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Petar Sabev Varbanov, Jiří Jaromír Klemeš, Ferenc Friedler
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  1. EC FP7 project “Intensified Heat Transfer Technologies for Enhanced Heat Recovery” – INTHEATGrant Agreement No.262205Project 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

  2. Overview of the tasksinvolving UNIPAN for the period

  3. 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).

  4. Task 4.1Development of a streamlined and computationally efficient methodology for design of HENs

  5. Task 4.1 Outline • Introduction • HEN design for flexibility and multiperiod operation • Need for a rigorous synthesis tool • P-graph for HEN Synthesis • Extensions being developed • Conclusions

  6. Introduction

  7. Main Approaches Classic approach to process synthesis • Analyse a base case scenario • Evaluate the expected process variations • Prepare a representative base case for HEN synthesis • Synthesise a heat exchanger network 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

  8. Classical HEN Synthesis Pinch design method Specify the heat recovery problem • 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) Pinch Analysis Obtain MER topology Evolve the network 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.

  9. 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

  10. 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.

  11. 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

  12. HEN Design for Flexibilityand Multiperiod Operation

  13. Main Definitions

  14. Operability A heat exchanger network is termed operable if it is simultaneously: • 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.

  15. 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

  16. Flexibility Domain • This is the set of operating points over which the flexibility is specified • Two main ways for representing the Flexibility Domain: • Ranges of variation = nominal conditions + variation intervals. Conceptual understanding • Multiperiod operation = a list of discrete operating points with periods of activity. More convenient for applying algorithmic synthesis

  17. Flexible HEN Synthesis – Ranges of Variation Features • Using a specification of uncertainty ranges • Reflects more realistically the uncertain nature of the process variations • Possible to impose the maximum energy requirement 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

  18. Multiperiod Synthesis Approaches Features • 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 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

  19. Synthesis with uncertainty ranges

  20. Uncertainty Ranges Permanent and transient stream components Cerda et al. (1990)

  21. Uncertainty Ranges – Algorithm • Thermodynamic targets for heat recovery • Identify the pinch locations • Heat recovery targets • Decompose the temperature range of the set of process streams into sub-networks (or blocks) • 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. • The resulting heat exchange matches are further assigned to actual exchangers and sized

  22. Synthesis for multiperiod operation using Mathematical Programming

  23. Multiperiod HEN Synthesis Algorithms Aaltola (2002) Floudas & Grossmann (1987) Specify operating points Specification of the operating points and superstructure • 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 Utility cost targeting Superstructure reduction MINLP Feasibility testing Minimise utility costs under limited HE areas LP / NLP NLP network generation Flexibility testing End Modifications End

  24. 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

  25. P-graph for HEN Synthesis

  26. 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

  27. P-graph Example Streams / Materials BG: Biogas BM: Biomass BR: Biomass residues FRT: Fertiliser SG: Syngas PR: Particulate matter Q40: Steam at 40 bar Q5: Steam at 5 bar RSG: Raw syngas W: Electrical power Operations BGD: Biogas digester BMG: Biomass gasifier SGF: Syngas filter FCCC: Fuel Cell Combined Cycle BLR_BG: Biogas boiler LD_40_5: Letdown station

  28. 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

  29. 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

  30. Problem Formulation Reduction part Consistent sets O & M Composition part Maximal Structure P-graph algorithms:Maximal Structure Generation (MSG) • Problem Formulation • set of raw materials • set of products • set of candidate operating units • Maximal Structure • Union of all combinatorially feasible structures • Rigorous super-structure Legend: O: set of operating units M: set of materials

  31. P-graph algorithms:Solution Structures Generation (SSG) Start from products • Solution Structure • A combinatorially feasible network of materials and operating units Add units producing New decision mappingfor every decision branch Invoke SSG (Recursion) • Decision Mapping • A mathematical representation of a process network – either incomplete, or a solution structure All Solution Structures

  32. 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-Bound Further acceleration of the synthesis procedure

  33. PNS ParadigmsExample from Reactor Networks

  34. Extensions being developed

  35. Interfaces of the P-graph algorithms Generate candidate HE P-graph framework(MSG, SSG, ABB) Bounding: Variation of Supertargeting Branching: When to create sub-problems

  36. 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

  37. 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

  38. 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

  39. Task 6.2: “Dissemination events” : “Intensified heat exchangers – Novel developments (Information day for major stakeholders) (organisers: UNIPAN, PIL, UNIMAN)”

  40. 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)

  41. 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

  42. 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

  43. 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

  44. 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

  45. Thank you!