Neural Network Heater Corrosion

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    LAPPEENRANTA UNIVERSITY OF TECHNOLOGY

    Department of Chemical Technology

    Laboratory of Process Engineering

    NEURAL NETWORK FORPREDICTION OF

    SUPERHEATER FIRESIDE CORROSION IN

    ATMOSPHERIC FLUIDIZED BED COMBUSTION

    Examiners professor Lars Nystrom, Lappeenranta University of Technology

    professor Mikko Hupa, bo Akademi University

    Kotka 05.04.1998

    Pasi Makkonen

    Taaplaajankatu 1 B 7

    48900 SUNILA

    THESIS FOR THE DEGREE OF LICENTIATE OF TECHNOLOGY

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    PREFACE

    This study has been carried out at the Foster Wheeler Karhula R&D Center between 1993

    and 1997. The first four years were spent by gathering information for the corrosion

    database, based on which the neural network model was developed in 1997.

    The subject for the thesis changed several times during the study. At first, the purpose was

    to create a theoretical model for fouling and corrosion. However, theories of fouling and

    even calculation programs for estimation of fouling rates are available. Corrosion has been

    studied for decades, and many theories exist, leaving little room for totally new theories.

    Corrosion is such a complex phenomenon that whatever theoretical prediction method is

    used for its estimation, real life brings surprises. So why not use something that can learn

    the basic relations as a human can and continue learning as knowledge increases? Neural

    networks have such capabilities, so the thing remaining to be done was to construct a

    reasonably complex network and teach it to produce the desired result by using the input

    data available. Such a network can learn all the time, and give more reliable estimations as

    knowledge increases.

    Special thanks to Lic. Tech. Matti Hiltunen for the long conversations which made this

    book possible. This book is dedicated with love to my wife Eeva and my daughter Tea who

    both have made the life worth living.

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    ABSTRACT

    Pasi Makkonen Neural Network for Prediction of Superheater Fireside Corrosion in

    Atmospheric Fluidized Bed Combustion

    Licentiate thesis Lappeenranta University of Technology89 Pages, 50 Figures, 19 Tables, 6 Appendices

    Examiners professor Lars Nystrm, Lappeenranta University of Technologyprofessor Mikko Hupa, bo Akademi University

    Keywords neural network, fluidized bed, combustion, corrosion, fouling

    The theory of neural networks for determination of difficult relations has been available forseveral decades, but lack of computational resources has limited the commercial use ofneural networks until the middle of the 1980s. During the short operating period, theneural networks have reached an indispensable status in handling of unclear correlationsand large databases. Different self-organizing maps and adaptive fuzzy controls arecommon uses for neural networks. The large availability of neural networks is based onlearning: the more data there is available, the more reliable the result will be. A neuralnetwork can learn all the time and optimize itself by using a feedback connection.

    Superheater corrosion causes vast annual losses for the power companies. If the corrosioncould be reliably predicted, the plants could be designed accordingly, and knowledge offuel selection and determination of process conditions could be utilized for minimization ofsuperheater corrosion. Growing interest in using recycled fuels creates additional demandsfor the prediction of corrosion potential. Models depending on corrosion theories will fail,if interdependencies between inputs and the output are poorly known or even moreunknown. A prediction model based on a neural network is able to learn from mistakes andimprove its performance without major changes in the network structure as the amount ofdata increases.

    During this study, a corrosion database was developed based on fuel and bed materialanalyses and measured corrosion data. The database was used as learning input for theneural network model. The selected neural network contained two hidden layers consistingof non-linear neurons.

    The developed neural network predicts superheater corrosion with 80 % accuracy on theearly stages of a project. This helps to recognize the risk cases and allows selection ofcorrosion resistant boiler designs. Furthermore, fuel and tube materials can be selectedaccordingly. The accuracy of the prediction will improve as the amount of data increases.

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    TABLE OF CONTENTS

    PREFACE

    ABSTRACT

    CONTENTS

    SYMBOLS

    THEORETICAL PART

    1.0 INTRODUCTION................................................................................................................. 1

    2.0 NEURAL NETWORKS....................................................................................................... 2

    2.1 Neuron.......................................................................................................................3

    2.2 Activation Function ..................................................................................................4

    2.3 Neural Networks.......................................................................................................6

    2.4 Learning Procedures ................................................................................................. 7

    2.5 Applications.............................................................................................................. 7

    3.0 FLUIDIZED BED TECHNOLOGY....................................................................................8

    3.1 Bubbling Fluidized Bed Boiler................................................................................. 93.2 Circulating Fluidized Bed.........................................................................................10

    4.0 SUPERHEATER FIRESIDE CORROSION....................................................................... 13

    4.1 Mechanism and Kinetics of Oxidation.....................................................................14

    4.2 Sulphur Corrosion.....................................................................................................17

    4.2.1 Formation of SO3 in Fluidized Bed Combustion .....................................17

    4.2.2 Kinetics of Sulphidation............................................................................ 18

    4.2.3 Sulphur Corrosion Mechanisms ...............................................................194.2.4 Sulphide Corrosion ...................................................................................19

    4.2.5 Sulphate Corrosion.................................................................................... 20

    4.3 Chlorine Corrosion ...................................................................................................20

    4.3.1 Chlorine Corrosion Mechanisms ..............................................................21

    4.3.2 Kinetics of Chlorine Corrosion................................................................. 24

    4.4 Other Corrosion Types .............................................................................................26

    4.4.1 Vanadium Corrosion.................................................................................26

    4.4.2 Erosion-Corrosion.....................................................................................26

    4.4.3 Corrosion in the Soot-Blower Region ......................................................27

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    4.5 Fouling and Deposit Formation................................................................................27

    4.5.1 Particle Impaction .....................................................................................28

    4.5.2 Condensation............................................................................................. 29

    4.5.3 Thermophoresis......................................................................................... 30

    4.5.4 Chemical Reactions................................................................................... 31

    4.6 Measurement of High-Temperature Corrosion........................................................ 32

    5.0 EFFECT OF FUEL CHARACTERISTICS......................................................................... 33

    5.1 Fuel Composition .....................................................................................................34

    5.1.1 Coals.......................................................................................................... 34

    5.1.2 Peat ............................................................................................................ 36

    5.1.3 Biofuels......................................................................................................36

    5.1.4 Recycled Fuels ..........................................................................................37

    5.2 Fuel Feeding..............................................................................................................38

    6.0 EFFECT OF BED MATERIAL...........................................................................................39

    7.0 EFFECT OF TEMPERATURES.........................................................................................40

    7.1 Flue Gas Temperature............................................................................................... 40

    7.2 Tube Material Temperature...................................................................................... 41

    7.3 Combustion Temperature.........................................................................................437.4 Bed Material Temperature........................................................................................43

    8.0 EFFECT OF SUPERHEATER DESIGN ............................................................................ 43

    8.1 Tube Heat Exchangers..............................................................................................44

    8.2 Omega-panels ........................................................................................................... 45

    8.3 External (EHE) Superheaters ...................................................................................45

    8.4 Soot Removal Systems.............................................................................................46

    9.0 WAYS OF PREVENTING SUPERHEATER CORROSION............................................47

    9.1 Fuel Fractions............................................................................................................48

    9.2 Superheater Materials ............................................................................................... 48

    9.3 Superheater Design................................................................................................... 50

    9.4 Boiler Design ............................................................................................................50

    9.5 Process Conditions ................................................................................................... 50

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    EXPERIMENTAL PART

    10.0 SCOPE OF THE EXPERIMENTAL PART......................................................................51

    11.0 CORROSION EVALUATION..........................................................................................52

    11.1 Optical Microscopy ................................................................................................52

    11.2 Electron Microscopy...............................................................................................53

    11.3 Chemical Analyses .................................................................................................54

    11.4 Calculations ............................................................................................................ 55

    11.5 Probe Tests.............................................................................................................. 55

    11.5.1 Deposit Formation Tests......................................................................... 56

    11.5.2 Corrosion Probe Tests............................................................................. 5611.6 Tube Thickness Measurements ..............................................................................57

    12.0 CORROSION CASES........................................................................................................57

    12.1 Corrosion in BFB Boilers.......................................................................................57

    12.2 Corrosion in CFB Boilers.......................................................................................59

    13.0 CORROSION TESTS.........................................................................................................60

    13.1 Corrosion Tests in Pilot Units ................................................................................ 60

    13.2 Corrosion Tests in Commercial Units....................................................................60

    14.0 CORROSION DATABASE............................................................................................... 61

    15.0 PREDICTION OF CORROSION WITH A NEURAL NETWORK................................61

    15.1 User Interface for the Neural Network...................................................................61

    15.2 Evaluation of The Neural Network Model ............................................................ 63

    15.2.1 Test Plan for the Neural Networks .........................................................63

    15.2.2 The Yates's Algorithm ............................................................................ 64

    15.2.3 Statistical Means .....................................................................................65

    15.3 Learning data ..........................................................................................................65

    15.4 Neural Networks Constructed................................................................................66

    15.4.1 Linear Feed Forward Network................................................................ 66

    15.4.2 Non-Linear Feed Forward Network .......................................................67

    15.4.3 Feed Forward Network with a Hidden Layer .........................................67

    15.4.4 Feed Forward Network with Two Hidden Layers..................................68

    15.4.5 Further Improved Networks....................................................................68

    15.5 Learning Algorithms............................................................................................... 69

    15.5.1 Learning of Linear Networks.................................................................. 69

    15.5.2 Error Correcting Learning.......................................................................70

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    15.5.3 Modified Learning Algorithms...............................................................72

    16.0 RESULTS............................................................................................................................73

    16.1 Evaluation of Different Neural Networks.............................................................. 76

    17.0 FURTHER IMPROVEMENT OF THE MODEL.............................................................80

    18.0 SUMMARY........................................................................................................................81

    REFERENCES

    APPENDICES

    1. List of activation functions

    2. CFB Pilot models at the Karhula R&D Center of Foster Wheeler

    3. Neural network learning data

    4. Neural networks

    5. Residuals of the selected neural network

    6. Sensitivity analysis of the selected neural network

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    SYMBOLS

    a slope parameter

    A constant

    Cc cubic constant, (kg/m)3

    Cl linear constant, kg/m

    Co logarithmic constant, s-1

    Cp parabolic rate constant, (kg/m)2

    Cpl paralinear constant, (kg/m)2

    Ct correction factor

    d(t) desired response at time t

    e error vector

    e(t) error at time t

    EA activation energy, J/mol

    f frequency factor, m/s

    I unity matrix

    j, k, p indexes

    J jacobian matrixk1 rate constant, mol/(m s)

    k2 rate constant, mol/(m s)

    kc cubic rate constant, (kg/m)3 s-1

    kl linear rate constant kg/(m s)

    ko logarithmic rate constant, kg/m

    kp parabolic rate constant, (kg/m)2 s-1

    kpl paralinear rate constant, kg/m

    kv linear rate constant for scale vaporization, m/s

    qm weight gain per surface area, kg/m

    r radius of particle, m

    R molar gas constant, J/(mol K)

    t time, s

    T temperature, K

    T0 reference temperature, K

    u result of linear combinerUT thermophoretic velocity, m/s

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    v sum of input and bias

    w synaptic weight

    W array of synaptic weights

    x input signal

    X array of input signals

    y output signal

    z scale thickness, m

    GREEK LETTERS

    scaler

    learning rate

    activation function

    mean free path, m

    g thermal conductivity of gas, W/(m K)

    p thermal conductivity of particle, W/(m K)

    kinematic viscosity, m/s threshold / bias

    (t) sum of squared errors at time t

    Temperature gradient, K

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    DEFINITIONS

    BFB Bubbling fluidized bed

    BMP Bitmap image

    CFB Circulating fluidized bed

    EDS Energy dispersive x-ray analysis

    EHE External heat exchanger

    LHV Lower heat value

    M Metal catalyst

    Me Metal

    RDF Refuse derived fuel

    REF Recycled fuel

    SEM Scanning electron microscope

    SSE Sum of squared errors

    SSEn Sum of squared errors, classed data

    Stdev Standard deviation

    TIFF Tagged image file format

    XRF X-ray fluorescence

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    1.0 INTRODUCTION

    Neural networks are an effective method of prediction when the relations which correspond

    to the result are not fully known. The use of neural networks is based on teaching the

    network with existing data, and, after the sufficient prediction accuracy has been achieved,

    utilizing the network with new input data to achieve a solution for the problem. The

    decision chain behind the answer does not have to be known, and a result can be derived

    with little data. However, in order to create a reliable device for prediction, a large amount

    of data has to be available for the learning process.

    The material loss in superheaters is one of the most expensive phenomena concerning the

    maintenance costs of commercial fluidized bed boilers. Superheater corrosion is a common

    reason for boiler shutdown in the case of combustion of fuels containing corrosive

    substances. A shutdown and superheater reparation normally causes a major increase in the

    operating costs. The need for new methods for safe and environmentally friendly

    combustion of RDF and biofuels, as well as the multifuel combustion, creates a driving

    force for the boiler makers to create new, cost efficient solutions for the task. Fluidized bed

    combustion has been commercially available for several decades, but only lately it has been

    granted to be the most feasible solution for combustion of RDF, biofuels, and multifuel

    mixtures. However, knowledge about the factors which cause corrosion in fluidized bed

    combustion are not fully known. This study is a combination of theory and practice about

    the possibilities of decreasing the corrosion rate and creating longer enduring superheaters.

    The corrosion of steel can be described as a tendency of a metal alloy to return back to a more

    stable composition; in other words, the metal releases the energy it has accumulated in the

    steel making process. The helpers in this process can be gaseous ions, molten salts, or any

    other types of electrolytes, which create an electric gateway for the electrochemical

    phenomena taking place when the environment is right. If there is a way of preventing the

    electron exchange, the electrochemical corrosion could be stopped. However, a major part of

    the corrosion in hot environments may be caused by oxidation, which can take place without

    electrochemical causes. This makes the corrosion prevention a difficult task: when a metal is

    designed for the maximum strength, it may not be suitable for corrosive environment, or the

    internal oxidation rate can be too high.

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    The reason for the material loss in a steam tube of a superheater can be erosion, but in most

    cases the main factor behind the material loss is corrosion. Often the corrosion taking place in

    a superheater is sulphur and/or chlorine corrosion, but other corrosion types can also be the

    cause of the material loss. The material loss can also be both erosion and corrosion, so that

    the erosion removes the protective oxide layer and the corrosion can attack the steel with full

    force. Experimental studies on the corrosion mechanisms exist, and the most well known

    theories can be used as a basis for theoretical corrosion models.

    The factors which have to be taken into account when creating a neural network model for

    the superheater corrosion taking place in a fluidized bed boiler are:

    fuel characteristics bed material characteristics temperatures, both in the combustion zone and in the superheater area boiler design.

    The purpose of this study was to create a "handbook" for theories of the superheater corrosion

    in fluidized bed combustion, and a guideline for the ways of creating process conditions

    which decrease the risk of fireside corrosion. The experimental part consists of fireside

    corrosion examples and descriptions of probe tests made to evaluate the actual risks taking

    place in different types of fluidized bed boilers. These results have been utilized for a

    corrosion database and a corrosion prediction model based on a neural network.

    2.0 NEURAL NETWORKS

    The theory behind neural networks is an old one. The idea of neuron was introduced by

    Ramn y Cajl in the middle of the 20 th century, but at that time, devices for utilizing the

    theory in practice were unavailable (Haykin /1/). The first applications utilizing neural

    network for prediction were adaptive linear filters in the middle of the 1980s. The idea

    behind neural networks is the similarity to the action of brain cells: the neural network allows

    complex, nonlinear, and multitasking function.

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    By Haykin /1/, the properties available with neural networks can be listed as follows:

    nonlinearity input-output mapping

    adaptivity evident response contextual information fault tolerance very large scale integrated (VLSI) implementability uniformity of analysis and design neurobiological analogy.

    2.1Neuron

    A neuron is a simple derivation element consisting of several inputs and one output. A

    neuron is shown in Figure 1. The operation of the neuron in Figure 1 can be presented with

    two equations

    u w xk kj jj

    p

    ==

    1

    (1)

    where u result of linear combiner

    j, k, p indexes

    w synaptic weight

    x input signal

    and

    ( )y uk k k= (2)

    where activation function

    y output signal

    threshold / bias.

    Each input xcan be separately scaled with a synaptic weightw. The weighed inputs are then

    summed in the linear combiner as u prior to the activation function . The output y can

    be lowered by the threshold . It is also possible to increase the output by bias, which is the

    negative of the threshold. Each neuron and each input of a neuron have its own index, which

    can be written as a subscript. In Figure 1, k is used for indexing the neuron and p for the

    input.This allows a simple algorithm for the handling of neurons and their inputs.

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    Figure 1. Model of a neuron (Haykin /1/, page 8).

    The output of a neuron depends on the inputs, weights, activation function, and

    threshold/bias. In principle, a neuron is a simple element performing a complex task.

    2.2 Activation Function

    The activation functions can be divided into three basic types:

    threshold function piecewise-linear function sigmoid function.

    If we leave out the effect of the threshold/bias, the threshold function acts as an on-off switch,

    resulting in a ramp operation, in which the output depends on the linear combiner (equation

    (1) - threshold) according to function (3).

    yif v

    if v=

    >

    1

    0,

    ,

    ,

    (4)

    resulting in a linear change between 0 and 1 in the output when the linear combiner changes.

    The sigmoid function gives out a strictly increasing function that exhibits smoothness and

    asymptotic properties, for example equation (5).

    yav

    =+

    1

    1 exp( )(5)

    where a slope parameter.

    The outputs of different activation functions are illustrated graphically in Figure 2, the value

    of the slope parameter in function (5) is 1. The different activation functions can be used for

    various purposes in neural networks. A list of typical activation functions is shown as

    Appendix 1.

    0.0

    0.2

    0.4

    0.6

    0.8

    1.0

    1.2

    -5 -4 -3 -2 -1 0 1 2 3 4 5

    Threshold Piecewise-linear Sigmoid

    Figure 2. Responses of different activation functions.

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    2.3Neural Networks

    A neural network is a combination of one or more neurons which are connected according to

    the desired operation. The architectures in which the neurons are structured depend on the

    learning algorithms used. The simpliest approach is the linear single-layer feed-forward

    network, illustrated in Figure 3. This network consists of an input layer of source nodes, a

    single layer of neurons, and one or more outputs. If the number of neuron layers is increased,

    the added neuron layers are called hidden, because they are not directly visible to the user.

    The hidden neurons intervene between the external input and the network output, allowing

    higher order statistics. The multi-layernetwork also shown in Figure 3 is fully connected, but

    it is also possible to leave some connections open by setting their synaptic weights to zero.

    When one or more connections are connected as a feedback, the neural network type changes

    to recurrent. This allows the use of the network output as an input, which in many cases

    speed up the learning process.

    A lattice structure is a feed-forward neural network, in which the outputs are arranged in

    rows and columns, allowing a matrix-like layout. Self-organizing maps are one use of

    lattice structures.

    Figure 3. Two examples of feed forward neural networks (Haykin /1/, pages 18 and 19).

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    2.4 Learning Procedures

    The learning process is the initial function when working with neural networks. In order tofunction as desired, a neural network has to learn with known data. It is also possible to use

    the output of the neural network as new input data, so that the network improves its

    performance as the number of learning runs increases. Undergoing changes is an essential

    part of the learning process. The changes can take place in the synaptic weights, biases, and

    even in the activation functions. However, the larger the number of neurons in the network

    is, the more data has to be used for the learning process.

    Several learning procedures exist. The theory behind these processes is omitted in this

    study, but the methods used for teaching of the model will be described in some detail in

    the experimental part.

    2.5 Applications

    The amount of neural network applications is increasing all the time. The variety of possible

    subjects is vast, and only the difficulties in teaching the network and in some cases the high

    number of neurons required set the limits to the target applications. If the problem at hand is

    simple, or the relations between the variables are easy to determine, the use of neural

    networks provides little benefit. However, when the problem becomes more complicated, the

    neural networks can provide a solution for the task. Examples of the use of neural networks

    in Finland are listed by Bulsari and Saxn /2/, and Koikkalainen /3/. Some of these examples

    are included in the following list:

    image compression modelling:

    non-linear time seriessupermarket customer profilefault diagnostics

    self organizing maps conscious machines.

    The variety of applications is increasing all the time.

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    3.0 FLUIDIZED BED TECHNOLOGY

    The fluidized bed technology has been utilized for combustion since the 1920s. Foster

    Wheeler has been utilizing the fluidized bed technology for combustion since the end of the

    1960s. The first fluidized bed boilers were of the bubbling bed type (BFB) (Abdulally and

    Moore /4/) in which the fuel was fed on top of a sand bed, which was kept in fluidized state

    by primary air. The operating principle is shown in Figure 4. This technology is still

    competitive in combustion of biofuels and sludges.

    Figure 4. Bubbling fluidized bed boiler (Foster Wheeler Energia Oy /5/).

    The first commercial fluidized bed boilers utilizing a solids separator for solids recirculation

    were constructed in the 1970's by Lurgi /6/. The idea is to use a higher fluidization velocity

    and return most of the escaping bed material back to the furnace with a cyclone, the principle

    is shown in Figure 5. The benefit of the CFB (Circulating Fluidized Bed) technology is that

    the residence time of the solid fuel in the boiler is longer, because most of the unburned fuel

    is returned back to the furnace. The bed can also be utilized for sulphur capture by adding

    limestone. The sulphur can be taken out as gypsum mixed with the bottom and fly ashes.

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    Figure 5. Circulating fluidized bed boiler (Foster Wheeler Energia Oy, /5/).

    3.1 Bubbling Fluidized Bed Boiler

    The bubbling fluidized bed boiler operates with fluidization velocities less than 3 m/s. In

    atmospheric combustion, this fluidization velocity allows 3 MW/m of net effect per cross

    section area of the furnace (Hyppnen and Raiko /7/). About half of the combustion air is

    brought through the grid into the furnace as primary air, the rest of the combustion air can be

    introduced as secondary and tertiary air.

    The variety of fuels used in commercial size bubbling bed boilers is large, from coal to RDF

    and sludges. The fuel is usually fed on the surface of the bubbling bed, some air can be used

    to distribute the fuel evenly on the bed surface.

    The large fuel scale can be both a curse and a blessing: if the mainly used fuel contains no

    harmful components, it is easy to think that no corrosion will take place. On some occasions

    it is easy to create severe corrosive environment with an accessory fuel which contains some

    harmful element. If the corrosion attack starts, the change back to normal fuel may not be

    sufficient for stopping the initial attack: the corrosion may continue under the protective

    metal oxide layer.

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    In BFB boilers, the superheaters are normally located behind a peak to protect them from the

    radiation of the bed region. In older designs, some superheaters were located inside the

    furnace so that the superheater tubes were exposed to the radiation from the combustion zone.

    This design has been proven to be unuseful for waste and biofuel combustion due to

    excessive corrosion, if the steam temperature is higher than 400 C.

    The bubbling fluidized bed boiler is commercially available in a large scale. At the moment,

    the largest bubbling bed boiler ever built is in the United States. The unit is combusting sub-

    bituminous coal, and the thermal input of the unit is 387 MW. The design values for the

    steam produced by the unit are 105 bar and 540 C (Foster Wheeler /8/).

    3.2 Circulating Fluidized Bed

    The circulating fluidized bed boilers operate with higher fluidization velocities than BFBs.

    With coal, the net effect per cross section area of the furnace can be as high as 6 MW/m

    (Hyppnen and Raiko /7/). The main benefits of a CFB boiler over the bubbling fluidized bed

    are in the solids recirculation. The solids circulation allows very uniform temperaturedistribution in the boiler furnace, while in a bubbling fluidized bed the combustion takes

    place partially in the gas above the bed region, and the gas temperature in the furnace can be

    over 1200 C. This may lead to high NOx emissions (Pels et al. /9/). Due to the solids

    recirculation, the combustion efficiency of coal in CFB boilers is higher than in BFB boilers.

    The novel Foster Wheeler CFB design utilizes a Compact separator, in which the walls of the

    solids separator are straight, see Figures 6 and 7.

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    Figure 6. A modern Foster Wheeler Compact boiler with straight walls in the solidsseparators (Foster Wheeler Energia Oy, /5/).

    Figure 7. Operating principle of Compact separators of the Foster Wheeler Compact

    boiler (Foster Wheeler Energia Oy, /5/).

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    The solids recirculation allows effective sulphur capture by using limestone as an additive in

    the bed material. According to Leckner and Amand /10/, and Newby and Dale /11/, the

    optimum temperature range for sulphur capture with limestone is between 800 and 850 C, so

    if the temperature in the furnace can be maintained within these limits, the reaction of SO2 to

    CaSO4 can take place in the whole furnace instead of a small region.

    In order that the solids separator could operate properly, no gas backflow through the solids

    return line can take place (EPRI /12/). The pressure barrier for preventing the gas upflow is

    created by a gas seal, the operating principle of a gas seal is shown in Figure 8. The gas seal

    area can be efficiently utilized as a location for external heat exchangers.

    Figure 8. Operating principle of the gas seal for CFB boilers (Hill, Mallory andMcKinsey /13/, page 865).

    The circulating solids increase the heat transfer rate in the furnace, but the risk of erosion

    caused by the bed material increases. Together with water wall corrosion this may cause very

    rapid material loss in the furnace. This is one of the risks which have to be noticed when

    designing a CFB boiler for fuels containing corrosive components.

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    The superheaters in a CFB boiler can be located after the solids separator as convective heat

    exchangers, or inside the furnace as Omega panels, which are exposed to both radiative and

    convective heat transfer. The heat transfer rate in the furnace is much higher than in the

    convective pass due to radiation and particle convection (Basu /14/). The rate of erosion in

    the Omega panel is higher, so the panel edges have to be protected from vertical solids flow.

    The circulating fluidized bed combustion represents relatively new technology. However, an

    increasing percentage of the new boilers constructed all over the world are based on

    circulating fluidized bed. The largest circulating fluidized bed boiler so far is the Gardanne

    coal combustion unit in Provence, France. The thermal input of the boiler is 600 MW, and the

    steam values produced by the unit are 163 bar and 565 C (Jestin et al. /15/).

    4.0 SUPERHEATER FIRESIDE CORROSION

    Fireside corrosion is often a very complicated process. Superheater metal wastage can occur

    due to oxidation, sulphidation, erosion-corrosion, or a combination of these. Chlorine

    containing deposits can also be the main cause of metal loss. In order to evaluate the risk of

    superheater fireside corrosion, one has to be aware of the main corrosion types and their

    effects. The risk evaluation is often done by examining the fuel characteristics, but the role of

    bed material and combustor type must not be neglected (Jia-Sheng /16/).

    Many corrosion types are speeded up by a reducing gas environment. Different corrosion

    types have been widely studied, the first papers on the subject date back to the end of the

    1800s (Evans /17/). Quite often the corrosion taking place is a mixture of at least two

    different corrosion mechanisms. This makes it difficult to predict the corrosion rate and

    material lifetime in the superheater region. There are various studies of the corrosion

    kinetics, both in laboratory and field scale, but few of these can directly be used for

    estimation of superheater material endurance.

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    4.1 Mechanism and Kinetics of Oxidation

    The most common metal degradation mechanism encountered in combustion is oxidation.Oxidation takes place when the oxygen in the flue gas gets in contact with the superheater

    steel surface. Due to the need for elevated strength at high temperatures, the steels used in

    superheaters are often chromium and/or nickel containing alloys. The oxidation of the alloy

    surface creates a protective iron or chromium oxide layer which prevents diffusion of oxygen.

    Because of the decreased diffusion, the oxidation rate slows down, and a stable oxide layer is

    formed on the steel surface. This type of oxidation is called selective, and the mechanism is

    widely used for protection against corrosion. However, if the protective layer is not sufficientfor preventing diffusion, the rate of oxidation can be linear or even exponential, ending up

    with a very high metal loss rate. The different oxidation equations have been presented by

    Fontana and Greene /18/. The equations are presented graphically in Figure 9.

    Parabolic oxidation

    q k t C m p p2 = + (6)

    where qm weight gain per surface area

    kp parabolic rate constant

    t time

    Cp parabolic constant.

    Logarithmic oxidation

    q k C t Am o o= +log( ) (7)

    where ko logarithmic rate constant

    Co logarithmic constant

    A constant.

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    Linear oxidation

    q k t C m l l= + (8)

    where kl linear rate constant

    Cl linear constant.

    Cubic oxidation

    q k t C m c c3 = + (9)

    where kc cubic rate constantCc cubic constant.

    Paralinear oxidation

    q k q k t C m pl m p pl 2 + = + (10)

    where kpl paralinear rate constant

    Cpl paralinear constant.

    The oxidation rate constants are usually reported to depend on the temperature according to

    the Arrhenius expression

    k f eE

    RT

    A

    =

    (11)

    where f frequency factor

    EA activation energy

    R molar gas constant

    T temperature.

    Oxidation can seldom be presented with only one equation, combinations of rate laws are

    common. The oxidation rate constants depend on metal, temperature, and diffusion

    coefficient of oxygen through the oxide layer. If the diffusion rate through the oxide layer is

    low, the oxide forms a protection against further oxidation. This protective oxide

    layer can also protect the metal from chemical corrosion caused by corrosive contaminants.

    Some parabolic oxidation and sulphidation rate constants are listed in Table 1.

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    0.0

    0.2

    0.4

    0.6

    0.8

    1.0

    1.2

    1.4

    1.6

    1.8

    2.0

    0 0.5 1 1.5 2

    TIME

    qm,g/m

    parabolic logaritmic linear

    cubic

    Figure 9. Oxidation equations, weight gain per unit area.

    TABLE 1. Oxidation and sulphidation rate constants for parabolic oxidation andsulphidation (Mrowec /19/, page 373).

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    Oxidation can become a problem if the metal temperature rises too high, or there are

    corrosive contaminants present in the gas atmosphere. These contaminants (sulphur,

    chlorine, fluorine, vanadium) can generate very high oxidation and metal wastage rates

    (Birks and Meier /20/).

    4.2 Sulphur Corrosion

    Sulphur corrosion, often referred to as sulphidation, is a very common phenomenon in coal

    combustion, but it can also occur with other fuels. In the combustion process, the sulphur in

    fuel reacts with oxygen in the combustion air forming SO2, and, if the residence time and O2

    content are sufficient, also SO3. The SO2 and SO3 can react with calcium in the bed material

    forming CaSO4, but other sulphides and sulphates are also possible. The formed sulphides

    and sulphates may deposit on a superheater surface, after which the corrosion takes place on

    the interface between the metal oxide and the deposit.

    4.2.1 Formation of SO3 in Fluidized Bed Combustion

    The formation of SO3 in combustion systems can be presented with the following

    mechanism:

    S O SO

    SO O SO O

    M SO O SO M

    2 2

    2 2 3

    2 3

    +

    + +

    + + +

    k

    k

    k

    k

    1

    1

    2

    2

    (12)

    where k1, k2 rate constants

    M metal catalyst.

    The formation of SO3 can be accelerated by various catalysts, but the maximum SO3

    concentration is always limited by the thermodynamical equilibrium. If reaction (12) takes

    place without a catalyst, Burdett et al. /21/ have measured for the rate constant a value of

    (2.61.3)1012 e((-230001200)/T) mol-1cm3s-1 at a temperature range of 900-1350 K.

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    Burdett et al. /22/ have studied the effect of bed material on the SO3 formation in combustion

    of coal in the fluidized bed. A major fraction of the SO2 can be captured by adding limestone

    to the fluidized bed, but despite the reduced SO2 level, some SO3 can still be found in the flue

    gas. They reported that approximately 1.5 % of the total sulphur oxide in the flue gas is

    present as SO3. This free SO3 can be considered as the main initiator of superheater metal

    sulphidation.

    4.2.2 Kinetics of Sulphidation

    Metal sulphidation is a much faster reaction than metal oxidation, see Table 1. However, thereaction seldom continues so as to result in internal sulphidation of the metal. By Mrowec

    /19/, the reasons for this can be listed as follows:

    solubility of sulphur into most metals is low diffusion of sulphur in metals is slow due to high sulphidation rate, the sulphur does not have time to penetrate into the

    metallic phase.

    Sulphidation does not normally create similar protective layer as oxidation. The formed layerhas a more pronounced defect structure in the sulphide lattice. The formed sulphide can be

    scaled off more easily, leaving the base metal vulnerable to further oxidation and

    sulphidation. The sulphidation rate depends on temperature according to the Arrhenius

    expression, equation (11). Some activation energies for sulphidation are shown in Table 2.

    TABLE 2. Activation energies of sulphidation of Fe, Ni, Cr and alloys (Mrowec /19/,page 379).

    Metal / alloy Temperature Activation Activation energy, kJ/molrange, C energy, kJ/mol Range I Range II Range III

    Fe 600-950 80 - - -Ni 450-620 88 - - -Cr 600-1100 130 - - -Fe-Cr 700-1000 - 80-110 230-310 130

    Ni-Cr 520-900 - 88-92 170-280 120

    In Table 2, ranges I, II, and III correspond to low-percent, intermediate-percent and high-

    percent alloys. It can be noted that the activation energies are at highest in range II. This is

    due to the heterogeneous double-layer scale formed in the sulphidation reaction.

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    4.2.3 Sulphur Corrosion Mechanisms

    The sulphur corrosion is hardly ever due to diffusion of SO2 or SO3 from the flue gas straightto the tube surface, unless the concentrations are very high. In most cases the diffusion occurs

    from the flue gas to the deposit and then from the deposit to the metal oxide.

    The sulphur corrosion mechanism depends on the composition of the sulphur containing

    component and gas atmosphere. The sulphur corrosion mechanism can occur in reducing

    environment, where the reaction products are sulphides, or in oxidizing environment, where

    the corrosion products are sulphates. The reducing environment can be caused by formationof H2 or CO in the combustion process due to undesired chemical reactions. The reason for

    the high CO content in the flue gas is in many cases in the unstable fuel feeding, which

    occasionally causes the air to fuel ratio to be lower than unity.

    4.2.4 Sulphide Corrosion

    Sulphide corrosion occurs in reducing environments. According to Plumley et al. /23/,

    sulphides are formed from alkali metals and sulphur oxides in the presence of CO as follows:

    Na SO CO Na SO CO

    6Na SO 4Na SO 2Na S O

    Na S MeO MeS (Na O)

    2 4 2 3 2

    2 3 2 4 2 2

    2 2

    + + + +

    + +(13)

    where Me Cr, Fe, Ni...

    Reaction (13) produces MeS, which can react further to volatile compositions, resulting in

    high metal wastage rates. For iron, the initiation of this reaction can occur by reduction of

    Fe2O3 to Fe3O4 by CO, while FeO is formed as a side product.

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    4.2.5 Sulphate Corrosion

    Sulphate corrosion can take place in oxidizing environment, when the SO3 concentration ishigh. At a temperature range from 320 to 480 C, the alkali sulphates form pyrosulphates

    Na SO SO Na S O2 4 3 2 2 7+ (14)

    which decompose and react with the protective oxide layer, an example for iron oxide:

    ( )( )

    Na S O Fe O 2Na Fe SO

    2Na Fe SO 3Na SO Fe O 3SO

    2 2 7 2 3 3 4 3

    3 4 3 2 4 2 3 3

    + + +

    (15)

    resulting in degradation of the protective oxide layer. The reaction (15) accelerates when the

    temperature is increased over 490 C (Barniske /24/). The reaction mechanism can also

    involve alkali trisulphates, which are formed according to equation (16) (Reid /25/).

    3Na SO Fe O 3SO 2Na Fe(SO )2 4 2 3 3 3 4 3+ + (16)

    It may be noted that in equations (14) to (16), potassium can be used instead of sodium.

    4.3 Chlorine Corrosion

    Chlorine induced corrosion is a common corrosion type in waste combustion. This type ofcorrosion can also take place in combustion of other chlorine containing fuels, for example

    biofuels and high-chlorine coals. Chlorine corrosion is often accelerated by alkaline

    components in the fuel. At low temperatures, chlorine corrosion can take place as

    hydrochloric acid corrosion, but in the case of superheater corrosion, the mechanism is

    initiated when the fuel contains sufficient amounts of chlorine, and the superheater tube

    temperature is sufficient for chlorides to form molten eutectics. If there are sulphur containing

    components present, the chlorine corrosion can cause very high metal loss rates.

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    The highest superheater tube degradation rates reported have been about 3 mm/1000 hours of

    operation. Chlorine corrosion has been mostly studied in connection with waste combustion.

    4.3.1 Chlorine Corrosion Mechanisms

    Chlorine corrosion can take place as gas phase corrosion (Hupa et al. /26/) in which the HCl

    in the flue gas gets in contact with unprotected metal, for example iron. The formed Fe (II)

    chloride reacts further to Fe (III) chloride, which is very volatile. The Fe (III) chloride is

    vaporized, leaving the surface vulnerable to further reactions.

    Fe(s) 2HCl(g) FeCl (s) H (g)2 2+ + (17)

    Fe(s) 3HCl(g) FeCl (s, volatile) H (g)332 2+ + (18)

    This mechanism is illustrated in Figure 10. In waste combustion, the mechanism is often

    more complicated due to the high CO and H2O content in the flue gas.

    Figure 10. Corrosion of iron due to high HCl concentration in waste incineration(Nieminen /27/, page 57).

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    Chlorine corrosion has been widely studied, and some corrosion mechanisms have been

    proposed, one of the most widely agreed on is the mechanism presented by Krause (/28/ and

    /29/). These reactions take place in the region between the deposit and the metal oxide,

    Figures 11 and 12. The reactions in chlorine corrosion mechanism in Figures 11 and 12 take

    place in the molten phase, which explains the high metal wastage rates. The liquid phase is

    formed of salts with low melting points, some of which are listed in Tables 3 and 4.

    Figure 11. Chlorine and sulphur corrosion mechanisms, cyclic corrosion (Krause /28/,page 91).

    Figure 12. Different scales on top of superheater tube and their compositions, chlorinecorrosion (Krause /28/, page 89).

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    TABLE 3. Common salts with low melting points (Elliot /30/, page 65).

    Composition Meltingpoint C

    Composition Meltingpoint C

    NaVO3 630 Na2S2O7 400

    CrVO4 810 K 2S2O7 335

    FeVO4 816 3K 2S2O7 Na2S2O7 280

    MgV2O6 700 Na3Fe(SO4)3 624

    CoV2O6 705 K 3Fe(SO4)3 618

    NiV2O6 720 Na3Fe(SO4)3 K3Fe(SO4)3 552

    V2O4 >1750 Na3Al(SO4)3 646

    V2O3 690 K 3Al(SO4)3 655Na2OV2O5 630 ZnSO4 decomp. 740

    Na2O3V2O5 621 Na2SO4 884

    2Na2O3V2O5 620 K 2SO4 1076

    2Na2OV2O5 640 MgSO decomp. 1124

    3Na2O3V2O5 850 Al2(SO)3 decomp. =>Al2O3

    770

    10Na2O7V2O5 573 CaSO4 1450

    Na2OV2O45V2O5 625 Fe2(SO4)3 decomp. =>Fe2O3

    480

    5Na2OV2O411V2O5 535 NiSO4 decomp. => NiO 783

    2MgOV2O5 835 MoCl5 194

    3MgOV2O5 1190 NbCl5 205

    Na2O/MoO3 550 FeCl3 282

    Ni-Ni3S2 645 Na2SO4-NaCl eutectic 625

    FeO-FeS eutectic 940 NaCl 800

    Fe-FeS eutectic 988 KCl 776

    Co-CoS 877 CaCl2 772

    NaHSO4 decomp. =>Na2S2O7 + H2O

    250 CrCl2 820

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    TABLE 4. Eutectic chloride mixtures with low melting points (Krause /29/, table 2).

    Composition Melting point

    mol % mol % F C

    25 NaCl 75 FeCl3 313 156

    37 PbCl2 63 FeCl3 347 175

    60 SnCl2 40 KC1 349 176

    70 SnCl2 30 NaCl 361 183

    70 ZnCl2 30 FeCl3 392 200

    20 ZnCl2 80 SnCl2 400 204

    55 ZnCl2 45 KC1 446 230

    70 ZnCl2 30 NaCl 504 262

    60 KC1 40 FeCl2 671 355

    58 NaCl 42 FeCl2 698 370

    70 PbCl2 30 NaCl 770 410

    52 PbCl2 48 KC1 772 411

    72 PbCl2 28 FeCl2 790 421

    90 PbCl2 10 MgC12 860 460

    80 PbCl2 20 CaCl2 887 475

    49 NaCl 51 CaCl2 932 500

    4.3.2 Kinetics of Chlorine Corrosion

    The chlorine corrosion rate depends on the chlorine concentration in the flue gas, tube

    metal temperature, and tube steel composition (Brooks and Meadowcroft /31/). It has been

    reported that the increase in chlorine concentration at low temperatures accelerates corrosion

    vastly. When the temperature is increased, the role of concentration decreases, and the

    temperature increase becomes a more important factor. Around a metal temperature of

    500 C, a temperature increase of 15 C approximately doubles the chloride corrosion rate.

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    4.4 Other Corrosion Types

    In addition to the corrosion mechanisms described earlier, there are several other corrosion

    types mentioned in the literature, including vanadium corrosion and erosion-corrosion. These

    mechanisms alone can seldom create rapid material losses, but together with oxidation and/or

    sulphidation, they can seriously accelerate the material wastage.

    4.4.1 Vanadium Corrosion

    Vanadium, and especially vanadium pentoxide (V2O5) can cause a fast increase in thematerial loss rate in sulphur corrosion environment. The role of vanadium in this corrosion

    case is not fully agreed on: some researchers claim that the vanadium pentoxide acts as a

    catalyst, while others think that the role of vanadium is more complex (Evans /33/). Table 3

    presents several vanadium containing salts with low melting points. However, significant

    concentrations of these salts require high amounts of vanadium in the fuel. Some petroleum

    cokes and refinery tars may contain sufficient amounts of vanadium for the corrosion attack

    to initiate.

    4.4.2 Erosion-Corrosion

    In erosion-corrosion, the protective oxide layer, or the created corrosion product, is removed

    from the tube surface by erosion. The erosion is due to particles colliding with the metal

    surface. The erosion rate depends on the particle velocity and characteristics, as well as the

    metal surface properties (Barkalow, Goebel and Pettit /34/).

    The erosion-corrosion is considered to be the main reason for superheater material wastage in

    the vicinity of soot-blowers. The erosion-corrosion rates in the furnace are often high, and for

    this reason the lower edges of the Omega panels used in CFB boilers have to be protected

    with shields.

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    4.4.3 Corrosion in the Soot-Blower Region

    In the case of steam soot-blowers, the corrosion in the soot-blower area can be due to erosion-corrosion. On the other hand, the corrosion can also take place due to the highly reducing

    environment. The reason for the low-oxygen zone may be in the water gas reaction

    C(s) H O(g) CO(g) H (g)2 2+ + (20)

    which can occur if there exists unburned carbon on the tube surface, and the carbon gets in

    contact with steam or water spray from the steam soot-blowers. The resulting CO and H2

    react rapidly with the O2 in the flue gas, resulting in low-oxygen atmosphere. Reaction (20) is

    very rapid when the temperature is over 650 C.

    4.5 Fouling and Deposit Formation

    When solid fuel is combusted in a fluidized bed, some ash will be carried with the flue gas

    out from the furnace. Part of the ash can deposit inside the furnace, and cause an event called

    slagging. If the ash is deposited in the flue gas duct after the furnace, the phenomenon is

    called fouling. In most cases, the superheaters are located outside the furnace, and fouling

    is the main concern connected with the reduced heat transfer rate in the superheater region. If

    the ash contains harmful impurities, the previously mentioned corrosion mechanisms can take

    place in the superheater tubes. In order to remove the deposit from the superheater tubes,

    different soot removal systems have been developed. Some of the commercial soot removal

    apparatus will be described later in the context of superheater design.

    Deposit formation is a key factor in the study of sulphur and chlorine corrosion, some

    existing theories and studies of deposit formation are described in this chapter. The deposits

    which are formed on the surface of a superheater are often considered to be the main initiator

    of a fireside corrosion attack.

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    The main deposit formation mechanisms are:

    particle impaction condensation

    thermoporesis diffusion and chemical reactions.

    One or more of these mechanisms can cause superheater fouling and thus initiate a corrosion

    attack. One of the worst properties of the components that cause fouling is that they are often

    also corrosive. By Miles et al. /35/, the compounds containing potassium, sulphur and

    chlorine are often the initial bonding agents. Jokiniemi and Kauppinen /36/ have listed the

    fouling meachanisms and the size of gas component as follows:

    fumes and gases => condensation and surface reactions aerosols => diffusion, thermophoresis small particles, 0.05 - 0.5 m => vortex impaction coarse particles, 0.5 - 50 m => direct impaction.

    In general, the tube temperature is the main factor influencing the fouling rate of gases,

    aerosols and small particles. When the particle size increases, the flue gas velocity starts to

    increasingly affect the deposit formation rate.

    4.5.1 Particle Impaction

    Fouling in the superheater tubes can be caused by particle impaction, so that the deposit is

    formed by particles of size 0.5 - 50 m, which due to inertia cannot follow the flue gas

    flow but hit the surface with high velocity, and get deposited on the surface, Figure 14.

    These particles can be partially condensed aerosols, molten salts, or silicates. The deposits

    formed by direct impaction can be found on the surface facing the gas flow. Smaller

    particles may collide with the sides and back of the surfaces due to gas vortex impaction.

    These deposits are normally less dense than the deposits formed by direct impaction.

    However, chemical reactions in the deposit can create very hard scales which cannot be

    removed with soot removal systems.

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    Figure 14. Difference between fouling by diffusion and impaction (Skrifars and Hupa/37/, page 216).

    4.5.2 Condensation

    Gas and fume condensation intensity on the surfaces depends mostly on three factors:

    surface temperature, gas temperature, and partial pressure of vapour. An example of the

    temperature effect can be seen in Figure 15, where dew points and condensing intensities

    of four substances are shown. It can be noted that KCl can condensate at as high a surface

    temperature as 650 C, which corresponds to a live steam temperature of approximately620 C.

    Figure 15. Condensing intensity and dew points of selected substances (Ots /38/, Figure

    6.).

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    In order to minimize the condensation rate, the flue gas temperature should be reduced to

    minimize the temperature difference between gas and the heat exchange surface. In waste

    combustion, 700 C is considered to be a critical flue gas temperature. The partial pressures

    of gas components are important as regards the condensation characteristics, because the

    dew point of a gas component depends on the partial pressure of the component.

    4.5.3 Thermophoresis

    Thermophoresis is the term describing the phenomenon wherein small particles, such as

    soot particles, aerosols and the like, when suspended in gas in which there exists atemperature gradient T, experience a force in the direction opposite to that of T(Talbot

    et al. /39/). This force can correspond to the deposition of superheater tubes, because in

    such a case, the direction of T is always from the tube surface to flue gas. The

    thermophoretic velocity can be presented by an empirical formula

    UT

    T

    Cr

    Cr

    Tx

    g

    pt

    g

    p

    t

    = +

    + +

    2 2

    1 2 20

    . ( )

    (21)

    where UT thermophoretic velocity

    kinematic viscosity

    T0 reference temperature

    g thermal conductivity of gas

    p thermal conductivity of particle

    Ct correction factor

    mean free path

    r radius of particle.

    The equation (21) was first presented by Derjaguin et al. /40/. This equation, although

    empirical, can be used for estimating the particle velocities caused by the thermophoreticforce in the vicinity of superheater tubes.

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    4.5.4 Chemical Reactions

    Chemical reactions seldom are the only fouling mechanism. However, after the initial deposithas been formed by one of the primary fouling mechanisms, the deposit can react with the

    flue gas components. This may lead to a vast change in the initial deposit composition. As an

    example, Ots /38/ has presented a figure illustrating the Cl and SO3 content change depending

    on time in the combustion of Estonian oil shale, Figure 13.

    Figure 16. Cl and SO3 content change in ash deposits from combustion of Estonian oilshale (Ots /38/, Figure 7.).

    The change is mainly due to transformation of Cl-containing components to sulphates by

    chemical reactions, and most of the released chlorine is vaporized as alkali chlorides. The

    formed sulphates (K2SO4, CaSO4) act as binding agents, forming a dense and hard deposit.

    The problem with the release of chlorine lies in the tendency of chlorine to form iron and

    chromium chlorides. This mechanism may lead to a rapid chlorine corrosion.

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    4.6 Measurement of High-Temperature Corrosion

    Several high-temperature corrosion measurement methods have been developed to be used ina laboratory scale (Haynes and Baboian /41/). However, few of these give results which could

    be directly applied to field use. The problem lies in the changing flue gas atmosphere, which

    is very hard to create in a laboratory. Results achieved in metal tests at laboratories can only

    be used with caution for material selection, and that is why field tests are considered to be the

    only reliable method for achieving a basis for material lifetime estimation.

    The superheater corrosion can be evaluated directly by measuring the material loss on a timebasis. This, however, requires a boiler shutdown. The other ways of measuring the corrosion

    rate and determining the corrosion mechanism are more or less indirect. The corrosion rate

    can be evaluated by measuring the material loss by using a special probe. The corrosion probe

    can be of a type that utilizes electrical phenomenon taking place in a corrosion attack (Barrett

    /42/), or a type which contains several cooled coupons which are weighed and analysed. The

    principle of a lance-type corrosion probe is shown in Figure 17. The probe can be cooled with

    air or air and water. The coupon temperatures are measured according to demand, and thetemperature can be adjusted with the cooling fluid flow. A probe of this type has been

    successfully used at the Foster Wheeler Karhula R&D Center for several years, and the

    majority of the test results referred to in the experimental part have been measured by using

    such a probe.

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    AIR IN

    STEEL COUPONS

    AIR OUT

    TI TI

    50 mm

    38 mm 28 mm

    Figure 17. Lance-type corrosion probe and a test coupon.

    5.0 EFFECT OF FUEL CHARACTERISTICS

    Fuel characteristics and composition have a major role in the superheater corrosion. The

    amount of corrosive components and other harmful impurities is one of the main factors

    related to the corrosion behaviour of a particular fuel. The task of predicting the corrosion

    behaviour becomes more difficult when two or more fuels are combusted as co-combustion.

    Several aspects have to be taken into account in the estimation of corrosion rate by the fuel

    characteristics. According to Miles et al. /34/, at least the following analyses have to be made

    in order to evaluate the fuel characteristics:

    proximate analysis (moisture, volatile matter, fixed carbon, ash) ultimate analysis (C, H, N, S, O, Cl...) heating value chlorine a direct measure of oxygen elemental ash analysis.

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    If it is possible, some special analyses should be made to evaluate the fuel ash behaviour.

    These include sintering tendency, water soluble and volatile alkali, and tendency to form

    chlorides and sulphides. The fuel analyses can be utilized for theoretical modelling of

    deposition (Jokiniemi et al. /43/) and modelling of corrosion. However, in order to achieve

    reliable results of corrosion rates by theoretical modelling, the calculations must be checked

    against known cases.

    5.1 Fuel Composition

    The fuel composition is important at the design state. The amounts of sulphur, chlorine,potassium, sodium, vanadium etc. have to be known prior to the final decision on the boiler

    type and superheater design. If, according to the fuel analyses, there seems to be a risk of

    corrosion, this risk has to be noted before any decisions on the final design are made. The

    following chapters list some fuels and their characteristics, some fuels with known corrosion

    risks have been highlighted. A database of fuel characteristics was constructed during the

    study, and this database was utilized as a basis for the corrosion model.

    5.1.1 Coals

    Coals are the most important solid fuels in energy production. Coal types can roughly be

    divided into five main gategories:

    anthracite bituminous

    lignite char brown coals.

    Different classification systems have been developed for coal quality, but the existing systems

    make it difficult to determine the coal qualities just by the main type. A comparison of

    different coal classification systems is shown in Figure 18. Some compositions of different

    coals are listed in Table 5.

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    Figure 18. Coal classification systems (Skorupska /44/, page 28).

    Table 5. Compositions of various coals, measured at the Karhula R&D Center ofFoster Wheeler Energia Oy. The statistics are from all samples analyzed.

    LHV Volatile Ash C H N O S Cl Ca Na, K Coal type MJ/kg w-% w-% w-% w-% w-% w-% w-% w-% w-% w-%Anthracite 29.7 18.2 10.1 77.7 3.0 1.4 6.9 1.0 0.0 0.2 0.0Bituminous 29.7 25.8 17.1 72.3 4.2 1.1 4.8 0.5 0.0 0.0 0.4

    Lignite 21.3 42.9 17.2 54.7 3.9 0.6 22.2 1.5 0.0 0.4 0.0Brown coal 19.4 36.4 30.6 47.2 4.0 0.7 16.1 1.4 0.0 0.0 0.0Oil shale 13.9 48.1 51.9 28.4 3.0 0.1 15.1 1.5 0.1 14.2 0.1Coal residue 19.5 6.7 41.4 53.7 1.2 0.7 2.4 0.8 0.0 0.0 0.0

    Min 6.4 5.7 4.0 16.8 0.6 0.3 0.7 0.3 0.0 0.1 0.0Average 25.3 32.0 19.4 62.3 4.1 1.2 11.2 1.3 0.1 0.7 0.3Max 31.8 52.9 79.1 77.7 5.7 2.1 23.6 5.7 0.5 10.7 1.9Stdev 5.3 9.6 14.3 13.0 0.9 0.4 5.1 1.1 0.1 1.6 0.3

    Moilanen and ijl /45/ have presented a large database of various coal types and their

    specifications, similar databases are also available from other sources.

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    5.1.2 Peat

    Peat is a very important fuel in Finland, because it is considered a renewable source of energy

    (Alakangas, Kanervirta and Kallio /46/), although the formation of peat takes thousands of

    years. The peat used for combustion contains considerable amounts of sulphur, so it has to be

    regarded as a corrosive fuel. However, the corrosion type encountered in combustion of peat

    is sulphur corrosion, which in most cases can be handled with superheater materials selection.

    In some cases cold-end chloride corrosion has been reported due to an occasionally high

    chlorine content of peat. Some compositions of different peats are listed in Table 6.

    Table 6. Compositions of various peats, measured at Karhula R&D Center of FosterWheeler Energia Oy. The statistics are from all samples analyzed.

    LHV Volatile Ash C H N O S Cl Ca Na, K Peat MJ/kg w-% w-% w-% w-% w-% w-% w-% w-% w-% w-%

    Type 1 19.4 65.4 5.1 53.2 5.2 1.9 34.0 0.6 0.0 0.0 0.1Type 2 20.8 65.1 8.0 51.6 5.5 2.1 32.6 0.2 0.0 0.4 0.2Type 3 23.3 67.9 4.9 56.2 6.0 2.1 30.6 0.2 0.0 0.0 0.0Type 4 22.0 68.3 4.4 53.7 6.0 2.8 32.9 0.2 0.0 0.0 0.0Type 5 21.8 69.3 3.7 54.4 5.8 1.7 34.3 0.2 0.0 0.0 0.0Type 6 22.1 59.9 3.0 58.0 4.7 1.5 32.6 0.2 0.1 0.0 0.1Type 7 23.1 64.7 1.1 58.9 5.4 0.7 33.8 0.1 0.0 0.0 0.0

    Min 19.0 59.9 1.1 48.7 4.7 0.7 29.4 0.1 0.0 0.1 0.0Average 21.7 68.1 4.4 54.4 5.7 1.8 33.2 0.2 0.0 0.4 0.1Max 23.8 74.6 8.0 58.9 6.4 2.8 39.2 0.7 0.1 0.8 0.2Stdev 1.3 3.3 1.6 2.2 0.4 0.5 2.8 0.1 0.0 0.2 0.1

    5.1.3 Biofuels

    Biofuels may be the most important renewable source of energy, in Finland about 15 % of

    primary energy is produced by combustion of bioenergy (Sipil, Kurkela and Solantausta

    /47/). However, because the heating value of biofuels is low compared to fossil fuels, fuel

    mass flow rates are high. Most biofuels contain potassium and chlorine, which together cause

    boiler fouling and corrosion in superheaters. In worst cases, combustion of biofuels is close to

    waste incineration at superheater corrosion rates, for example, one CFB for co-combustion of

    straw and coal has suffered from fast corrosion, not only in the hottest tubes but also on

    relatively cool heat transfer surfaces.

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    This is due to high chlorine content of the straw combusted: the worst samples have been

    detected to contain about 2 w % of chlorine. Presently, the chlorine content considered to be

    risky is 0.1 w-%. Some biofuel compositions are shown in Table 7.

    Table 7. Compositions of various biofuels, measured at Karhula R&D Center ofFoster Wheeler Energia Oy. The statistics are from all samples analyzed.

    LHV Volatile Ash C H N O S Cl Ca Na, K Fuel MJ/kg w-% w-% w-% w-% w-% w-% w-% w-% w-% w-%

    Wood 1 19.0 70.2 8.8 48.5 5.4 0.4 36.8 0.0 0.0 0.0 0.3Wood 2 20.4 78.0 1.7 51.1 6.2 0.5 40.5 0.0 0.0 0.0 0.0Wood 3 20.3 81.5 1.1 50.0 6.1 0.2 42.5 0.0 0.0 0.3 0.1Bark 1 18.0 77.4 3.7 46.3 5.5 0.3 43.9 0.0 0.2 0.0 0.3

    Bark 2 19.4 73.5 5.1 49.5 5.7 0.6 39.0 0.1 0.0 0.0 0.3Bagasse 18.8 73.2 7.2 47.4 5.5 0.6 39.2 0.1 0.1 0.0 0.4Nut shell 22.0 76.5 11.8 49.5 6.1 2.3 29.3 0.4 0.5 0.0 5.0Olive resi due 20.0 73.6 8.8 48.7 6.1 0.9 35.4 0.1 0.0 0.0 0.9

    Min 12.8 57.9 0.3 33.0 4.3 0.0 16.1 0.0 0.0 0.1 0.0Average 19.6 76.3 5.0 49.3 5.9 0.6 38.5 0.1 0.1 0.6 0.4Max 22.2 86.8 35.7 53.6 6.5 7.3 44.2 1.4 0.9 2.9 5.0Stdev 1.7 6.0 6.8 3.9 0.4 1.0 4.9 0.2 0.2 0.7 0.8

    5.1.4 Recycled Fuels

    Due to the increasing demand for package and waste recirculation, the recycled fuels, such as

    REF (Recycled Fuel) and RDF (Refuse Derived Fuel) have become important. Both of these

    fuel types have a relatively high heat value, and in some cases REF can be a very good fuel,

    for example pure polyethylene (Fire /48/). In most cases, though, there are varying amounts of

    impurities and harmful substances in the recycled fuels. These can create a very damaging

    environment in the superheater vicinity (Campbell et al. /49/). In most cases, the RDF and

    REF combustion require special measures for corrosion prevention. Some approximate

    elementary analyses of recycled fuels are listed in Table 8.

    In addition to the aforementioned recycled fuels, the wood processing industry has started to

    burn different sludges from waste-water treatment in utility boilers. These sludges may

    contain high amounts of alkalis and chlorides, thus even low fractions of these sludges in

    combustion may cause fouling and chloride induced corrosion in the superheater region.

    Analyses of several sludges are also included in Table 8.

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    Table 8. Compositions of various recycled fuels, measured at Karhula R&D Centerof Foster Wheeler Energia Oy. The statistics are from all samples analyzed.

    LHV Volatile Ash C H N O S Cl Ca Na, K Fuel type MJ/kg w-% w-% w-% w-% w-% w-% w-% w-% w-% w-%

    Sludge 1 22.7 72.2 18.1 49.8 6.8 0.3 23.6 0.1 1.3 0.0 0.2Sludge 2 18.2 76.8 7.1 44.9 5.8 1.7 39.5 0.8 0.2 0.6 0.0Sludge 3 13.5 84.4 32.9 33.0 4.4 0.3 29.3 0.1 0.0 0.0 0.0Waste 1 15.1 62.1 27.9 36.4 4.8 0.9 29.5 0.2 0.5 0.0 0.0Waste 2 20.7 74.8 6.4 50.4 6.2 1.1 35.6 0.1 0.1 0.0 1.3Waste 3 34.3 88.3 9.2 65.0 10.4 0.1 15.2 0.1 0.0 0.0 0.0Waste 4 41.0 93.7 1.6 75.0 12.0 0.7 9.4 0.1 1.3 0.0 0.1

    Min 3.2 17.0 1.1 5.7 2.3 0.1 2.0 0.0 0.0 0.6 0.0Average 19.4 70.3 18.7 44.7 5.9 1.1 27.6 0.3 0.6 4.1 0.5Max 41.0 93.7 76.5 75.0 12.0 6.6 43.0 1.4 9.4 9.7 2.8Stdev 6.6 14.7 16.3 11.7 1.8 1.3 9.4 0.3 1.4 3.2 0.6

    5.2 Fuel Feeding

    Together with the fuel characteristics, the importance of the fuel feeding has to be noted. If

    the fuel feeding system can provide the boiler with a constant, homogenous fuel flow, the

    combustion will be stable, and variations in the flue gas composition will be minimized

    (Andrews et al. /50/). As mentioned earlier in the Chapter 4.2.1 about sulphur corrosion,

    fluctuation in the fuel feeding can cause a high CO content in the flue gas, thus increasing the

    corrosion rate. The effect of fuel feeding on corrosion in fluidized bed combustion has not

    been extensively studied.

    It is also possible to create reducing combustion zones in the furnace by feeding the fuel only

    into one side of the boiler. This situation, especially in BFB boilers, can with very poor fuel

    mixing in the furnace, result in one-sided combustion, i.e. one side of the furnace is gasifying

    and the other is combusting with excessive air. In the worst case, this will cause afterburning

    in the superheater section. This afterburning can increase the tube material and deposit

    temperatures, causing an accelerated corrosion rate.

    In the CFB boilers, the fuel can be fed into the solids return. This allows the fuel to be dried

    prior to entering the furnace region. The fuel will also be mixed with the hot recirculating

    solids, which in turn will be further mixed in the furnace.

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    6.0 EFFECT OF BED MATERIAL

    The bed material, both in the BFB and the CFB, can have a major effect on fouling and

    hence on the superheater corrosion. In a fluidized bed boiler, the bed material has three main

    roles:

    heat storage temperature stabilator catalyst or sorbent.

    The most often used bed materials are silica based sands, and limestones. Limestones are

    mainly used for sulphur capture in CFB boilers. Different sorbents and additives can be added

    for alkali metal capture and emission control (Ylipahkala and Orjala /51/).

    Kaolin based sands are used when the potassium content of the fuel used is high, because

    potassium and silica can form compositions with low melting points. It has been reported by

    Baxter et al. /52/ that in the case of high-potassium fuels, the silica based sands will adsorb

    the potassium and react by forming potassium silicates with low sticky temperatures. This

    will lead to sintering in the bed. If such fuel is used, the bed has to be regenerated sufficiently

    by introducing new bed material and simultaneously taking out the used bed.

    Evans /32/ and Keairns et al. /53/ have referred to several tests with magnesia (MgO) based

    bed materials. These tests point out that the magnesia can reduce fouling and corrosion. The

    problem is that MgO is more expensive than silica based sand.

    Other bed materials such as aluminium oxide have also been tested, but the problem always

    seems to be the costs: the materials are expensive, and their consumption is high. Some of

    these materials are also very erosive. Table 9 shows some analysis results of different

    sorbents. Table 10 contains information about different sands used as the bed material.

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    Table 9. Compositions of sorbents, measured at Karhula R&D Center of FosterWheeler Energia Oy. The statistics are from all samples analyzed.

    Ca Mg C S

    Sorbent w-% w-% w-% w-%Limestone 1 37.3 0.5 11.5 0.0Limestone 2 36.3 0.8 11.1 0.0Dolomite 1 11.6 6.1 5.4 0.3Dolomite 2 20.8 11.5 12.0 0.0

    Min 11.6 0.1 0.3 0.0Average 36.3 1.3 10.9 0.1Max 69.4 12.0 12.8 3.2Stdev 5.5 2.3 1.8 0.2

    Table 10. Compositions of sands, measured at Karhula R&D Center of Foster WheelerEnergia Oy. The statistics are from all samples analyzed.

    Al Ca Fe K Mg Na S Siw-% w-% w-% w-% w-% w-% w-% w-%

    Sand 1 5.6 1.2 1.9 1.7 0.7 2.3 0.0 34.6Sand 2 7.1 1.2 2.0 2.4 0.4 1.5 0.0 35.3Sand 3 1.5 0.4 0.3 1.0 0.1 0.4 0.1 43.5Sand 4 3.1 9.5 0.8 2.1 1.4 0.8 0.0 33.4

    Min 0.0 0.0 0.3 0.1 0.0 0.0 0.0 22.1Average 3.7 5.3 1.2 1.9 0.5 0.9 0.0 50.4

    Max 9.3 30.1 2.5 4.0 1.4 2.3 0.1 98.8Stdev 3.0 9.1 0.8 1.2 0.4 0.7 0.0 24.5

    7.0 EFFECT OF TEMPERATURES

    Flue gas temperature and tube material temperature are very important parameters in

    superheater fireside corrosion, because they directly affect the corrosion rate. The combustion

    temperature and bed material temperature also affect the corrosion rate, but the mechanisms

    are more indirect.

    7.1 Flue Gas Temperature

    The flue gas temperature acts in two ways:

    The higher the flue gas temperature near the superheater is, the higher the tubematerial temperature will be. The higher the flue gas temperature near the superheater is, the more partially

    molten particles there will be in the flue gas.

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    The effect of flue gas temperature has been illustrated by curves created by VDI /54/, an

    example is shown in Figure 19. These curves have been developed for coal combustion, but

    they are also useful for combustion of biofuels. The curves show an increase in the corrosion

    rate as the flue gas temperature increases . The increase in the corrosion rate can be explained

    by the fact that the higher the flue gas temperature is, the greater the risk of fouling will be.

    The formed deposit will then contain more corrosive components, and the risk of metal

    corrosion will be increased.

    Figure 19. Dependence of corrosion rate on the flue gas temperature (VDI /54/).

    7.2 Tube Material Temperature

    The tube material temperature is a very important design parameter in connection with

    fireside corrosion. The tube material temperature depends on several aspects:

    steam temperature steam flow tube material heat transfer rate flue gas temperature.

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    An increase in the tube material temperature will often cause two things:

    The material endurance will decrease. The corrosion reaction rate will increase.

    In most cases, the corrosion rate will increase fast, if the metal temperature is increased. The

    corrosion rate can be presented as a curve showing material loss versus temperature. An

    example curve is shown in Figure 20. The corrosion rate can also be shown as decrease in

    thickness per time, i.e. mm/h. The corrosion rate depends on the steel used for the

    superheater. The oxidation rate of the steel will often increase if the temperature is increased.

    Together with the increased corrosion reaction rate the material loss may increase

    exponentially.

    Figure 20. Corrosion rate of selected steels versus metal temperature (Sumitomo MetalIndustries, Ltd. /55/).

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    7.3 Combustion Temperature

    The combustion temperatures, and especially in BFB boilers, the freeboard temperature of theflue gas are very important as regards the alkali and chlorine release. It has been reported by

    Baxter et al. /52/ that the alkali release increases greatly when the combustion temperature is

    increased. The sulphur capture rate will be reduced when the combustion temperature is

    increased over 900 C, resulting in high SO2 and SO3 concentrations in the flue gas.

    7.4 Bed Material Temperature

    The bed material temperature affects two mechanisms:

    fuel decomposition bed material decomposition

    The bed material decomposition becomes a problem, if the bed is used for sulphur or alkali

    capture, because the captured components may be released in the process, resulting in the

    aforementioned problems.

    8.0 EFFECT OF SUPERHEATER DESIGN

    The superheater design and placement affects the corrosion rate. Often the affect of the design

    will be related to the temperature, but in some cases the flue gas velocity in the superheater

    area will contribute to the corrosion, especially in the case of erosion-corrosion. The

    superheaters should be designed so that fouling could be minimized, but the need for

    sufficient heat effect often sets the boundary criteria for the location.

    There are some ways of preventing corrosion of the superheater by the superheater lay-out.

    The superheater tubes can be partially protected by shields; these are used especially in the

    soot-blowing region to protect the first tube rows from the erosion caused by steam. The

    superheater tubes can also be protected by metal coatings, but unfortunately, most of the

    commercially available coatings are still at development stage.

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    8.1 Tube Heat Exchangers

    The tube heat exchangers are the most common type of superheaters. The tubes can bevertically or horizontally aligned, supported or hanging bundles, through which the steam

    flows. The tubes can also be welded to plate-like constructions. Tube-type superheaters can

    be located in the furnace (in BFB boilers) or in the back pass. The tube configuration can be

    linear or staggered, and the distance between the tubes can be used as a design parameter: the

    denser the bundle is, the more vulnerable to fouling it will be. The tube diameter and wall

    thickness can be selected according to the need. Some oxidation and corrosion allowance

    must be included in the wall thickness.

    The tube superheaters are often fouled according to Figure 21. The corrosion is usually at

    worst between 30 and 90 of the gas-particulate streamline. This is due to several reasons.

    The deposit is formed on the front and back sides of the tube, and hence the heat transfer

    through the deposit and the tube wall creates higher temperatures in the area where the

    deposit thickness is the lowest. If there are molten salts or eutectic mixtures in the deposit, the

    molten phase will diffuse in the high temperature region. Also, the volatile corrosion productsare most easily released in the area where the deposit thickness is the lowest.

    Figure 21. Tube deposition pattern (Allan, Erickson and McCollor /56/, Figure 2).

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    The tube vibration can be caused by an ultrasonic transmitter, which is inexpensive to use and

    brings no unfavourable gases into the process like the steam soot-blowers do. The weakness

    of the ultrasonic soot removal systems is their incapability to loosen dense deposits.

    Controlled erosion is utilized in external heat exchangers, where the fluidized sand constantly

    removes the deposit. The problem in the process is in balancing the fluidization so that it

    would be sufficient for the deposit removal without causing erosion on the tube material.

    9.0 WAYS OF PREVENTING SUPERHEATER CORROSION

    The superheater corrosion can seldom be totally prevented, if the fuel is harmful enough.

    However, there are ways to reduce the corrosion rate. The previously mentioned factors can

    be taken into account in the superheater design. The concentration of harmful impurities in

    the flue gas can be reduced by limiting the fraction of the worst fuel component. The boiler

    can be designed so that the corrosion risk is reduced. The superheater materials can be

    selected according to the presumed corrosion risk. The superheaters can be placed so that the

    fouling and corrosion are minimized. Hein /59/ has presented several possible means for

    reducing high temperature corrosion:

    avoiding slagging and fouling keeping flue gas O2-lean avoiding unburnt carbon in deposits diluting sulphur oxides changing tube material changing tube temperatures shielding tubes

    changing fuel or blend reducing concentrations of harmful components via additives.

    The aforementioned means are often more or less limited, and the available methods can be

    reduced to the following:

    fuel fractions superheater materials and protection design issues process conditions.

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    9.1 Fuel Fractions

    The difficult fuels are seldom combusted alone. In many cases, the corrosive fuel is only oneof many, and the fraction of the harmful fuel can be reduced. Especially in the case of

    recycled fuels, the fraction of the worst component can be limited so that the chlorine and

    potassium mass flow into the boiler is minimised. This needs to be considered in the fuel

    preparation, because a corrosion attack can be initiated by a short period of combustion of a

    fuel mixture containing substantial amounts of chlorine. Presently, the maximum chlorine

    content allowed in the fuel mixture is 0.05 %.

    The slagging and fouling can also be reduced by fuel selection. By limiting the percentage of

    the slagging and/or fouling fuel component, the superheater fouling can be minimized. In

    addition to the reduced fouling rate, the removability of the deposit can be improved by fuel

    selection.

    9.2 Superheater Materi