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CPI2
EC FP7 project “Intensified Heat Transfer Technologies for Enhanced Heat Recovery” – INTHEAT
Grant Agreement No.262205
Project Meeting July 8, 2011
Petar Sabev Varbanov, Jiří Jaromír Klemeš, Ferenc Friedler
Centre for Process Integration and Intensification – CPI2, Research Institute of Chemical and Process Engineering, Faculty of Information
Technology, University of Pannonia, Veszprém, Hungary
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Overview of the tasksinvolving UNIPAN for the period
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UNIPAN Tasks WP 4 “Design, retrofit and control of intensified heat
recovery networks”
Task 4.1: “Development of a streamlined and computationally efficient methodology for design of HENs“, started month 1 (December 2010),
Deliverable D4.1 “Report on design methodology for new heat exchanger networks using P-graph and the ABB (Accelerated Branch-and-Bound) optimisation algorithm” due in month 9 (August 2011)
WP 6 “Technology transfer”
Task 6.2: “Dissemination events” : “Intensified heat exchangers – Novel developments (Information day for major stakeholders) (organisers: UNIPAN, PIL, UNIMAN)”
Deliverable D6.3 “Four dissemination events” – event 1, due in month 8 (July 2011).
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Task 4.1Development of a streamlined and
computationally efficient methodology for design of HENs
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• Introduction
• HEN design for flexibility and multiperiod operation
• Need for a rigorous synthesis tool
• P-graph for HEN Synthesis
• Extensions being developed
• Conclusions
Task 4.1 Outline
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Introduction
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Main Approaches
Analyse a base case scenario
Evaluate the expected process variations
Prepare a representative base case for HEN synthesis
Synthesise a heat exchanger network
Classic approach to process synthesis
The main approaches use different views of the system Insight-based : exploit thermodynamic insights such as
the heat recovery pinch and its associated targets
Superstructure-based: a reducible network including all possible options and then optimise and reduce it
Hybrid: combine the thermodynamic insights and the use of superstructures
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Classical HEN Synthesis
Pinch design method
Specify the heat recovery problem
Pinch Analysis
Obtain MER topology
Evolve the network
• Linnhoff and Hindmarsh (1983)
• Follow-ups and elaborations
Capital and total cost targets (Linnhoff and Ahmad, 1990)
Block Decomposition method (Zhu 1997)
Total Sites (Klemeš et al., 1997)
Total Sites integrating renewables (Perry et al., 2008)
• Mathematical Programming
• E.g. Yee and Grossmann (1990)
Yee, T. F., Grossmann I. E., 1990, Simultaneous optimization models for heat integration—II. Heat exchanger network synthesis, Computers & Chemical Engineering 14(10):1165-1184.
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Comparison of Approaches
Pinch design method A suite of techniques for HEN synthesis and process changes
Based on the pinch division and pinch design rules
Generates MER networks and evolves them
The networks may be inflexible
Superstructure-based approaches Build, optimise and reduce a superstructure
MILP and MINLP superstructure formulations are possible
Can treat multiple heat exchanger types non-isothermal mixing
Hybrid approaches Attempt to combine the insights of the Pinch Analysis with the strengths
of the superstructure construction and reduction
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Variable Factors
Ambient conditions
Temperature, humidity, etc.
Production rates
Feedstock variations, market conditions/demand
Catalyst activity
Gradual and steady, catalysts not replaced immediately
Fouling in heat exchangers
Connected batch processes
Inherent variations – e.g. batch distillation
Frequent stops due to batch cycles
Upsets in upstream processes
Gradual variations change steady states.
Transient changes cause transitions between states.
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Recognition of Variations
Introduction of the multi-period optimisation: Floudas and Grossmann (1986), using an MILP model
Follow-ups:
Aaltola (2002) MINLP Verheyen and Zhang (2006) MINLP Ahmad et al. (2008) used Simulated Annealing
MP for multiperiod HEN synthesis faces major challenges and limitations mainly with solution space and efficiency
P-graph is capable of efficiently addressing these limitations
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HEN Design for Flexibilityand Multiperiod Operation
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Main Definitions
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Operability
Flexible – the ability to operate at a variety of different steady-state points.
Controllable – the ability to manipulate the system, both in terms of feasible dynamic response and in terms of achieving the control system objectives.
Reliable – includes having excess capacities in certain system components to ensure ability to deal with breakdowns and device failures.
A heat exchanger network is termed operable if it is simultaneously:
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Current Focus: Flexibility
Many definitions in the research literature Means ability to retain specific properties and qualities
under varying conditions Applied to a heat exchanger network, the flexibility
can be defined as retaining the following properties for a given set of operating points:
The network satisfies the heating and cooling demands imposed by the process streams
Remains steady-state feasible
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Flexibility Domain
This is the set of operating points over which the flexibility is specified
Two main ways for representing the Flexibility Domain:
i. Ranges of variation = nominal conditions + variation intervals. Conceptual understanding
ii. Multiperiod operation = a list of discrete operating points with periods of activity. More convenient for applying algorithmic synthesis
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Flexible HEN Synthesis – Ranges of Variation
Using a specification of uncertainty ranges Reflects more realistically the uncertain nature of the
process variations Possible to impose the maximum energy requirement
Features
Problems
Impossible to assign the appropriate energy and capital costs to any of the operating points in the uncertainty envelope
Proper estimation of the cost trade-offs cannot be implemented
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Multiperiod Synthesis Approaches
Translates the variability of process parameters into a list of discrete operating points
Form operating cycle of usually one year
The periods can be assigned specific duration weights, ambient conditions and utility costs
Synthesise a minimum total cost network
Features
Problems
Processes almost never operate at fixed points, or it is difficult to predict them precisely
The computational difficulty imposed by the optimisation of the resulting superstructures, since the formulations are generally non-linear (MINLP)
Some of the multiperiod methods for HEN synthesis allow only isothermal mixing
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Synthesis with uncertainty ranges
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Uncertainty Ranges
Permanent and transient stream componentsCerda et al. (1990)
Cold streamT (ºC)
W(kW/ºC)
Fixed target temperature
Range of variation of the inlet temperature
Hot streamT (ºC)
W(kW/ºC)
0 00 WMIN
WMAX
WMIN
WMAX
Pe
rma
nent
com
po
nent
Pe
rma
nent
com
po
nentTransient
components
Fixed target temperature
Range of variation of the inlet temperature
Transient components
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Uncertainty Ranges – Algorithm
1. Thermodynamic targets for heat recovery
Identify the pinch locations Heat recovery targets
2. Decompose the temperature range of the set of process streams into sub-networks (or blocks)
3. Considering each sub-network as an energetically balanced system, obtain a network featuring a minimum number of heat exchanger matches. This stage usually uses the superstructure approach, defining all significant options for implementing the network and further optimising and reducing this superstructure.
4. The resulting heat exchange matches are further assigned to actual exchangers and sized
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Synthesis for multiperiod operation using Mathematical Programming
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Multiperiod HEN Synthesis Algorithms
Floudas & Grossmann (1987) Aaltola (2002)
Specifyoperating points
Utility cost targeting
Feasibility testing
NLP network generation
Flexibility testing
End
Modifications
Specification of the operating points and superstructure
Superstructure reduction MINLP
Minimise utility costs under limited HE areas
LP / NLP
End
Variability translated to a set of discrete operating points and periods
Periods duration weights
Individual ambient conditions and utility costs
Synthesise minimum TAC HEN feasible for all periods
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Need for a rigorous synthesis tool
Complexity caused by combining continuous and combinatorial aspects
Combinatorial complexity increases exponentially with the number of streams and periods
MP – moderate success in reducing superstructures Very few applications of constructing the superstructures
using MP are known Solvers examine topologically clearly infeasible
combinations of integer variable values Rather difficult to build the necessary problem
superstructures without rigorous combinatorial tools
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P-graph for HEN Synthesis
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HE representation with P-graph
Grid-diagram representation P-graph
P-graph is a bi-partite graph. It features 2 vertex types: materials (streams) and operating units
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P-graph Example
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BM
BMG
RSGBR
25.9 MW
2.1 t/h
SGF
SGPR
8·10-3 t/h
BG
Q40
FCCC_60(MCFC+ST)
W 10.0 MW
BGD
FRT
2.0 t/h
Q5
2.2 MW
LD_40_5
BLR_BG
15.0 MW
CO2
0.6 t/h0.7 t/h
16.8MW
16.7 MW 0.17
t/h
26.0 MW
12.8 MW
15.1MW
Streams / MaterialsBG: BiogasBM: BiomassBR: Biomass residuesFRT: FertiliserSG: SyngasPR: Particulate matterQ40: Steam at 40 barQ5: Steam at 5 barRSG: Raw syngasW: Electrical power
OperationsBGD: Biogas digesterBMG: Biomass gasifierSGF: Syngas filterFCCC: Fuel Cell Combined CycleBLR_BG: Biogas boilerLD_40_5: Letdown station
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P-graph Combinatorial instruments
Axioms ensuring combinatorially feasible structures
Maximal Structure Generation (MSG) algorithm – builds the union of all combinatorially feasible network structures
Solution Structures Generation (SSG) – generates all combinatorially feasible network structures from the maximal one
ABB: Accelerated Branch-and-Bound algorithm. Combines the “branch-and-bound” search strategy with the SSG logic
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P-graph foundation: axioms
Ensuring a combinatorially feasible structure:
(S1) Every product is included in the structure
(S2) A raw material can’t be an output of any operating unit in the structure
(S3) Every operating unit is defined in the synthesis problem
(S4) At least one path from any operating unit leading to a product
(S5) Every stream belonging to the structure must consumed or produced by at least one operating unit from the structure
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P-graph algorithms:Maximal Structure Generation (MSG)
Problem Formulation
set of raw materials set of products set of candidate operating
unitsReduction part
Composition part
Problem Formulation
Consistent sets O & M
Maximal Structure
Maximal Structure
Union of all combinatorially feasible structures
Rigorous super-structure
Legend:
O: set of operating units
M: set of materials
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P-graph algorithms:Solution Structures Generation (SSG)
Add units producing
New decision mappingfor every decision branch
Invoke SSG(Recursion)
Start from products
All Solution Structures
Solution Structure
A combinatorially feasible network of materials and operating units
Decision Mapping
A mathematical representation of a process network – either incomplete, or a solution structure
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ABB Algorithm – Even Faster Search
• Employs the “branch-and-bound” strategy• Combines this with the P-graph logic (SSG algorithm)• Ensures combinatorial feasibility
• Non-optimal decisions are eliminated• It is possible to select a set of solution structures which are
optimal or near-optimal
ABB: Accelerated Branch-and-BoundFurther acceleration of the synthesis procedure
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PNS ParadigmsExample from Reactor Networks
Conventional MP(MILP, MINLP)
P-graph(MSG, SSG, ABB)
Network Model Formulation
Mostly MANUAL ALGORITHMIC
Automation allowing user interaction
Complexity
(Solution Speed)
Example: separation sequence synthesis
34 Billion
possible combinations
3,465 combinatorially feasible structures
106 ratio(6 orders of magnitude)
Interpretation of results
Flowsheets
(only)
Flowsheets and P-graphs
Easier to spot structural patterns
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Extensions being developed
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Interfaces of the P-graph algorithms
P-graph framework(MSG, SSG, ABB)
Generate candidate
HE
Branching:When to create sub-problems
Bounding:Variation of
Supertargeting
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Multiperiod formulation specifics
Establish the design horizon Define the operation periods (cumulative
durations) and the linked steady-state points (average stream flowrates, temperatures and heat capacity flowrates)
Binding heat exchange matches from different periods to a particular unique heat exchanger unit
Streams temperature-based partitioning and splitting – which first
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Preliminary Results
The approach is being tested on case studies First results look encouraging Solution times are fast For a toluene hydrodealkylation example 105
cold sub-streams and 333 hot sub-streams are generated from 18 temperature intervals
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Conclusions for Task 4.1
Most currently available methods for HEN design are based on mathematical programming
Few are using evolutional and random-search algorithms
The superstructure-based methods are not practical for generation of the superstructures
The P-graph framework offers algorithmic construction of the superstructures and combinatorially efficient reduction of the search space presented to the optimisation solvers
Currently several case studies are in progress
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Task 6.2: “Dissemination events” : “Intensified heat exchangers – Novel developments (Information day for major stakeholders)
(organisers: UNIPAN, PIL, UNIMAN)”
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Dissemination event: PRES’11
PRES’11 has been organised by UNIPAN together with The Italian Association of Chemical Engineering AIDIC. 8-11 May 2011 in Florence, Italy
There was a special session added to the programme, dedicated to intensifying heat transfer. Several works have been presented and discussion:
The Generalized Correlation for Friction Factor in Cris-Cross Flow Channels of Plate Heat Exchangers by Arsenyeva et al. (http://www.aidic.it/cet/11/25/067.pdf)
The Heat and Momentum Transfers Relation in Channels of Plate Heat Exchangers, by Kapustenko et al. (http://www.aidic.it/cet/11/25/060.pdf)
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Task 4.1, Deliverable D4.1
Deliverable D4.1 “Report on design methodology for new heat exchanger networks using P-graph and the ABB (Accelerated Branch-and-Bound) optimisation algorithm”
Due in month 9 (August 2011) The report will be delivered by UNIMAN with the main
input from UNIPAN and help from SORDU and OIKOS
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Dissemination event: PRES’11
There have been also more presentations on intensified heat transfer
Improving Energy Recovery in Heat Exchanger Network with Intensified Tube-side Heat Transfer, by Pan et al., UNIMAN (http://www.aidic.it/cet/11/25/063.pdf)
Heat Exchanger Network Retrofit through Heat Transfer Enhancement, by Wang et al., UNIMAN (http://www.aidic.it/cet/11/25/099.pdf)
Deliverable D6.3 “Four dissemination events” – event 1, due in month 8 (July 2011) will be delivered by UNIPAN by the end of July 2011 with the assistance of UNIMAN, and PIL
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Work for the next period (until month 12) WP 2 “Combined tube-side and shell-side heat exchanger
enhancement”, started in month 1 (December 2010)
Task 2.2. Heat transfer enhancement for the shell-side of heat, deliverable D2.2. “Report on tube side and shell side enhancement research” due in month 9 (August 2011).
UNIPAN is exploring process integration options based on the research of UNIMAN, UNIBATH, EMBAFFLE, led by CALGAVIN
WP 4 “Design, retrofit and control of intensified heat recovery networks”
Deliverable D4.1 “Report on design methodology for new heat exchanger networks using P-graph and the ABB (Accelerated Branch-and-Bound) optimisation algorithm” , Due in month 9 (August 2011). The report will be delivered by UNIMAN with the main input from UNIPAN and help from SORDU and OIKOS
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Work for the next period (until month 12) WP 4 “Design, retrofit and control of intensified heat
recovery networks”
Task 4.2. A systematic retrofit procedure will be developed to account for heat exchanger networks prone to fouling deposition. Deliverable D4.2 “Report on retrofit procedure for heat exchanger networks prone to fouling deposition” , Due in month 14 (January 2012). UNIPAN collaborates with UNIMAN, UPB .
WP 6 “Technology transfer”
Task 6.2: “Dissemination events” : “Intensified heat exchangers – Enhanced heat transfer (Workshop/session at a recognised international conference) (organisers: UNIPAN, CALGAVIN, EMBAFFLE, SODRU)
Suggesting to be organised at CAPE Forum 2012 organised by UNIPAN, 26-28 March 2012
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Thank you!