Process design and integration Timo Laukkanen

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

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

Evolution of process design experiments unit operation integration phenomena Timo Laukkanen

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

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

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

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

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

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

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

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

Separation System Utility System Timo Laukkanen

IEA BLUE MAP Separation System Timo Laukkanen

Heat Integration with Pinch Technology Targeting before Design

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)

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

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)

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

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

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

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

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

Composite Curves Tsupply(°C) Ttarget (°C) m cp (kW/K) Qtotal (kW) C1 50 110 1 60 H1 100 2 80 ∆Tmin = 15°C Qc,min = 40kW Qh,min = 20kW

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

∆Tmin Qh,min Qc,min

  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

∆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

  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

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

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

heat deficit pinch temperature pinch point minimum driving force heat surplus

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

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

Optimal ∆Tmin ∆Tmin optimal min total costs

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