1. Process design and integration
Timo Laukkanen

2. The main objectives of this course
To learn how to use tools that can be used to design heat recovery systems
To obtain a ”holistic” view for process design and especially heat recovery design
Timo Laukkanen

3. Process Engineering, Process Systems Engineering and Process Integration
Process engineering focuses on the design, operation, control, and optimization of chemical, physical, and biological processes.
Process systems engineering = systematic computer- based methods to process engineering.
Process Integration = a holistic approach to process design and optimization, integrated process design or process synthesis
Timo Laukkanen

4. Evolution of process design
experiments
unit operation
integration
phenomena
Timo Laukkanen

5. Evolution of process design
Synthesis is the creation of a process
Simulation predicts how the process would behave if built
Product
streams
Feed
streams
Product
streams
Feed
streams
Timo Laukkanen

6. Hierarchy of process design: The ”onion” diagram
Water and Effluent Treatment
Heating and Cooling System Utilities
Separation and Recycle System
Heat Recovery System
Reactor
Separation
System
Timo Laukkanen

7. Continuous vs. Batch process
BATCH processes
Small volumes
Flexible in changing product formulation
Flexible in production rate
Allows the use of multipurpose equipment
Best if regular cleaning necessary
Products from each batch can be identified
CONTINUOUS processes
Economical for large volumes
Timo Laukkanen

8. New design (greenfield) vs. Retrofit process design
Retrofit design
Old and new equipment can be used
The wearing of old equipment needs to considered
Greenfield design
Only new equipment can be used
Separation
System
Utility System
Timo Laukkanen

9. Irreducible structure vs
Irreducible structure vs. Reducible stucture (super structure) approach to process design
Irreducible structure (for example the pinch approach)
Follows the onion logic
Series of local decisions
Many designs need to be made due to sequential approach
No quarantee that best possible solution is found due to fixed designs in different levels
Designer in control of the design process
Superstructure approach (mathematical programming)
All design options included in a mathematical model
Huge problem that can be hard to solve
Needs simplifications in unit operations
If the best design is not one that is embeded in the superstructure , optimal solution is not found
Teoretically possible to find the global optimum
Utility System
Timo Laukkanen

10. Trade-offs in process design (multi-objective optimization)
Process topology
Energy
Capital
Operation
Raw materials
Separation
System
Utility System
Timo Laukkanen

11. Process Integration Methods
Systematic visual thermodynamic analysis
Targets before design
Pinch Analysis
“Second law”-thermodynamic analysis
Quantitative measure of process efficiency
Suitable multicomponent plant criteria of performance
Exergy Analysis
Constrained single- or multiobjective optimisation
Models for systematic design and analysis
Mathematical Programming
(Artificial intelligence)
Case-based reasoning, rule-based reasoning
Knowledge Based Expert Systems
Separation
System
Utility System
Timo Laukkanen

12. Process Integration Application
Initial trade-off between operating and investment costs
Heat recovery targets
Number of units, total heat exchanger surface area
External energy supply vs. recycling
Heat exchanger networks synthesis
Thermally driven
Distillation, evaporation and drying
Separation systems design
Boilers, turbine and heat pump integration
Utility system synthesis
Utility systems design
Flexibility, controllability, startup- and shutdown
Plant operability design
Separation
System
Timo Laukkanen

13. Separation
System
Utility System
Timo Laukkanen

14. IEA BLUE MAP
Separation
System
Timo Laukkanen

15. Heat Integration with Pinch Technology
Targeting before Design

16. Phases in Pinch based HEN-Synthesis
Data Extraction
Performance Targets
Energy, Area, Units, Total Annual Cost
Process Modifications
Design of Maximum Heat Recovery Network
Improvement (tuning)

17. Basic Equations for a Countercurrent Heat Exchanger
Specific enthalpy
Specific heat capacity
Mass flow
Th,in
A = Area
U = Overall Heat
Transfer Coefficient
Th,out
Tc,out
Tc,in x L
Specific heat transfer
coefficient
A=Q/(Utot*DLMTD)
1/U = 1/h1+1/h2

18. Data Extraction Process Streams Utility System/Streams Heat exchangers
Flowrates
Temperatures
Start (Supply) and End (Target)
Specific Heat Capacity
Incl. Latent Heat
Film Heat Transfer Coefficient (for U-estimate)
Utility System/Streams
Temperature(s)
Heat Content
Cost per unit
Heat exchangers
Cost function(s)

19. Data extraction: Small Example
Reactor with two reactant and one product stream
Necessary Data
m cp = 1 kW/K C1
40°C
300°C
Reactor
m cp = 3 kW/K H1
315°C
90°C
m cp = 2 kW/K C2
40°C
300°C

20. Alternative Networks H1 C1 Reactor C2 90°C -75 kW 115°C 40°C 210°C

21. Alternative Networks C1 Reactor C2 H1 228°C 315°C 40°C 300°C 142°C
180 kW
115°C
90°C H1
-75 kW

22. Alternative Networks H1 C1 Reactor C2 90°C 40°C 75°C 300°C 70 kW 315°C

23. Minimum Qc and Qh? Tsupply (°C) Ttarget (°C) m cp (kW/K) H1 400 120
Tsupply (°C)
Ttarget (°C)
m cp (kW/K) H1
400
120
1.0 H2
250
2.0 C1
160
1.5 C2
100
1.3

24. Composite Curves Tsupply(°C) Ttarget (°C) m cp (kW/K) Qtotal (kW) C150
110 1 60 H1
100 2 80
∆Tmin = 15°C
Qc,min = 40kW
Qh,min = 20kW

25. Tsupply(°C)
Ttarget (°C)
m cp (kW/K)
Qtotal (kW) H1 80 20
1.0 60 H2
120 40
0.5

27. ∆Tmin
Qh,min
Qc,min

28. Tsupply (°C)
Ttarget (°C)
m cp (kW/K) H1
400
120
1.0 H2
250
2.0 C1
160
1.5 C2
100
1.3
400
120
250
∆T (°C)
m cp (kW/K)
Q (kW) H1 H2 HC
400
150
1.0
250
130
2.0
3.0
390
120

29. ∆T (°C)
m cp (kW/K)
Q (kW) H1 H2 HC
400
150
1.0
250
130
2.0
3.0
390
120
Q (kW)
T (°C)
120
390
250
540
400

30. Tsupply (°C)
Ttarget (°C)
m cp (kW/K) H1
400
120
1.0 H2
250
2.0 C1
160
1.5 C2
100
1.3
m cp (kW/K)
∆T (°C) C1 C2 CC
Q (kW)
400
150
1.5
225
250 90
1.3
2.8
252
160 60 78
100
Q (kW)
T (°C)
100 78
160
330
250
555
400

31. Q (kW)
T (°C)
100 78
160
330
250
555
400

32. Qh,min = 220kW
∆Tmin=50°C
heat available
for recovery
Qc,min = 200kW

33. heat
deficit
pinch
temperature
pinch point
minimum driving force
heat
surplus

34. Trade-off between utility consumption and driving forces
Optimal ∆Tmin
Trade-off between utility consumption and driving forces

35. These are now correct ∆T Qc (kW) Qh (kW) Qutil (kW) Th,in Th,out Tc,in
Tsupply(°C)
Ttarget (°C)
m cp (kW/K)
Qtotal (kW) H1
110 50
1.0 60 C2
100
2.0 80 ∆T
Qc (kW)
Qh (kW)
Qutil (kW)
Th,in
Th,out
Tc,in
Tc,out
∆Tlm Q A 10 30 40
110 60 85 - 50 20 70 80 18
3.3 75 27
1.9
100 90 35
1.2

36. Optimal ∆Tmin
∆Tmin optimal
min total costs

37. Summary Composite Curves
Visualising the system
Key information about the system
minimum utility consumption, hot and cold (given ∆Tmin)
pinch point
decomposition (areas of heat surplus and deficit)
Temperature of min. driving forces for heat exchange