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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
PrePre--IntroductionIntroduction
PrePre--IntroductionIntroduction
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
Application frameworkApplication framework
Process
industries
Power
industry WTE
OurOur focusfocus
Research Research Industrial practiceIndustrial practice
University Engineering
company End user 3
End user 2
End user 1
Manufacturer 3
Manufacturer 2
Manufacturer 1
Successful approach combines industrial practice and
research mutual benefit
Thermal processing of waste
Plant level: Process design,
modelling, optimization
Equipment level: Detailed
design, modelling, optimization
Contents
Application framework
Simulation
– Plant level
– Model Identification
– Equipment level
Modeling
– Structural mechanics (FEM)
– Fluid dynamics (CFD)
Design optimization
– Plant design
– Equipment design
Conclusions
MSW incinerator Termizo, a.s. Liberec
MSW incinerator with annual waste processing capacity of 100 kt
Termizo – simplified technological
scheme (process flow-sheet)
Simulation software W2E
Termizo heat recovery system model
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
0
100
200
300
400
500
600
700
800
900
0 10 20 30 40 50 60 70 80 90 100
Ele
ctri
city
pro
du
ctio
n
[kW
h/t
]
Steam to condensing stage [%]Steam to condensing stage [%]Steam to condensing stage [%]
4/0.3 MPa
4/1.1 MPa
6/0.3 MPa
0
10
20
30
40
50
60
70
0 10 20 30 40 50 60 70 80 90 100
HEAT
Steam to condensing stage [%]
Ne
teff
icie
ncy
[%
]
ELECTRICITY
6/0.3 MPa
4/0.3 MPa
4/1.1 MPa
Steam from HRSG
(40 bar, 400°C)
G
Steam for
heating
(11.7 bar)
Consumed on-site
(air-preheating,
deaeration, etc.) Exported
Exported
Consumed on-site
CONDENSER
Waste heat
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)
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
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)
MSW incinerator Termizo, a.s. Liberec
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)
Analysis of waste LHV
Box-plot of LHV in individual months
Histogram of LHV through months
7 8 9 10 11 12 13 14
LHV (GJ/t)
0
100
200
300
400
500
600
Fre
qu
en
cy
Median 25%-75%
1 2 3 4 5 6 7 8 9 10 11 12
Month
9,0
9,5
10,0
10,5
11,0
11,5
12,0
12,5
13,0
13,5
LH
V (
GJ/t
)
Operation regime of the MSWI boiler
Frequency diagram of individual operation steps (hour/year)
5-6 6-7 7-
8 8-9
9-10 10
-…11
-…1
2-…
13-…
14-…
15-…
16-…
17-…
18-…
19-…
0
50
100
150
200
250
300
46-47
43-44
35-36
32-33
29-30
26-27
23-24
20-21
Waste processed (t/h)
Frequency
Steam
generation (t/h)
250-300
200-250
150-200
100-150
50-100
0-50
Regression analysis for key elements
TG1 output
On-site power consumption
22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Steam flow rate (t/h)
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
3200
3400
3600
Po
we
r o
utp
ut
(kW
)
500 1000 1500 2000 2500 3000 3500 4000 4500
TG1 + TG2 power output (kW)
400
600
800
1000
1200
1400
1600
1800
2000
Po
we
r se
lf-c
on
su
mp
tio
n (
kW
)
TG2 output
0 2 4 6 8 10 12 14 16 18 20
Steam flow rate (t/h)
0
100
200
300
400
500
600
700
800
900
Po
we
r o
utp
ut
(kW
)
Technical-economic model of the MSWI
Waste processing capacity of 100 kt – user interface
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
Thermal processing of waste
Plant level: Process design,
modelling, optimization
Equipment level: Detailed
design, modelling, optimization
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)
• Heat exchangers for (very) high temperatures and low fouling:
a) whole module b) arrangement of plates
Plate type heat exchanger for air pre-heating (Courtesy of EVECO Brno Ltd)
Temperature range:Temperature range:
-- generally:generally: below below 800 800 °°C C
-- typically:typically:
from from 2200 to 00 to 660000°°CC
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)
Example of heat recovery system (continued)
Application example: Air preheater
Air preheater ↑
Modeled U-tube section →
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.
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:
Branch-by-branch approach
2 3 4 5 6
Principle:
1. …
2. 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. …
UTES software: User interface
↑ UTES: main window
Selection of fluid: air, water, ...
Optimization target:
either minimum non-uniformity
or minimum pressure drop
Width/height profile change:
linear, circular curved or a
special profile from literature
U-tube inlet orifice type:
exserted, conical or circular
bellmouth
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
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
Economizer of steam generator (1/3)
Damage of tubes in the connection with collector
– Identification of causes of tube damage
– Verification of proper design
Economizer of steam generator (2/3)
Results of CFD analysis
– Analysis of medium distribution in economizer
– Flow in reverse direction identified
streamline
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
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%
113,2°C
460,0°C
200
300
400
measurement
modeling
potential rupture
Conclusion: New design of inside shirt
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
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
Boiler of a MSWI
Outline of MSWI boiler – side view
MSWI plant – photo
Troubleshooting using CFD
Analysis of SNCR system
Burner design
Specification:
– Nominal duty 1MW
– Nonpremixed combustion
– Staged gas injection
– Guide-vane flame stabilizer
– Variable nozzle geometry
– Low NOx emissions
Flame prediction
Impact of natural convection: Visible flame lift
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
Experimental facility
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
Distance [m]
Measurement
k-omega
k-epsilon
Heat
flu
x [k
W/m
2]
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
Optimization tool in GAMS environment
Optimization of cogeneration operation
on yearly basis
Investment planning
Daily production planning
Study including optimization
of available fuel utilization
Study of potential options of
integration of renewables
(biomass) into existing plant
Optimization system architecture
Mathematical model of energy producing
system (created in GAMS modeling
language) User interface
(created in MS Excel software)
Modeling example: CHP plant
CHP plant in the city of Pilsen co-firing coal and biomass
Spent grain
(residue from beer production)
Modeling example: Task definition
biomas
biomass
losses
Boiler room II
steam
condensing turbine
steam to turbine room
heat for
electricity production
waste heat
electricity production
heat
Boiler room I coal heat in hot water
heat production
in steam
in hot water
losess/ other
utilization
self – consumption
of electricity
losses
by-pass
Boiler room III
coal
coal
backpressure turbine
Fuel Boilers
Turbines Power products
limit of biomass availability - 100 kt/year
Technical limitation (e.g. IPPC boilers)
Financial effect
Example of results No. 1: Fuel selection
Elements of positive financial effects (income
increase + costs savings) achieved in
biomass cogeneration
0%
20%
40%
60%
80%
100%
1 2 3 4 5 6 7 8 9 10 11 12
[%]
ztráty v
kotlích
ztráty
t ransforma
cíVlastní
spotřeba
teplaExport
tepla
Vlastní
spotřeba
elektřinyExport
elektřiny
Electricity export
Self-consumption
Heat export
Transformation losses
Stack losses
21% electricity prod. Efficiency 25%
Structure of energy utilization through the year[%]
Financial effect - analysis
65%
28%
4% 3%Government
subsidy
CO2 permition
trading
Flue gas
desulphurization
Ash dispozal
VLSVtep
vyrFOSvyrOZEvyr
OZEvyr
OZE EEQQQ
QE
,,
,
Preference to use biomass in winter
– Result contrary to expectations based on thermo-
dynamic laws
– Impact of legislation – calculation of amount of power
generated from RES according to local legislation 0
2
4
6
8
10
12
14
1 2 3 4 5 6 7 8 9 10 11 12
Bio
ma
ss (
kt)
Optimal plan (100 kt/year)
Objective: maximize annual profit
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
13,6
1,6
16,5
1,3
17,3
1,2
0,02,04,06,08,0
10,012,014,016,018,020,0
fuel purchasing ash, CO2, SO2 dispozal
An
nu
al c
ost (
M€
)
Coal only (0 kt/year)
Availability limit (100 kt/year)
Tech. Limit (125 kt/year)
0,0
1,1
4,8
2,3
5,7
3,0
0,0
1,0
2,0
3,0
4,0
5,0
6,0
subsidy for RES-E emission allowances trading
An
nu
al i
nco
me (
M€
)
Coal only (0 kt/year)
Availability limit (100 kt/year)
Tech. limit (125 kt/year)
Example of results No. 3: Sensitivity
0
20
40
60
80
100
120
140
100 86 71 57 42 28
Bio
mas
s u
sed
(kt
/ye
ar)
Government subsidy level (%)
Acceptoable bonus decrease in S2 category is 14 %
Decrease between 2006 and 2010 reached 18%
30
50
70
90
110
130
14,6 10,8 8,8 6,9 5,0 3,1 1,2 0,0
Bio
mas
s u
sed
(kt
/ye
ar)
price of CO2 permits (EUR/ton of CO2)
0
20
40
60
80
100
120
140
1,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00 9,00 10,00
Bio
ma
ss u
sed
(k
t/y
)
Ratio of biomass price to fossil fuel price (-)
Technological limit Biomass availability limit
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
Contents
Application framework
Simulation
– Plant level
– Model Identification
– Equipment level
Modeling
– Structural mechanics (FEM)
– Fluid dynamics (CFD)
Design optimization
– Plant design
– Equipment design
Conclusions
Design of furnace air preheating system
Air preheating by furnace outlet flue gas heat
Air preheating by external heat (processs outlet heat)
Furnace without air preheating
Air preheater placed
aside from heater
Air preheater as
part of furnace
Preferred
system
Principle of optimum design
of furnace air preheating system
Capital/energy trade-off
Annual
cost
Optimum
Total cost
Fuel cost
Air preheater cost
Tair Tair,OPT
Flue gas and
air fans cost
Tair,max TO
exist OPT
FLUE GAS TO STACK
FLUE GAS FAN
AIR PREHEATER
A = Qÿ/(U.m)
FLUE GAS
PROCESS
FURNACE
BURNERS
FUELAIR
AIR FAN
AIR
pa
ha
pfg, hfg
AIR FAN
air
PLYN 2
FLUE GAS FAN
flue gas
AIR PREHEATER
Results of nested
technical-economic
optimization of air preheating
system
( ← retrofit only))
OptimizationOptimization ofof air air preheaterspreheaters
Goal of optimization:
Obtaining the most economically optimum design
FLUE GAS
FLUE GAS
AIR
AIRB2
B1
L2
L1
W
PLATE TYPEHEAT EXCHANGER
FAN1
FAN 2
GAS 1
GAS 2
Geometry of air preheater Heat exchange system model
StrategyStrategy ofof optimum designoptimum design
OBJECTIVE FUNCTION
COSTS ARE INFLUENCED BY PRESSURE DROP
(p) AND HEAT TRANSFER (h)
h AND p ARE INTERDEPENDENT
… deriving equation for CT …
TOTAL ANNUAL COST - CT
CAPITAL COST - CC OPERATING COST - CO
ObjectiveObjective functionfunction -- continuedcontinued
We can obtain the objective function in a final form:
CT = function (h1 , h2)
where: h1 , h2 … two independent variables
The OPTIMUM HEAT TRANSFER COEFFICIENTS (h1 , h2)
can be obtained from the necessary conditions for the
extremum existence of the objective function:
RESULTS:
optimum design variables h1 , h2
remember: p = f(h) optimum pressure drops p1 , p2
0),(
1
21 h
hhCT
0
),(
2
21 h
hhCT
AdvancedAdvanced optimizationoptimization approachapproach
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
TypicalTypical exampleexample:: Air Air preheaterpreheater forfor processprocess furnacefurnace
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, $ : 17870•A0.874/RE
Fan capital cost, $ : 66.29•(p•V •)0.883/RE
Power, $/kWh : 2.15/RE Fan efficiency, % : 70
Rate of interest, % : 10 Equipment life, yrs : 10
Maintenance cost ratio from capital cost: 0.05
Equipment availability factor: 0.9
Note: RE is rate of exchange between U.S. dollar and Czech Crown.
RESULTS OF OPTIMIZATION:
Main
parameters
Existing
solution
Optimum
solution
(plate gaps
fixed)
Optimum
solution by
GAMS (plate
gaps as
variables)
flue
gas
air flue
gas
air flue
gas
air
B mm 12 12 12 12 10 10
h W/m2K 74 55 47 44 48 45
p Pa 750 528 212 343 208 335
L1 x L2 x
WB m
2.4 x 2 x
1
1.85 x 2 x
1.8
1.5 x 1.6 x
2.2
A m2 409 547 536
CO [$/yr] 20 322 8 129 7 933
CT $/yr 46 576 38 545 37 695
CT saved - 17 19
TypicalTypical exampleexample:: Air Air preheaterpreheater forfor processprocess furnacefurnace –– contcont..
ha hfg
(CT)
TOTAL ANNUAL COSTS (TAC) VERSUS HEAT TRANSFER COEFFICIENTS (h.t.c.):
(3D PLOT)
TAC
[k$/yr]
h.t.c.- flue gas side
[W/m2.K]
h.t.c.- air side
[W/m2.K]
TypicalTypical exampleexample:: Air Air preheaterpreheater forfor processprocess furnacefurnace –– contcont..
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
ExperienceExperience and knowand know--how how
+ +
sophisticatedsophisticated approachapproach
Demonstration through real industrial case “I”
Incinerator for thermal
treatment of sludge
from pulp production
with capacity of 130 t/day
Concerned part of technology
The modelled part
of the exhaust duct FLUE GAS
DUCT
Demonstration through real industrial case “I”
Problem A Problem B
• 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
Problem A
Thermal expansion and fouling caused a malfunction and
eventually a complete destruction of the heat exchanger
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.
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
Problem A
Demonstration through real industrial case “I”
Incinerator for thermal
treatment of sludge
from pulp production
with capacity of 130 t/day
Concerned part of technology
The modelled part
of the exhaust duct FLUE GAS
DUCT
Demonstration through real industrial case “I”
Problem A Problem B
VirtualVirtual prototypingprototyping
BetterBetter fluid fluid flowflow
distributiondistribution
AvoidingAvoiding foulingfouling
Inlet (outlet from air
preheater )
Heat exchanger “flue gas-water”,
Duct expansion element (with
water injection nozzles)
Outlet (inlet into stack fan)
Manually optimized
flow homogenizing
swirl generator
Vanes in the Vanes in the
second duct second duct
elbowelbow 3D MODEL3D MODEL
OF FLUE GAS DUCTOF FLUE GAS DUCT
CFD approach for troubleshooting
Problem B
Heat exchanger “flue gasHeat exchanger “flue gas--water”:water”:
Problem B
PrePre--selected measures: selected measures:
•• Vanes in the second Vanes in the second
duct elbowduct elbow →→
•• Swirl generator above the duct Swirl generator above the duct
expansion element (two options expansion element (two options
with 12 and 18 blades)with 12 and 18 blades) →→
Problem B
A B C
D E
ComparisonComparison of the alternatives:of the alternatives:
Problem B
•• Quantitative comparisonQuantitative comparison –– two alternative objective functions:two alternative objective functions:
•• Ratio of min. to max. velocity in the reference plane Ratio of min. to max. velocity in the reference plane
•• Value of maximum velocity magnitude in the reference Value of maximum velocity magnitude in the reference
planeplane
•• Both criteria point to a single design alternative (B)Both criteria point to a single design alternative (B)
23.2 14.7 14.3 26.1 14.7 Min./max. ratio [%]
2.522 1.758 1.511 2.722 1.709 Velocity minimum [m/s]
10.89 11.97 10.60 10.42 11.60 Velocity maximum [m/s]
E D C B A
Problem B
•• Additional shape optimization of the preAdditional shape optimization of the pre--selected swirl selected swirl
generator using software SCULPTOR and FLUENTgenerator using software SCULPTOR and FLUENT
•• Several deformations were allowed and automatically Several deformations were allowed and automatically
evaluated by the softwareevaluated by the software
Problem B
•• Sensitivity analysis has shown the most promising deformation Sensitivity analysis has shown the most promising deformation
directionsdirections
•• Optimum swirl generator as been found:Optimum swirl generator as been found:
•• Red Red –– original original
•• Blue Blue –– optimized optimized
•• Obtained improvement is Obtained improvement is
about 8% (decrease of about 8% (decrease of
maximum velocity magnitude)maximum velocity magnitude)
Problem B
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)
Demonstration through real industrial case “II”
Plain tube HE 24 m2/m3
Tube-fin HE with circular tube 728 m2/m3
Tube-fin HE with circular tube 916 m2/m3
Plate type HE 124 m2/m3
Tube-fin HE with circular tube 841 m2/m3
240 °C
880 °C
160 °C
240 °C
190 °C
240 °C 160 °C
max. 240 °C
160 °C
200 °C
94 °C
240 °C 160 °C
Air outlet 120 °C
150 °C
215 °C 240 °C
25 °C 25 °C
• Thermal oil is used as a heat carrier
• 4 MW cross-flow recuperative HE (two 2 MW tube banks)
Demonstration through real industrial case “II”
Extremely heavy fouling
• Heavy fouling in heat exchanger “flue gas – thermal oil”
InIn--line tube bank:line tube bank:
FlueFlue gasgas flowflow
OnOn--line line cleaningcleaning
Various options: high-pressure jets, air/water guns, sonic/steam
sootblowers, etc.:
Sonic sootblower Steam sootblower (source: www.clydebergemann.com)
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)
Inserts for improved auto-cleaning capability
PreventivePreventive solutionsolution
Tube bank inserts as customized solution
PreventivePreventive solutionsolution
• Inserts help to ensure higher heat transfer rate in the exchanger and longer cleaning periods
Photos: inserts after operationPhotos: inserts after operation (Courtesy of EVECO Brno Ltd)
PeriodicalPeriodical cleaningcleaning
EconomicEconomic evaluationevaluation
• 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
EconomicEconomic evaluationevaluation
Best option:
CFD & tube bank inserts
UUpp--toto--date unitdate unit ((1 to 3 MW1 to 3 MW)) for energy utilization of biomassfor energy utilization of biomass
Integration of proven technical solutions into a new
modern technological unit with progressive features
Unit with capacities from 1 to 3 MW Unit with capacities from 1 to 3 MW for energy utilization of biomassfor energy utilization of biomass ((contcont.).)
3D model
Unit with capacities from 1 to 3 MW Unit with capacities from 1 to 3 MW for energy utilization of biomassfor energy utilization of biomass ((contcont.).)
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
Application for air pre-heater
•• Simulation of the primary air Simulation of the primary air preheaterpreheater
•• The objective was to improve the design of inlet The objective was to improve the design of inlet
chamber, turning chamber, baffles and their positionchamber, turning chamber, baffles and their position
Unit with capacities from 1 to 3 MW Unit with capacities from 1 to 3 MW for energy utilization of biomassfor energy utilization of biomass ((contcont.).)
Unit with capacities from 1 to 3 MW Unit with capacities from 1 to 3 MW for energy utilization of biomassfor energy utilization of biomass ((contcont.).)
Reference and demonstration unit
Unit with capacities from 1 to 3 MW Unit with capacities from 1 to 3 MW for energy utilization of biomassfor energy utilization of biomass ((contcont.).)
Reference unit
Unit for energy production fromUnit for energy production from contaminated contaminated biomass and/or alternative fuelsbiomass and/or alternative fuels
Schematic layout
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
Acknowledgements
Ministry of Education, Youth and Sports of the Czech Republic provided within:
– Research plan No. MSM 0021630502 ‘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
ThatThat''s all …s all …
The very conclusionThe very conclusion