Energy efficiency in process plants with an emphasis on hens

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Energy efficiency in process plants with an emphasis on hens

  1. 1. Norwegian University of Science and Technology Department of Energy and Process EngineeringEnergy 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. 2. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  3. 3. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  4. 4. Energy IntegrationEnergy integration is defined as systematic methods for generatingintegrated energy recovery systems.
  5. 5. Energy IntegrationEnergy integration is defined as systematic methods for generatingintegrated energy recovery systems.
  6. 6. Energy IntegrationEnergy integration is defined as systematic methods for generatingintegrated energy recovery systems.
  7. 7. Energy Integration
  8. 8. Energy Integration
  9. 9. Energy Integration
  10. 10. Energy Integration
  11. 11. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  12. 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. 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. 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. 15. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  16. 16. MotivationNeed a tool for energy integration of energy intensive plants likemethanol 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. 17. MotivationNeed a tool for energy integration of energy intensive plants likemethanol 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. 18. MotivationNeed a tool for energy integration of energy intensive plants likemethanol 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. 19. MotivationNeed a tool for energy integration of energy intensive plants likemethanol 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. 20. MotivationNeed a tool for energy integration of energy intensive plants likemethanol 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. 21. MotivationNeed a tool for energy integration of energy intensive plants likemethanol 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. 22. MotivationNeed a tool for energy integration of energy intensive plants likemethanol 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. 23. ObjectiveDevelop a new methodology for enery intergration of process involvingheat 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. 24. Search for a quality parameter
  25. 25. Search for a quality parameter
  26. 26. Search for a quality parameter
  27. 27. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  28. 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. 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. 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. 31. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  32. 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 qualityEnergy 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 factProcess 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. 33. Energy integration of a methanol plantELCC 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. 34. Energy integration of a methanol plantELCC - 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. 35. Energy integration of a methanol plantIntegration 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. 36. Energy integration of a methanol plantIntegration 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. 37. Energy integration of a methanol plantIntegration 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. 38. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  39. 39. Conclusions and further workConclusions 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. 40. Conclusions and further workConclusions 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. 41. Conclusions and further workConclusions 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. 42. Conclusions and further workFurther 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. 43. Conclusions and further workFurther 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. 44. Conclusions and further workFurther 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. 45. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  46. 46. Heat Exchanger Network SynthesisFor a given set of hot and cold process streams as well as externalutilities, design a heat exchanger network that minimizes TotalAnnualized Cost (TAC).TAC = Capital Cost + Energy Cost Sequential Framework Engine
  47. 47. Heat Exchanger Network SynthesisSolution methods1. Evolutionary methods such as Pinch Design Method2. Sequential synthesis methods3. Simultaneous synthesis methods4. Stochastic optimization methods
  48. 48. Heat Exchanger Network SynthesisSolution methods1. Evolutionary methods such as Pinch Design Method2. Sequential synthesis methods3. Simultaneous synthesis methods4. Stochastic optimization methods
  49. 49. Heat Exchanger Network SynthesisSolution methods1. Evolutionary methods such as Pinch Design Method2. Sequential synthesis methods3. Simultaneous synthesis methods4. Stochastic optimization methods
  50. 50. Heat Exchanger Network SynthesisSolution methods1. Evolutionary methods such as Pinch Design Method2. Sequential synthesis methods3. Simultaneous synthesis methods4. Stochastic optimization methods
  51. 51. Heat Exchanger Network SynthesisTimeline
  52. 52. Heat Exchanger Network SynthesisTimeline
  53. 53. Heat Exchanger Network SynthesisTimeline
  54. 54. Heat Exchanger Network SynthesisTimeline
  55. 55. Heat Exchanger Network SynthesisTimeline
  56. 56. Heat Exchanger Network SynthesisTimeline
  57. 57. Heat Exchanger Network SynthesisTimeline
  58. 58. Heat Exchanger Network SynthesisTimeline
  59. 59. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  60. 60. HENS in the 21st centuryReview225 references published from 2000-2008 216 journal papers 48 jounals 43 countries 4 conference proceedings 10 Ph.D. theses 4 textbooks
  61. 61. HENS in the 21st centuryReview225 references published from 2000-2008 216 journal papers 48 jounals 43 countries 4 conference proceedings 10 Ph.D. theses 4 textbooks
  62. 62. HENS in the 21st centuryReview225 references published from 2000-2008 216 journal papers 48 jounals 43 countries 4 conference proceedings 10 Ph.D. theses 4 textbooks
  63. 63. HENS in the 21st centuryReview225 references published from 2000-2008 216 journal papers 48 jounals 43 countries 4 conference proceedings 10 Ph.D. theses 4 textbooks
  64. 64. HENS in the 21st centuryReview225 references published from 2000-2008 216 journal papers 48 jounals 43 countries 4 conference proceedings 10 Ph.D. theses 4 textbooks
  65. 65. HENS in the 21st centuryReview225 references published from 2000-2008 216 journal papers 48 jounals 43 countries 4 conference proceedings 10 Ph.D. theses 4 textbooks
  66. 66. HENS in the 21st centuryReview 45 40 35 30 25 20 15 10 5 0 2000 2001 2002 2003 2004 2005 2006 2007 2008
  67. 67. HENS in the 21st centuryReview
  68. 68. HENS in the 21st centuryReview 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. 69. HENS in the 21st centuryReview 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. 70. HENS in the 21st centuryReview 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. 71. HENS in the 21st centuryReview 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. 72. HENS in the 21st centuryReview 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. 73. HENS in the 21st centuryReview 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. 74. HENS in the 21st centuryReview 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. 75. HENS in the 21st centuryReview 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. 76. HENS in the 21st centuryReviewConclusions 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. 77. HENS in the 21st centuryReviewConclusions 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. 78. HENS in the 21st centuryReviewConclusions 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. 79. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  80. 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. 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. 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. 83. Motivation for the Sequential FrameworkHENS 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. 84. Motivation for the Sequential FrameworkHENS 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. 85. Motivation for the Sequential FrameworkHENS 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. 86. Motivation for the Sequential FrameworkHENS 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. 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. 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. 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. 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. 91. Sequential FrameworkThe engine Tool: SeqHENS 3 way trade-off Compromise between Pinch Design and MINLP methods
  92. 92. Sequential FrameworkThe engine Tool: SeqHENS 3 way trade-off Compromise between Pinch Design and MINLP methods
  93. 93. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  94. 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 ($) = 8,600 + 670A0.83 (A is in m2 )
  95. 95. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 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. 96. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 1 8 2.5 A 199,914 2 8 5 A 199,914 3 8 7.5 - No Soln 4 9 2.5 A 147,861 5 9 2.5 B 151,477 6 9 5 A 147,867 7 9 5 B 151,508 8 9 7.5 A 149,025 9 9 7.5 B 149,224 10 10 2.5 A 164,381 11 10 5 A 167,111 12 10 7.5 A 164,764
  97. 97. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 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. 98. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 1 8 2.5 A 199,914 2 8 5 A 199,914 3 8 7.5 - No Soln 4 9 2.5 A 147,861 5 9 2.5 B 151,477 6 9 5 A 147,867 7 9 5 B 151,508 8 9 7.5 A 149,025 9 9 7.5 B 149,224 10 10 2.5 A 164,381 11 10 5 A 167,111 12 10 7.5 A 164,764
  99. 99. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 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. 100. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 1 8 2.5 A 199,914 2 8 5 A 199,914 3 8 7.5 - No Soln 4 9 2.5 A 147,861 5 9 2.5 B 151,477 6 9 5 A 147,867 7 9 5 B 151,508 8 9 7.5 A 149,025 9 9 7.5 B 149,224 10 10 2.5 A 164,381 11 10 5 A 167,111 12 10 7.5 A 164,764
  101. 101. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 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. 102. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 1 8 2.5 A 199,914 2 8 5 A 199,914 3 8 7.5 - No Soln 4 9 2.5 A 147,861 5 9 2.5 B 151,477 6 9 5 A 147,867 7 9 5 B 151,508 8 9 7.5 A 149,025 9 9 7.5 B 149,224 10 10 2.5 A 164,381 11 10 5 A 167,111 12 10 7.5 A 164,764
  103. 103. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 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. 104. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 1 8 2.5 A 199,914 2 8 5 A 199,914 3 8 7.5 - No Soln 4 9 2.5 A 147,861 5 9 2.5 B 151,477 6 9 5 A 147,867 7 9 5 B 151,508 8 9 7.5 A 149,025 9 9 7.5 B 149,224 10 10 2.5 A 164,381 11 10 5 A 167,111 12 10 7.5 A 164,764
  105. 105. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 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. 106. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 1 8 2.5 A 199,914 2 8 5 A 199,914 3 8 7.5 - No Soln 4 9 2.5 A 147,861 5 9 2.5 B 151,477 6 9 5 A 147,867 7 9 5 B 151,508 8 9 7.5 A 149,025 9 9 7.5 B 149,224 10 10 2.5 A 164,381 11 10 5 A 167,111 12 10 7.5 A 164,764
  107. 107. Example 1 - 7TP1Looping to the solutionHRAT fixed at 20K (Qh,min = 244.1 kW & Qc,min = 172.6 kW)Umin = 8 units Soln. No U EMAT (K) HLD TAC ($) 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. 108. Example 1 - 7TP1Best solution
  109. 109. Example 1 - 7TP1Comparison 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. 110. EMAT in the Sequential FrameworkChosing 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 dependsexplicitly on EMATEMAT is an optimizing variable in this formulationEMAT comes into play when there is an extra degree of freedom in thesystem - number of units greater than Umin
  111. 111. EMAT in the Sequential FrameworkChosing 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 dependsexplicitly on EMATEMAT is an optimizing variable in this formulationEMAT comes into play when there is an extra degree of freedom in thesystem - number of units greater than Umin
  112. 112. EMAT in the Sequential FrameworkChosing 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 dependsexplicitly on EMATEMAT is an optimizing variable in this formulationEMAT comes into play when there is an extra degree of freedom in thesystem - number of units greater than Umin
  113. 113. EMAT in the Sequential FrameworkChosing 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 dependsexplicitly on EMATEMAT is an optimizing variable in this formulationEMAT comes into play when there is an extra degree of freedom in thesystem - number of units greater than Umin
  114. 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. 115. Example 2 - 15TP1Looping to the solutionHRAT 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. 116. Example 2 - 15TP1Best solution
  117. 117. Example 2 - 15TP1Comparison 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. 118. Example 2 - 15TP1Comparison 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. 119. Example 2 - 15TP1Comparison 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. 120. Example 2 - 15TP1Comparison 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. 121. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  122. 122. ChallengesCombinatorial 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 timeLocal 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 problemSequence of MILP and NLP problems considerably easier to solve than MINLPformulations
  123. 123. Challenges - Minimum Units MILPMitigation 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. 124. Challenges - Minimum Units MILPMitigation 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. 125. Challenges - Minimum Units MILPMitigation 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. 126. Challenges - Minimum Units MILPMitigation 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. 127. Challenges - Minimum Units MILPMitigation 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. 128. Challenges - Minimum Units MILPMitigation 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. 129. Challenges - Minimum Units MILPMitigation 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. 130. Challenges - Minimum Units MILPMitigation 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. 131. Challenges - Stream Match Generator MILPMitigation 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. 132. Challenges - Stream Match Generator MILPMitigation 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. 133. Challenges - Stream Match Generator MILPMitigation 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. 134. Challenges - Stream Match Generator MILPMitigation 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. 135. Challenges - Stream Match Generator MILPMitigation 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. 136. Challenges - Stream Match Generator MILPMitigation 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. 137. Challenges - Stream Match Generator MILPMitigation 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. 138. Challenges - Stream Match Generator MILPMitigation 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. 139. Challenges - Stream Match Generator MILPMitigation 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. 140. Challenges - Stream Match Generator MILPMitigation 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. 141. Challenges - Network generation and optimization NLPMitigation measuresDeveloped 4 starting value generators to get “good” initial networks1. Serial/Parallel heuristic2. H/H heuristic3. Stream match generator based heuristic4. Combinatorial heuristic based on insightCombinatorial heuristic performed best by ensuring that the NLPsolved for all test cases.
  142. 142. Challenges - Network generation and optimization NLPMitigation measuresDeveloped 4 starting value generators to get “good” initial networks1. Serial/Parallel heuristic2. H/H heuristic3. Stream match generator based heuristic4. Combinatorial heuristic based on insightCombinatorial heuristic performed best by ensuring that the NLPsolved for all test cases.
  143. 143. Challenges - Network generation and optimization NLPMitigation measuresDeveloped 4 starting value generators to get “good” initial networks1. Serial/Parallel heuristic2. H/H heuristic3. Stream match generator based heuristic4. Combinatorial heuristic based on insightCombinatorial heuristic performed best by ensuring that the NLPsolved for all test cases.
  144. 144. Challenges - Network generation and optimization NLPMitigation measuresDeveloped 4 starting value generators to get “good” initial networks1. Serial/Parallel heuristic2. H/H heuristic3. Stream match generator based heuristic4. Combinatorial heuristic based on insightCombinatorial heuristic performed best by ensuring that the NLPsolved for all test cases.
  145. 145. Challenges - Network generation and optimization NLPMitigation measuresDeveloped 4 starting value generators to get “good” initial networks1. Serial/Parallel heuristic2. H/H heuristic3. Stream match generator based heuristic4. Combinatorial heuristic based on insightCombinatorial heuristic performed best by ensuring that the NLPsolved for all test cases.
  146. 146. OutlineIntroduction Process Synthesis and Energy Integration ObjectivesEnergy Level Composite Curves Background Energy Level and Energy Level Composite Curves Case study Conclusions and further workHeat Exchanger Network Synthesis Introduction HENS in the 21st centurySequential Framework Introduction Examples Challenges Further workContributions
  147. 147. Further WorkMinimum 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. 148. Further WorkMinimum 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. 149. Further WorkMinimum 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. 150. Further WorkMinimum 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. 151. Further WorkMinimum 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. 152. Further WorkMinimum 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 166. THANK YOU! Source:xkcd

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