CAPE for Waste-to-Energy

Transcription

1 CAPE FORUM 2012 University of Pannonia Veszprém, Hungary CAPE for Waste-to-Energy Petr Stehlík Brno University of Technology Institute of Process and Environmental Engineering Czech Republic

2 Pre-Introduction

3 Pre-Introduction

4 Contents Application framework Simulation Plant level Model Identification Equipment level Modeling Structural mechanics (FEM) Fluid dynamics (CFD) Design optimization Plant design Equipment design Industrial applications and Conclusions

5 Application framework Process industries WTE Power industry Our focus

6 Research Industrial practice Successful approach combines industrial practice and research mutual benefit University Engineering company End user 3 End user 2 End user 1 Manufacturer 3 Manufacturer 2 Manufacturer 1

7 Thermal processing of waste Plant level: Process design, modelling, optimization Equipment level: Detailed design, modelling, optimization

8 Contents Application framework Simulation Plant level Model Identification Equipment level Modeling Structural mechanics (FEM) Fluid dynamics (CFD) Design optimization Plant design Equipment design Conclusions

9 MSW incinerator Termizo, a.s. Liberec MSW incinerator with annual waste processing capacity of 100 kt

10 Termizo simplified technological scheme (process flow-sheet)

11 Simulation software W2E Termizo heat recovery system model

12 Electricity production [kwh/t] Net efficiency [%] Expected energy production Net efficiency of power production in condensation regime does not exceed 20 %. Further efficiency increase is problematic and requires application of expensive materials and measures Consumed on-site (air-preheating, deaeration, etc.) Steam from HRSG (40 bar, 400 C) Steam for heating (11.7 bar) Consumed on-site G Exported CONDENSER Waste heat Exported /1.1 MPa 4/0.3 MPa 6/0.3 MPa /0.3 MPa 4/0.3 MPa 4/1.1 MPa HEAT ELECTRICITY Steam to condensing stage [%] Steam to condensing stage [%]

13 W2E open to new applications Separate user interface (scheme editor) from calculation core (database of particular apparatus, blocks, unit operations) Open system - database of blocks based on user s needs Adjustment of user s interface with new features based on user s needs: Specialized application for design of energy systems in particular segment: Marketing support Simplification and speed up of study calculations Effective (professional) presentation of results Single-purpose applications: Technical-financial models of existing systems: Suitable for final project phases and routine usage in operations (development in MS Excel environment with VBA see below)

14 Contents Application framework Simulation Plant level Model Identification Equipment level Modeling Structural mechanics (FEM) Fluid dynamics (CFD) Design optimization Plant design Equipment design Industrial applications and Conclusions

15 Identification: Purpose and approach Purpose: Prediction of system performance (relation between heat and power production depending on variable input parameters) Identification of power products application (support of contractual negotiations) Approach: Analysis and statistical data processing Employment of statistic software (Statistica) Note: missing data may be predicted using simulation calculation (mass and heat balance must be valid!) Design of simulation model (various development environments) Implementation of model into software application MS Excel (advanced method)

16 MSW incinerator Termizo, a.s. Liberec

17 Simulation model of MSWI in Liberec Technical-financial model of MSW incinerator with annual waste processing capacity of 100 kt, Termizo, a.s. Liberec Simplified technological scheme (process flow-sheet)

18 Analysis of waste LHV 13,5 Median 25%-75% , ,5 LHV (GJ/t) 12,0 11,5 11,0 10,5 Frequency , ,5 9, Month LHV (GJ/t) Box-plot of LHV in individual months Histogram of LHV through months

19 Operation regime of the MSWI boiler Frequency Steam generation (t/h) Waste processed (t/h) Frequency diagram of individual operation steps (hour/year)

20 Regression analysis for key elements Power output (kw) Steam flow rate (t/h) Power output (kw) Steam flow rate (t/h) TG1 output TG2 output Power self-consumption (kw) TG1 + TG2 power output (kw) On-site power consumption

21 Technical-economic model of the MSWI Waste processing capacity of 100 kt user interface

22 Contents Application framework Simulation Plant level Model Identification Equipment level Modeling Structural mechanics (FEM) Fluid dynamics (CFD) Design optimization Plant design Equipment design Industrial applications and Conclusions

23 Thermal processing of waste Plant level: Process design, modelling, optimization Equipment level: Detailed design, modelling, optimization

24 Example of heat recovery system A number of possible heat transfer solutions can be applied: Typical temperature profiles, heat transfer between hot and cold streams and feasible heat exchangers integration in waste processing technology (Pavlas et al., 2007)

25 Example of heat recovery system (continued) Heat exchangers for (very) high temperatures and low fouling: Temperature range: - generally: below 800 C - typically: from 200 to 600 C a) whole module b) arrangement of plates Plate type heat exchanger for air pre-heating (Courtesy of EVECO Brno Ltd)

26 Example of heat recovery system (continued) Heat exchangers for (very) high temperatures and low fouling: Thermal expansion and fouling caused a malfunction and eventually a complete destruction of the heat exchanger (Courtesy of EVECO Brno Ltd)

27 Application example: Air preheater Air preheater Modeled U-tube section

28 Simulation methods and tools Available methods Branch-by-branch approach (1D simplified algebraic model) flow velocity, pressure, and other quantities are evaluated separately for volumes surrounding individual branches and between them 1D discretization (differential model) the entire flow system is covered by a 1D grid of nodes in which quantites are evaluated CFD software (e.g. FLUENT ) Why to use (partly) simplified models? Computational times are significantly shorter with obtained results still being sufficiently precise.

29 Branch-by-branch approach Software system UTES: U-Tube Exchanger Section Designed for prediction of distribution in a specific tube air preheater containing splitting and combining manifolds with variable rectangular cross-sections For both incompressible and compressible fluids User-friendly Application example:

30 Branch-by-branch approach Principle: Evaluate velocity, pressure, etc. in the (i-1)-th section 3. Evaluate velocity, pressure, etc. around i-th branch 4. Evaluate velocity, pressure, etc. in the i-th section 5. Evaluate velocity, pressure, etc. around (i+1)-th branch 6. Evaluate velocity, pressure, etc. in the (i+1)-th section 7.

31 UTES software: User interface Selection of fluid: air, water,... Optimization target: either minimum non-uniformity or minimum pressure drop U-tube inlet orifice type: exserted, conical or circular bellmouth UTES: main window Width/height profile change: linear, circular curved or a special profile from literature

32 Contents Application framework Simulation Plant level Model Identification Equipment level Modeling Structural mechanics (FEM) Fluid dynamics (CFD) Design optimization Plant design Equipment design Industrial applications and Conclusions

33 Analysis of pipeline Incinerator for treatment of sludge from refinery with capacity of 2x6.1 t/hr, temperature of flue gas approx. 800C Identification of force and moment loads of pipeline ends caused by themrmal expansion Need to include expansion bend PIPELINE ANALYSIS distribution of total deformation

34 Economizer of steam generator (1/3) Damage of tubes in the connection with collector Identification of causes of tube damage Verification of proper design

35 Economizer of steam generator (2/3) Results of CFD analysis Analysis of medium distribution in economizer Flow in reverse direction identified streamline

36 Economizer of steam generator (3/3) Results of FEM analysis Stress analysis carried out for global and local model Excessive stress identified at the location of tube damage Stress scale: Pa

37 CFD+FEM: Fluid-structure interaction Mixing of hot and cold hydrogen flow in chemical industry Main pipeline hydrogen flow of 154 t/hr with temperature of 430 C Connected pipeline hydrogen flow of 9 t/hr with temperature of 60 C Agreement of analysis results with measured temperatures approx. 20% modeling measurement 460,0 C ,2 C potential rupture Conclusion: New design of inside shirt

38 CFD+FEM: Fluid-structure interaction Superheat remover in power industry Steam flow rate of 255 t/hr and temperature of 455 C Steam pressure 25 MPa Conclusion: Design is very good

39 Contents Application framework Simulation Plant level Model Identification Equipment level Modeling Structural mechanics (FEM) Fluid dynamics (CFD) Design optimization Plant design Equipment design Industrial applications and Conclusions

40 Boiler of a MSWI Outline of MSWI boiler side view MSWI plant photo

41 Troubleshooting using CFD Analysis of SNCR system

42 Burner design Specification: Nominal duty 1MW Nonpremixed combustion Staged gas injection Guide-vane flame stabilizer Variable nozzle geometry Low NO x emissions

43 Flame prediction Impact of natural convection: Visible flame lift

44 Model validation by measurement Combustion chamber Horizontal, diameter 1 m, max. length 4 m Water-cooled, 7 annular segments of the jacket For burners up to 2 MW Fuels Natural gas Fuel oils Data acquisition and control Automated burner duty and combustion air control Data collection from all sensors on a PC

45 Experimental facility

46 Heat flux [kw/m 2 ] Model validation: Local heat loads Measurement is accurate thanks to furnace design Graph shows a comparison of two alternative models with measured heat flux profile Measurement k-omega k-epsilon Distance [m]

47 Contentsa Application framework Simulation Plant level Model Identification Equipment level Modeling Structural mechanics (FEM) Fluid dynamics (CFD) Design optimization Plant design Equipment design Industrial applications and Conclusions

48 Optimization tool in GAMS environment Investment planning Optimization of cogeneration operation on yearly basis Daily production planning Study including optimization of available fuel utilization Study of potential options of integration of renewables (biomass) into existing plant

49 Optimization system architecture Mathematical model of energy producing system (created in GAMS modeling language) User interface (created in MS Excel software)

50 Modeling example: CHP plant CHP plant in the city of Pilsen co-firing coal and biomass Spent grain (residue from beer production)

51 Modeling example: Task definition waste heat self consumption of electricity losses biomas coal Boiler room II steam steam to turbine room condensing turbine backpressure turbine heat for electricity production electricity production coal biomass Boiler room III losess/ other utilization heat by-pass in steam in hot water heat production coal Boiler room I heat in hot water losses Turbines Power products Fuel Boilers limit of biomass availability kt/year Technical limitation (e.g. IPPC boilers) Financial effect

52 [%] Biomass (kt) Example of results No. 1: Fuel selection Objective: maximize annual profit Preference to use biomass in winter Result contrary to expectations based on thermodynamic laws Impact of legislation calculation of amount of power 2 generated from RES according to local legislation 0 E OZE Q vyr, OZE Q Financial effect - analysis vyr, OZE E tep SV EVL Q Q vyr, FOS vyr 100% Optimal plan (100 kt/year) Stack losses 28% 4% 3% Government subsidy 65% CO2 permition trading Flue gas desulphurization Ash dispozal 80% 60% 40% 20% Transformation losses Heat export 21% electricity prod. Efficiency 25% Self-consumption Elements of positive financial effects (income increase + costs savings) achieved in biomass cogeneration Electricity export 0% Structure of energy utilization through the year[%]

53 Annual income (M ) Annual cost (M ) Example of results No. 2: Scenarios Scenario analysis Scenario 1 fossil fuel only Scenario 2 biomass utilization to availability limit (100 kt/year) Scenario 3 biomass utilization to technical limit (125 kt/year) Calculation of financial balance for various scenarios with suggested operation (profit for Scenario 2 higher by 2 mill. /year than for Sc. 1) Financially feasible (beneficial) replacement of fossil fuel 6,0 5,0 4,0 3,0 2,0 1,0 0,0 0,0 4,8 5,7 1,1 2,3 3,0 20,0 18,0 16,0 14,0 12,0 10,0 8,0 6,0 4,0 2,0 0,0 16,5 17,3 13,6 1,6 1,3 1,2 subsidy for RES-E emission allowances trading fuel purchasing ash, CO2, SO2 dispozal Coal only (0 kt/year) Availability limit (100 kt/year) Tech. limit (125 kt/year) Coal only (0 kt/year) Availability limit (100 kt/year) Tech. Limit (125 kt/year)

54 Biomass used (kt/year) Biomass used (kt/year) Biomass used (kt/y) Example of results No. 3: Sensitivity Analysis of final solution sensitivity to potential change of particular decisive parameters Development of fuel price Development of bonuses on power from RES Development of prices of allowances and their combinations Government subsidy level (%) Acceptoable bonus decrease in S2 category is 14 % Decrease between 2006 and 2010 reached 18% ,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00 9,00 10, Ratio of biomass price to fossil fuel price (-) Technological limit Biomass availability limit 14,6 10,8 8,8 6,9 5,0 3,1 1,2 0,0 price of CO 2 permits (EUR/ton of CO 2 )

55 Contents Application framework Simulation Plant level Model Identification Equipment level Modeling Structural mechanics (FEM) Fluid dynamics (CFD) Design optimization Plant design Equipment design Conclusions

56 Design of furnace air preheating system Furnace without air preheating Air preheating by furnace outlet flue gas heat Air preheating by external heat (processs outlet heat) Preferred system Air preheater placed aside from heater Air preheater as part of furnace

57 Principle of optimum design of furnace air preheating system FLUE GAS TO STACK Annual cost Capital/energy trade-off Total cost AIR FAN AIR p a h a FLUE GAS FLUE GAS FAN p fg, h fg AIR PREHEATER A = Qÿ/(U. m) Optimum PROCESS FURNACE Fuel cost Flue gas and air fans cost Air preheater cost Results of nested technical-economic optimization of air preheating system AIR FAN AIR FUEL BURNERS FLUE GAS FAN T O exist T air,opt OPT T air,max T air ( retrofit only)) flue gas air PLYN 2 AIR PREHEATER

58 Optimization of air preheaters Goal of optimization: Obtaining the most economically optimum design Geometry of air preheater Heat exchange system model W L 2 FAN1 FLUE GAS B 1 L 1 AIR B 2 GAS 2 PLATE TYPE HEAT EXCHANGER FLUE GAS AIR GAS 1 FAN 2

59 Strategy of optimum design OBJECTIVE FUNCTION TOTAL ANNUAL COST - C T CAPITAL COST - C C OPERATING COST - C O COSTS ARE INFLUENCED BY PRESSURE DROP (p) AND HEAT TRANSFER (h) h AND p ARE INTERDEPENDENT deriving equation for CT

60 Objective function - continued We can obtain the objective function in a final form: C T = function (h 1, h 2 ) where: h 1, h 2 two independent variables The OPTIMUM HEAT TRANSFER COEFFICIENTS (h 1, h 2 ) can be obtained from the necessary conditions for the extremum existence of the objective function: C T ( h h 1, h ) C T (, h h h 2 ) RESULTS: optimum design variables h 1, h 2 remember: p = f(h) optimum pressure drops p 1, p 2

61 Advanced optimization approach USING SOLVERS (PARTIALLY OR FULLY PRECOMPILLED CODES): can be considered as optimization algorithms implemented as black boxes allows users to concentrate on the data input and output GAMS (General Algebraic Modeling System) HAS BEEN SELECTED: developed for linear, nonlinear and mixed integer programming simplifies manipulation with general models MINOS solver was applied for the optimization of plate type heat exchanger

62 Typical example: Air preheater for process furnace INPUT DATA: Plate type air preheater in cross-flow arrangement Heat duty: Q = 1842 kw Temperatures: flue gas: 410 C 283 C air: 110 C 239 C Mass flowrate of both air and flue gas: 12.5 kg/s Cost data: Plate type air preheater, $ : A /RE Fan capital cost, $ : (p V ) /RE Power, $/kwh : 2.15/RE Fan efficiency, % : 70 Rate of interest, % : 10 Equipment life, yrs : 10 Maintenance cost ratio from capital cost: 0.05 Equipment availability factor: 0.9 Note: RE is rate of exchange between U.S. dollar and Czech Crown.

63 Typical example: Air preheater for process furnace cont. RESULTS OF OPTIMIZATION: Main parameters Existing solution Optimum solution (plate gaps fixed) Optimum solution by GAMS (plate gaps as variables) flue air flue air flue air gas gas gas B mm h W/m 2 K p Pa L 1 x L 2 x W B m 2.4 x 2 x x 2 x x 1.6 x 2.2 A m C O [$/yr] C T $/yr C T saved

64 Typical example: Air preheater for process furnace cont. TOTAL ANNUAL COSTS (TAC) VERSUS HEAT TRANSFER COEFFICIENTS (h.t.c.): (3D PLOT) TAC [k$/yr] (C T ) h.t.c.- air side [W/m 2.K] h a h fg h.t.c.- flue gas side [W/m 2.K]

65 Contents Application framework Simulation Plant level Model Identification Equipment level Modeling Structural mechanics (FEM) Fluid dynamics (CFD) Design optimization Plant design Equipment design Industrial applications and Conclusions

66 Experience and know-how + sophisticated approach

67 Demonstration through real industrial case I Incinerator for thermal treatment of sludge from pulp production with capacity of 130 t/day

68 Demonstration through real industrial case I Concerned part of technology The modelled part of the exhaust duct FLUE GAS DUCT Problem A Problem B

69 Problem A As was mentioned before, only plain heat exchange surface should be used when heavily polluted fluid is used Apart from fouling, we also have to consider the fact that flue gas leaves secondary combustion chamber at a relatively high temperature, which may cause significant problems as well Example: Plate type heat exchanger for air pre-heating Whole module Arrangement of plates

70 Problem A Thermal expansion and fouling caused a malfunction and eventually a complete destruction of the heat exchanger

71 Problem A Novel type of modular double U-tube air preheater Detail of a double-u-tube bank Fluid distribution (and flow pattern in general) can also influence formation of deposits. Stagnant zones, characterized by a relatively low flow velocity or presence of eddies, are prone to fouling and as such we try to eliminate them.

72 Problem A Problems with fluid distribution => initiation of further research (which is currently being performed) Scheme of flow pattern based on experience Flow pattern in a splitting manifold obtained by CFD simulation

73 Demonstration through real industrial case I Incinerator for thermal treatment of sludge from pulp production with capacity of 130 t/day

74 Demonstration through real industrial case I Concerned part of technology The modelled part of the exhaust duct FLUE GAS DUCT Problem A Problem B

75 Problem B CFD approach for troubleshooting 3D MODEL OF FLUE GAS DUCT Inlet (outlet from air preheater ) Duct expansion element (with water injection nozzles) Vanes in the second duct elbow Manually optimized flow homogenizing swirl generator Virtual prototyping Better fluid flow distribution Avoiding fouling Heat exchanger flue gas-water, Outlet (inlet into stack fan)

76 Problem B Heat exchanger flue gas-water :

77 Problem B Pre-selected measures: Vanes in the second duct elbow Swirl generator above the duct expansion element (two options with 12 and 18 blades)

78 Problem B A Comparison of the alternatives: C B D E

79 Problem B Quantitative comparison two alternative objective functions: Ratio of min. to max. velocity in the reference plane Value of maximum velocity magnitude in the reference plane Both criteria point to a single design alternative (B) A B C D E Velocity maximum [m/s] Velocity minimum [m/s] Min./max. ratio [%]

80 Problem B Additional shape optimization of the pre-selected swirl generator using software SCULPTOR and FLUENT Several deformations were allowed and automatically evaluated by the software

81 Problem B Sensitivity analysis has shown the most promising deformation directions Optimum swirl generator as been found: Red original Blue optimized Obtained improvement is about 8% (decrease of maximum velocity magnitude)

82 Demonstration through real industrial case II Incinerator for treatment of sludge from refinery with capacity of 2 x 6.1 t/hr (4.1 t of sludge and 2.0 t oil slurry)

83 Demonstration through real industrial case II Thermal oil is used as a heat carrier 4 MW cross-flow recuperative HE (two 2 MW tube banks) Plain tube HE 24 m 2 /m C Tube-fin HE with circular tube 841 m 2 /m 3 Plate type HE 124 m 2 /m 3 Air outlet 120 C 160 C 160 C 240 C max. 240 C 880 C 150 C 240 C 215 C 200 C 240 C 160 C 25 C 25 C 240 C 160 C Tube-fin HE with circular tube 728 m 2 /m C 94 C Tube-fin HE with circular tube 916 m 2 /m 3

84 Extremely heavy fouling Heavy fouling in heat exchanger flue gas thermal oil In-line tube bank: Flue gas flow

85 On-line cleaning Various options: high-pressure jets, air/water guns, sonic/steam sootblowers, etc.: Sonic sootblower Steam sootblower (source:

86 Preventive solution Inserts for improved auto-cleaning capability Example of passive enhancement approach for improved auto-cleaning capability in applications with highly fouling flue gas containing high amounts of ash particles (Courtesy of EVECO Brno Ltd)

87 Preventive solution Tube bank inserts as customized solution

88 Periodical cleaning Inserts help to ensure higher heat transfer rate in the exchanger and longer cleaning periods Photos: inserts after operation (Courtesy of EVECO Brno Ltd)

89 Economic evaluation Three different design modifications were evaluated: a) no modification (baseline configuration) b) installation of sonic sootblower c) CFD analysis & installation of tube bank inserts Periodic cleaning requires shutdown shutdown cost was considered as well Costs were evaluated for a period of five years

90 Economic evaluation Best option: CFD & tube bank inserts

91 Up-to-date unit (1 to 3 MW) for energy utilization of biomass Integration of proven technical solutions into a new modern technological unit with progressive features

92 Unit with capacities from 1 to 3 MW for energy utilization of biomass (cont.) 3D model

93 Unit with capacities from 1 to 3 MW for energy utilization of biomass (cont.) Sophisticated design based on use of modern computational tools CFD application Temperature profile on inner shell of combustion chamber and iso-plane surfaces for defined temperature range

94 Unit with capacities from 1 to 3 MW for energy utilization of biomass (cont.) Application for air pre-heater Simulation of the primary air preheater The objective was to improve the design of inlet chamber, turning chamber, baffles and their position

95 Unit with capacities from 1 to 3 MW for energy utilization of biomass (cont.) Reference and demonstration unit

96 Unit with capacities from 1 to 3 MW for energy utilization of biomass (cont.) Reference unit

97 Unit for energy production from contaminated biomass and/or alternative fuels Schematic layout

98 Summary Computer-aided engineering (CAE) is a wide-reaching domain that spans from single pieces of equipment to complete plants from balance modeling to detailed 3D computations and covers all areas of process and power industries Multiple applications of CAE have been demonstrated Current and future work - modelling as a very efficient tool of strategic decisions (conceptual planning of WtE plants locations using two stage stochastic programming) - complex approach: strategic decision tailor-made technology equipment design simulation of operation optimization of waste feeding

99 Acknowledgements Ministry of Education, Youth and Sports of the Czech Republic provided within: Research plan No. MSM Waste and Biomass Utilization Focused on Environment Protection and Energy Generation Research project No. 2B08048 WARMES Waste as Raw Material and Energy Source Many thanks to all my colleagues both from academia and industry whose results are utilized in this presentation

100 The very conclusion That's s all