cape for waste-to-energy -...

CAPE FORUM

University of Pannonia

Veszprém, Hungary

for Waste-to-Energy

Petr Stehlík

Brno University of Technology

Institute of Process and Environmental Engineering

Czech Republic

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

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

Simulation

– Plant level

– Equipment level

Modeling

Design optimization

– Plant 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 10 20 30 40 50 60 70 80 90 100

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 80 90 100

Steam to condensing stage [%]

ELECTRICITY

6/0.3 MPa

4/0.3 MPa

4/1.1 MPa

Steam from HRSG

(40 bar, 400°C)

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

Simulation

– Plant level

– Equipment level

Modeling

Design optimization

– Plant design

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)

Median 25%-75%

1 2 3 4 5 6 7 8 9 10 11 12

Operation regime of the MSWI boiler

Frequency diagram of individual operation steps (hour/year)

5-6 6-7 7-

9-10 10

-…11

13-…

14-…

15-…

16-…

17-…

18-…

19-…

Waste processed (t/h)

Frequency

generation (t/h)

250-300

200-250

150-200

100-150

50-100

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)

500 1000 1500 2000 2500 3000 3500 4000 4500

TG1 + TG2 power output (kW)

TG2 output

0 2 4 6 8 10 12 14 16 18 20

Steam flow rate (t/h)

Technical-economic model of the MSWI

Waste processing capacity of 100 kt – user interface

Contents

Simulation

– Plant level

– Equipment level

Modeling

Design optimization

– Plant design

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

Simulation

– Plant level

– Equipment level

Modeling

Design optimization

– Plant design

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

Results of CFD analysis

– Analysis of medium distribution in economizer

– Flow in reverse direction identified

streamline

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

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

Simulation

– Plant level

– Equipment level

Modeling

Design optimization

– Plant design

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

– 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

Contentsa

Simulation

– Plant level

– Equipment level

Modeling

Design optimization

– Plant design

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

condensing turbine

steam to turbine room

heat for

electricity production

waste heat

electricity production

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

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

1 2 3 4 5 6 7 8 9 10 11 12

ztráty v

kotlích

ztráty

t ransforma

cíVlastní

spotřeba

teplaExport

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

4% 3%Government

subsidy

CO2 permition

trading

Flue gas

desulphurization

Ash dispozal

VLSVtep

vyrFOSvyrOZEvyr

OZEvyr

OZE EEQQQ

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

1 2 3 4 5 6 7 8 9 10 11 12

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

0,02,04,06,08,0

10,012,014,016,018,020,0

fuel purchasing ash, CO2, SO2 dispozal

Coal only (0 kt/year)

Availability limit (100 kt/year)

Tech. Limit (125 kt/year)

subsidy for RES-E emission allowances trading

Coal only (0 kt/year)

Availability limit (100 kt/year)

Tech. limit (125 kt/year)

Example of results No. 3: Sensitivity

100 86 71 57 42 28

Government subsidy level (%)

Acceptoable bonus decrease in S2 category is 14 %

Decrease between 2006 and 2010 reached 18%

14,6 10,8 8,8 6,9 5,0 3,1 1,2 0,0

price of CO2 permits (EUR/ton of CO2)

1,00 2,00 3,00 4,00 5,00 6,00 7,00 8,00 9,00 10,00

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

Simulation

– Plant level

– Equipment level

Modeling

Design optimization

– Plant 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

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

pfg, hfg

AIR FAN

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

PLATE TYPEHEAT EXCHANGER

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

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:

parameters

Existing

solution

Optimum

solution

(plate gaps

fixed)

Optimum

solution by

GAMS (plate

gaps as

variables)

air flue

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

2.4 x 2 x

1.85 x 2 x

1.5 x 1.6 x

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

TOTAL ANNUAL COSTS (TAC) VERSUS HEAT TRANSFER COEFFICIENTS (h.t.c.):

(3D PLOT)

[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

Simulation

– Plant level

– Equipment level

Modeling

Design optimization

– Plant design

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

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

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

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

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

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

Plate type HE 124 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

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

Reference and demonstration unit

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

– 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

cape for waste-to-energy -...

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