to stop the search after an appropriate solution
time.
Optimum value is reached early in the solution process
Identify subnetworks to get initial lower bound thus tightening the
gap.
Identify “phase transition” for the sub-problem.
Stream match generator sub-problem
Develop cutoff values to be used with CPLEX for cutting parts of
the search tree.
Understand the effect of setting lower bound on the objective.

150. Further Work
Minimum number of units sub-problem
Develop heuristics to stop the search after an appropriate solution
time.
Optimum value is reached early in the solution process
Identify subnetworks to get initial lower bound thus tightening the
gap.
Identify “phase transition” for the sub-problem.
Stream match generator sub-problem
Develop cutoff values to be used with CPLEX for cutting parts of
the search tree.
Understand the effect of setting lower bound on the objective.

151. Further Work
Minimum number of units sub-problem
Develop heuristics to stop the search after an appropriate solution
time.
Optimum value is reached early in the solution process
Identify subnetworks to get initial lower bound thus tightening the
gap.
Identify “phase transition” for the sub-problem.
Stream match generator sub-problem
Develop cutoff values to be used with CPLEX for cutting parts of
the search tree.
Understand the effect of setting lower bound on the objective.

152. Further Work
Minimum number of units sub-problem
Develop heuristics to stop the search after an appropriate solution
time.
Optimum value is reached early in the solution process
Identify subnetworks to get initial lower bound thus tightening the
gap.
Identify “phase transition” for the sub-problem.
Stream match generator sub-problem
Develop cutoff values to be used with CPLEX for cutting parts of
the search tree.
Understand the effect of setting lower bound on the objective.

153. Contributions
Exergy based method for energy integration
A novel methodology, “Energy Level Composite Curves” was developed.
Heat exchanger network synthesis review
A review of important developments in Heat Exchanger Network
Synthesis for the period 2000-2008.

154. Contributions
Exergy based method for energy integration
A novel methodology, “Energy Level Composite Curves” was developed.
Heat exchanger network synthesis review
A review of important developments in Heat Exchanger Network
Synthesis for the period 2000-2008.

155. Contributions
Exergy based method for energy integration
A novel methodology, “Energy Level Composite Curves” was developed.
Heat exchanger network synthesis review
A review of important developments in Heat Exchanger Network
Synthesis for the period 2000-2008.

156. Contributions
Exergy based method for energy integration
A novel methodology, “Energy Level Composite Curves” was developed.
Heat exchanger network synthesis review
A review of important developments in Heat Exchanger Network
Synthesis for the period 2000-2008.

157. Contributions
Sequential Framework for heat exchanger network synthesis
1. Identified and rationalized the loops in the Sequential Framework.
2. Showed that stream supply temperatures are also sufficienct for the
corresponding formulation for the minimum number of units.
3. Novel formulation of the minimum number of units sub-problem was
developed.
4. Developed a problem difficulty index for the minimum number of units
sub-problem to identify problems that will be computationally
expensive.
5. The importance of EMAT in the stream match generator sub-problem
and its role in obtaining a ranked sequence of HLDs identified. A new
EMAT loop added to the Sequential Framework as part of this work.
6. Procedure for setting up temperature intervals in the stream match
generator sub-problem was developed.
7. Automated starting value generators based on physical insight were
developed.
8. An Excel add-in “SeqHENS” was developed.

158. Contributions
Sequential Framework for heat exchanger network synthesis
1. Identified and rationalized the loops in the Sequential Framework.
2. Showed that stream supply temperatures are also sufficienct for the
corresponding formulation for the minimum number of units.
3. Novel formulation of the minimum number of units sub-problem was
developed.
4. Developed a problem difficulty index for the minimum number of units
sub-problem to identify problems that will be computationally
expensive.
5. The importance of EMAT in the stream match generator sub-problem
and its role in obtaining a ranked sequence of HLDs identified. A new
EMAT loop added to the Sequential Framework as part of this work.
6. Procedure for setting up temperature intervals in the stream match
generator sub-problem was developed.
7. Automated starting value generators based on physical insight were
developed.
8. An Excel add-in “SeqHENS” was developed.

159. Contributions
Sequential Framework for heat exchanger network synthesis
1. Identified and rationalized the loops in the Sequential Framework.
2. Showed that stream supply temperatures are also sufficienct for the
corresponding formulation for the minimum number of units.
3. Novel formulation of the minimum number of units sub-problem was
developed.
4. Developed a problem difficulty index for the minimum number of units
sub-problem to identify problems that will be computationally
expensive.
5. The importance of EMAT in the stream match generator sub-problem
and its role in obtaining a ranked sequence of HLDs identified. A new
EMAT loop added to the Sequential Framework as part of this work.
6. Procedure for setting up temperature intervals in the stream match
generator sub-problem was developed.
7. Automated starting value generators based on physical insight were
developed.
8. An Excel add-in “SeqHENS” was developed.

160. Contributions
Sequential Framework for heat exchanger network synthesis
1. Identified and rationalized the loops in the Sequential Framework.
2. Showed that stream supply temperatures are also sufficienct for the
corresponding formulation for the minimum number of units.
3. Novel formulation of the minimum number of units sub-problem was
developed.
4. Developed a problem difficulty index for the minimum number of units
sub-problem to identify problems that will be computationally
expensive.
5. The importance of EMAT in the stream match generator sub-problem
and its role in obtaining a ranked sequence of HLDs identified. A new
EMAT loop added to the Sequential Framework as part of this work.
6. Procedure for setting up temperature intervals in the stream match
generator sub-problem was developed.
7. Automated starting value generators based on physical insight were
developed.
8. An Excel add-in “SeqHENS” was developed.

161. Contributions
Sequential Framework for heat exchanger network synthesis
1. Identified and rationalized the loops in the Sequential Framework.
2. Showed that stream supply temperatures are also sufficienct for the
corresponding formulation for the minimum number of units.
3. Novel formulation of the minimum number of units sub-problem was
developed.
4. Developed a problem difficulty index for the minimum number of units
sub-problem to identify problems that will be computationally
expensive.
5. The importance of EMAT in the stream match generator sub-problem
and its role in obtaining a ranked sequence of HLDs identified. A new
EMAT loop added to the Sequential Framework as part of this work.
6. Procedure for setting up temperature intervals in the stream match
generator sub-problem was developed.
7. Automated starting value generators based on physical insight were
developed.
8. An Excel add-in “SeqHENS” was developed.

162. Contributions
Sequential Framework for heat exchanger network synthesis
1. Identified and rationalized the loops in the Sequential Framework.
2. Showed that stream supply temperatures are also sufficienct for the
corresponding formulation for the minimum number of units.
3. Novel formulation of the minimum number of units sub-problem was
developed.
4. Developed a problem difficulty index for the minimum number of units
sub-problem to identify problems that will be computationally
expensive.
5. The importance of EMAT in the stream match generator sub-problem
and its role in obtaining a ranked sequence of HLDs identified. A new
EMAT loop added to the Sequential Framework as part of this work.
6. Procedure for setting up temperature intervals in the stream match
generator sub-problem was developed.
7. Automated starting value generators based on physical insight were
developed.
8. An Excel add-in “SeqHENS” was developed.

163. Contributions
Sequential Framework for heat exchanger network synthesis
1. Identified and rationalized the loops in the Sequential Framework.
2. Showed that stream supply temperatures are also sufficienct for the
corresponding formulation for the minimum number of units.
3. Novel formulation of the minimum number of units sub-problem was
developed.
4. Developed a problem difficulty index for the minimum number of units
sub-problem to identify problems that will be computationally
expensive.
5. The importance of EMAT in the stream match generator sub-problem
and its role in obtaining a ranked sequence of HLDs identified. A new
EMAT loop added to the Sequential Framework as part of this work.
6. Procedure for setting up temperature intervals in the stream match
generator sub-problem was developed.
7. Automated starting value generators based on physical insight were
developed.
8. An Excel add-in “SeqHENS” was developed.

164. Contributions
Sequential Framework for heat exchanger network synthesis
1. Identified and rationalized the loops in the Sequential Framework.
2. Showed that stream supply temperatures are also sufficienct for the
corresponding formulation for the minimum number of units.
3. Novel formulation of the minimum number of units sub-problem was
developed.
4. Developed a problem difficulty index for the minimum number of units
sub-problem to identify problems that will be computationally
expensive.
5. The importance of EMAT in the stream match generator sub-problem
and its role in obtaining a ranked sequence of HLDs identified. A new
EMAT loop added to the Sequential Framework as part of this work.
6. Procedure for setting up temperature intervals in the stream match
generator sub-problem was developed.
7. Automated starting value generators based on physical insight were
developed.
8. An Excel add-in “SeqHENS” was developed.

165. Contributions
Sequential Framework for heat exchanger network synthesis
1. Identified and rationalized the loops in the Sequential Framework.
2. Showed that stream supply temperatures are also sufficienct for the
corresponding formulation for the minimum number of units.
3. Novel formulation of the minimum number of units sub-problem was
developed.
4. Developed a problem difficulty index for the minimum number of units
sub-problem to identify problems that will be computationally
expensive.
5. The importance of EMAT in the stream match generator sub-problem
and its role in obtaining a ranked sequence of HLDs identified. A new
EMAT loop added to the Sequential Framework as part of this work.
6. Procedure for setting up temperature intervals in the stream match
generator sub-problem was developed.
7. Automated starting value generators based on physical insight were
developed.
8. An Excel add-in “SeqHENS” was developed.

166. THANK YOU!
Source:xkcd