study of some performance parameters of an ...

THE JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT

Formerly The Journal of Resource Management and Technology (Volumes 12-22) Formerly NCRR Bulletin (Volumes 1-11) November 2008

Volume 34

Number 4

THE JOURNAL OF SOLID WASTE TECHNOLOGY AND MANAGEMENT ISSN: 1088-1697 Indexed/Abstracted by: Chemical Abstracts; Engineering Abstracts; Environmental Abstracts; Environmental Periodicals Bibliography; Pollution Abstracts, AllRussian Institute of Scientific and Technical Information (VINITI, REFERATIVNYI ZHURNAL )

EDITOR AND FOUNDER: Iraj Zandi University of Pennsylvania U.S.A.

EDITOR: Ronald L. Mersky Widener University U.S.A.

ASSOCIATE EDITOR: Wen K. Shieh University of Pennsylvania U.S.A.

REGIONAL EDITORS

Africa North America David Smith The Regional Municipality of Niagara Public Works Department Waste Management Services Division 2201 St. David’s Road, P.O. Box 1042 Thorold, Ont., L2V 4T7, Canada Email: [email protected]

Shoou-Yuh Chang Department of Civil Engineering North Carolina A&T State University Greensboro, NC 27411, U.S.A. Email: [email protected]

Middle East Emanuel Azmon Prof. Emeritus Ben-Gurion University P.O. Box 20 Omer 84965, Israel

Chukwu Onu Department of Civil Engineering Southern University Southern Branch Post Office Baton Rouge, LA 70813, U.S.A. Email: [email protected]

South and East Asia

A.J. Griffins Cardiff University U.K.

Kasturi Gadgil Centre for Energy Studies Indian Institute of Technology (IIT) New Delhi - 110016, India

Patrick Hettiaratchi University of Calgary Canada

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South and Central America Cristina Braga Universidade Federal do Paraná Setor de Tecnologia Departamento de Engenharia Química Centro Politécnico Cx.P. 19011 Curitiba - PR - 81531-990, Brasil Email: [email protected]

Email: [email protected]

Haluk Akgun Department of Geological Engineering Middle East Technical University Ankara 06531, Turkey Email: [email protected]

Noah I. Galil Department Civil Engineering Technion—Israel Institute of Technology Haifa 32000, Israel

EDITORIAL BOARD

Magdy Abdelrahman North Dakota State University U.S.A. Geoffrey E. Blight University of the Witwatersrand South Africa

Email: [email protected]

Europe Paul Phillips School of Environmental Science University College Northampton Boughton Green Road Northampton, NN2 7AL, U.K. Email: [email protected]

Adam Read Hyder Consulting Ltd Aston Cross Business Village 50 Rocky lane Aston, Birmingham B6 5RQ, U.K. Email: [email protected]

Tom Falcone Indiana University of Pennsylvania U.S.A.

Steve Bloomer University of Teesside U.K. Sarvesh Chandra Indian Institute of Technology Kanpur India Ni-Bin Chang University of Central Florida U.S.A Jess Everett Rowan University U.S.A

Mervat El-Hoz University of Balamand Lebanon Gennaro J. Maffia Widener University U.S.A. Franco Medici University of Rome “La Sapienza” Italy Yusuf Mehta Rowan University U.S.A. Ilan Nissim Israel Ministry of the Environment Israel Ram Ramanujam, P.E. California EPA/DTSC U.S.A. N.C. Vasuki Delaware Solid Waste Authority U.S.A. Ming-Yen Wey National Chung Hsing University Republic of China Keith P Williams Cardiff University U.K. Anita Závodská Barry University U.S.A.

The Journal of Solid Waste Technology and Management, is published by Widener University School of Engineering and the University of Pennsylvania. The responsibility for contents rests upon the authors and not upon the Universities. This journal is available by subscription and may be purchased at the rate of US$120 per volume (4 issues) for individuals and US$285 for libraries, businesses and organizations. Editorial and subscription address is: Department of Civil Engineering, Widener University, One University Place, Chester, PA 19013-5792, U.S.A.; Telephone (610) 499-4042; Fax (610) 499-4461. Email: [email protected]. Web site: www.widener.edu/solid.waste. Copyright © 2008 by Widener University. Printed in U.S.A.

STUDY OF SOME PERFORMANCE PARAMETERS OF AN ATMOSPHERIC BUBBLING FLUIDIZED BED BOILER BASED ON COAL WASHERY REJECTS CONSIDERING BUBBLE GROWTH ALONG BED HEIGHT S. K. Mohapatraa and Ravi Inder Singh b a

Professor and Head, Department of Mechanical Engineering Thapar University, Patiala, Punjab INDIA Email: [email protected] b

Lecturer, Department of Mechanical Engineering Guru Nanak Dev Engineering College, Ludhiana, Punjab INDIA Email: [email protected]

ABSTRACT The increasing problem of the disposal of solid wastes has presented many cities with a dilemma while choosing suitable disposal methods. The traditional means of disposal of solid wastes like coal washery rejects and middling, municipality waste, rice husk, saw dust etc. have been open dumping, land filling or dumping in the sea. These practices have been discouraged due to non availability of land and new environmental legislation. Incineration, has, therefore been identified in several studies to play an important role in future as disposal for solid waste. In India, fluidized bed combustion technology is gradually emerging as a potential technology for the incineration of solid wastes like washery rejects and middling, rice husk, saw dust, and municipality wastes etc. A number of small scale fluidized bed combustion power plants have been commissioned in the recent past as a result a large amount of data is available for modeling and simulation. A mathematical model has been developed for the exit-gas composition in a 10 MW atmospheric fluidized bed boiler in which coal washery rejects are being incinerated at Jamadoba, India. The model allows for bubble size variation with height and predicts the composition of outlet gas composition. Model predictions are compared with plant data and reasonable agreement is obtained. Keywords: Three phase model, bubble growth, exit gas composition

INTRODUCTION A fluidized bed coal combustor consists of a collection of combustible particles suspended in an upward flowing gas stream at such a velocity that the particles are not carried out of the vessel but continue to circulate vigorously within the

vessel. Cavities usually called ‘bubbles’ move through the suspended mass, which help the vigorous circulation of the bed material. Since the bed offers resistance to flow, the drag forces, as given by pressure drop across the bed, are sufficient to support the weight of the bed. Thus the bed has a pseudodensity and has many attributes of a liquid.

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When the superficial velocity of gas flowing through a fixed bed reaches the minimum fluidization velocity, Umf, the fixed bed transforms into an incipiently fluidized bed and the bed starts behaving as a liquid. As increase in gas flow beyond minimum fluidization can cause the extra gas to flow in the form of bubbles. The section of the bed outside the bubbles is called the emulsion phase. Bubbles are gas voids with very little or no solids. Due to buoyancy force a bubble rises through the emulsion phase and its size increases with particle diameter dp, excess gas velocity (U - Umf), and the bed height. A bubble carrying some solid particles in its wake erupts at the bed surface throwing particles into space above the bed, called the freeboard. The particles entrained travel upwards due to their momentum and local drag of gas. Some of the particles may be from the gas and fall to dense bed due to gravity. Beyond a certain height, called transportdisengaging height (TDH), particle disengagement is negligible, and flux rate of particles carried away is known as the elutriation rate. In most practical situations, the fluidizing velocity is sufficiently greater than Umf for bubbles to form continuously.

FLUIDIZED BED COAL COMBUSTION MODELS In this paragraph overall fluidized bed coal combustor models are shortly reviewed; The first attempt to model fluidized bed combustors was presented by Yagi and Kunii (1955), who did not consider, however, the presence of the bubble phase. A big impulse to fluidized bed combustion modeling was given in the seventies and eighties. Since then a great amount of models has been presented in literature as reviewed by La Nauze (1985) and Adanez and Abanades (1992). Most models apply the two-phase theory of fluidization (Davidson and Harrison, 1963) in order to characterize bubbling bed fluid dynamics. A limited number of models assume the presence of three phases: bubbles, clouds (and wakes) and emulsion (Chen and Saxena, 1977 and 1978; Sriramulu et al., 1996). The typical assumption for two-phase models is to consider gas in the bubble phase in plug flow while gas in the dense phase well mixed (Gordon et al., 1978; Leung and Smith, 1979; Garbett and Hedley, 1980; Ross and Davidson, 1981; Congalidis and Georgakis, 1981; Bukur and Amundson, 1982). Exceptions to this assumption are the models of Horio and Wen (1978), Rajan et al. (1978) and Rajan and Wen (1980) where mixed flow in both phases is considered, and the models of Becker et al. (1975), Chen and Saxena (1977), Donsì et al. (1979), Wells et al. (1980) and Arena et al. (1995c) where plug flow in both phases is considered. On the other hand authors proposing models for large scale operation suggested that no distinction has to be made between bubble phase and dense phase because bubbles are in the cloudless regime, so that global plug flow can be assumed (Park et al., 1980a, 1980b, 1981a and b; Bellgardt et al., 1987). A similar assumption was made also

214

for shallow bed combustors by Chakraborty and Howard (1981). Solids in the dense phase are usually assumed to be well mixed. However Fan et al. (1979) and Bellgardt et al. (1987) considered finite radial dispersion of solids. A more complex circulation pattern of solids was developed by Sriramulu et al. (1996). Early models assumed kinetics of char combustion controlled only by external diffusion (Avedesian and Davidson, 1973; Becker et al., 1975; Chen and Saxena, 1977 and 1978; Baron et al., 1978; Fan et al., 1979; Bukur and Amundson, 1981), while later models recognized that also surface reaction and intraparticle diffusion can be relevant to the overall char combustion kinetics. Some models took into account also resistance to gas diffusion through a coherent ash layer that forms around the char particle (Saxena and Rehmat, 1980; Lemcoff, 1988). Redddy and Mohapatra (1994) developed a mathematical model for oxygen mass balance for a 10 MW fluidized bed coal combustion power plant using coal washery rejects. Assuming three-phase theory of fluidization, the fluid bed is considered to consist of a number of equivalent stages in series. Within each stage, an exchange of gas takes place between the bubbles, cloud wake and emulsion phases. An effective chemical reaction rate of char combustion has been derived considering the single film theory of char combustion for shrinking particles. The model has been used to predict the consumption of oxygen in the fluidized bed combustor, the outlet gas composition, variation of average oxygen concentration along the bed height. Model predictions were compared with plant data and reasonable accuracy was obtained. Hull et al. (1999) studied the effect of the tube bank on bubble hydrodynamics in a 2-D bed using similar techniques as used by Yates et al.(1990). They presented the experimental observations of change in the bubble characteristics within and outside the tube bank. The size of the bubbles and their flow within the tube bank was found to depend on the operating conditions and the geometrical arrangements of the tubes. Rongs et al. (1999) carried out similar studies also in a 2-D bed to validate their own simulation of bubble flow and prediction of erosion rates of the tubes due to the tubeparticle interactions. Gustavson and almstedt (2000) simulated a 2-D bubbling fluidized bed containing two horizontal heat exchanger tubes. The solid phases stress was modeled according to an expression suggested by Enwald et al. (1999), which takes into account the soild phase turbulent viscosity along with the collisional viscosity for the effective viscosity of the solid phase. Table 1 gives the brief review of work done on fluidized bed combustor and fluidized bed chemical reactors. At the present time, computer models for coal combustion are not sufficiently accurate to enable the design of combustion plant or the selection models developed to date meet some success in predicting experimental results in selected operating conditions. A number of model parameters is usually adjusted to fit experimental data.

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TABLE 1 Review on Fluidized bed reactors and fluidized bed combustors Authors

Main observations

Main Results

M. Hupa et al. [2007]

1. Fluidized bed boilers in world were discussed. 2. New fuels and multiple firing were discussed. 3. Flue gas emissions for FBC were discussed. 4. Interaction of biomass ashes were discussed.

1. Fluidized-bed technology is rapidly expanding. 2. More than 600 large FBC boilers (>20 MWth) with a total installed thermal capacity of more than 70,000 MWth have been built. Around 75% of this capacity is CFBC technology, the rest mostly BFBC. 3. Besides coals, wood, biomasses, and various waste derived fuels have become important fuels in the different recent FBC projects. 4. Some fuel mixtures show surprising behaviors and there is a lot of interesting research to be done to more quantitatively establish the influence of the fuel mixture on the key operating parameters, such as boiler efficiency, flue gas emissions, ash behave or, and corrosion. 5. Mixed-fuel firing presents a big opportunity for more fundamental research. 6. Fuel analysis techniques should be improved to give more information on the behavior of the fuels when they are burned in mixtures. 7. Characterization of the formation of fuel ash should make it possible to differentiate between the different ash constituents in such a way that mixed fuel effects could be predicted.

1. CFD modeling of hydrodynamic and heat transfer in fluidized bed reactors was done. 2. Gas–solid fluidized bed reactor was simulated applying CFD techniques in order to investigate hydrodynamic and heat transfer phenomena. 3. Reactor model predictions were compared with corresponding experimental data reported in the literature to validate the model.

1. The results indicated that by considering two solid phases, particles with smaller diameters had lower volume fraction at the bottom of the bed and higher volume fraction at the top of the bed. 2. Model predictions of bubble size and gas–solid flow pattern using both Syamlal–O'Brien and Gidaspow drag models were similar. 3. Bed expansion was larger when a bimodal particle mixture was applied compared with the case of mono-dispersed particles. 4. Gas temperature was increased as it moved upward in the reactor due to the heat of polymerization reaction leading to the higher temperatures at the top of the bed.

Ravi Inder et al.[2007]

1. Fluidized bed reactors have been discussed. 2. Three phase model has been used for FBC chemical reactors. 3. Bubble growth variation along bed height for FBC chemical reactors has been considered. 4. Comparison of results with literature data.

1. The proposed model was developed to account for the performance of isothermal fluidized-bed reactors. Numerical solution for the model equation was obtained for reactions that follow first order kinetics. 2. The experimental data on reactant conversions and concentration profile confirmed the validity of the three-phase model with variable bubble diameter. 3. Fractional conversion of reactant gas decreased as the superficial velocity of gas was increased. 4. Average gas concentration profile decreased with increase in bed height. 5. Model developed would be useful for design and scale-up of fluid bed reactors and further above model would be modified to suit commercial plants.

J.J. Saastamoinen et al.[2006]

1. Simplified model for calculation of devolatilization in fluidized beds was developed. 2. It was simple and computationally fast enough to be incorporated as a sub model into a CFD code, but accurate enough to be suitable for different fuels including biomass with varying particle size, moisture, reactivity and shape.

1. The partial differential equation describing heat and mass transfer inside the particle was approximately converted to two differential equations. 2. Drying was described to take place on a shrinking core and pyrolysis, which can take place simultaneously with drying, was described to take place at a specific ‘‘characteristic pyrolysis temperature’’. 3. Devolatilization was approximated to take place at ‘‘characteristic pyrolysis temperature’’ for which expressions had derived. 4. The model was able to predict effects of particle size, shape, moisture, density and reactivity. It includes the temperature history (by the locations of the isotherms) unlike correlation based models so that it is applicable for situations, where interaction between particle model and CFDcalculations are required.

Sunun Limtrakula et al. [2005]

1. Solids motion and holdup profiles in liquid fluidized beds were discussed. 2. Non-invasive gamma rays-based techniques, computer tomography and computer-aided radioactive particle tracking, were used to measure solid holdup and solid velocity profiles in liquid–solid fluidized beds.

1. The solid holdup values increased slightly with the increase in radial position in the fully developed region. The average values of holdup in the column were in agreement with other measurements and with the modified Richardson–Zaki equation. 2. The solids mean velocities and eddy diffusivities increased with increase in liquid superficial velocity, column size, particle size and density. 3. Distributor-type was affecting the mean velocity and turbulence parameters while the column height had a relatively minor effect. 4. The solids motion and turbulence parameters presented would be useful for validation of CFD models.

Yaghoub al.[2007]

Behjat

et.

STUDY OF SOME PERFORMANCE PARAMETERS OF AN ATMOSPHERIC BUBBLING FLUIDIZED BED BOILER

215

1. CFD modeling study to examine the co-firing of pulverized coal and Biomass with particular regard to the burnout of the larger diameter biomass particles. 2. P1 radiation model and RNG k-ε turbulence model was used. 3. Computations were based on a research combustion facility that replicates an industrial coal-fired power station. 4. Pinewood blended with a bituminous UK coal and effects of the wood particle size and shape on the burnout of the combined wood and coal char were investigated. 5. A similarity between the coal and biomass sub models was assumed, although they involve slightly different mechanisms and different kinetic data. 1. An overview of the multiscale methods that were used to study gas–solid-fluidized beds was done.

1. Combustion of small (200µm) wood particles was rapid but the rate of combustion of larger particles was dependent on their composition, size and shape. 2. The addition of small amount of biomass to a coal flame, the reaction environment in the combustor is determined by the combustion of the coal rather than by the biomass kinetics. 3. Particle shape and size are important because biomass does not melt and irregular shape is maintained during combustion. 4. The reactions of the major components of wood, hemicellouse and lignin, are interconnected at high temperature, and wood reacts at one composite rate.

J.J. Saastamoinen et al.[2003]

1. Analytical solutions for steady and unsteady state particle size distributions in FBC and CFBC boilers for nonbreaking char particles have been discussed.

1. Analytical solutions were developed to the Particle size distribution (PSD) of fuel in the bed of FBC and CFBC boilers fornon-breaking char particles. 2. The calculation of PSD in the steady and unsteady states was much faster than using time-consuming numerical methods. 3. Analytical solutions were also useful, since they can be applied without extensive computer programs and as a reference case for testing a sub model for calculating a PSD in a comprehensive computer code. 4. The solution for the unsteady state PSD, mass of fuel in the bed and combustion rate give an insight into how the flow of air should be regulated with different fuels to avoid a significant decrease in the oxygen concentration and large CO emissions in the dynamic conditions, when the feed-rate of fuel is changed. 5. Steady and unsteady solutions for either the PSD or the mass of the char in the bed, along with measurements can be applied to extract kinetic data for the oxidation of char, provided that the particles used are sufficiently small, thereby assuring negligible fragmentation.

Bo Leckner et al.[2003]

1. A survey on research and development of fluidized bed combustion boilers during last two decades in Sweden was done. 2. Research related to emissions, heat transfer and fluid dynamics of FBC boilers were briefly summarized.

1. Bed temperature and excess air are the two most important parameters to influence the NO emission during combustion of coal, but they are not efficient during combustion of bio fuels. 2. There were an overwhelming number of correlations published on heat transfer to surfaces in bubbling fluidized beds. No heat transfer relationship would help if the fluid dynamic situation at the heat transfer surface was not understood. Al most all work on heat transfer in FBC was based on experiences from laboratory equipment and the corresponding results could not readily be transferred to the situation in a combustor bed. 3. A description of the basic fluid dynamic features of a combustor was achieved. There were qualitative similarities with the commonly published data from narrow risers, but there were also fund mental differences.

R. I. Backreedy et al. [2005]

M.A. van der Hoef et al.(2004)

SCOPE OF WORK From above review it is clear that Fluidized bed coal combustors are usually modeled using two or three distinct

216

1. Multi-scale approach involved the LBM, the DPM, the continuum model based on the kinetic theory of granular flow, and the discrete bubble model. 2. The idea was essentially that fundamental models, taking into account the relevant details of fluid–particle (lattice boltzmann model (LBM)) and particle–particle (discrete particle model (DPM)) interactions, were used to develop closure laws to feed continuum models which could used to compute the flow structures on a much larger (industrial) scale. 3. In particular, the TFM and DPM simulations will be used to guide the formulation of additional rules to describe the coalescence of bubbles properly, which is at present not incorporated in the model. This will be the subject of future research.

phases. According to the two-phase theory of Fluidization, a fluidized bed has two phases, a dense or emulsion phase consisting of solid particles and interstitial gas and a dilute or bubble phase consisting of rising voids that are essentially free from the particles. According to the three-phase theory there is an additional phase consisting of cloud-wake region;

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⎡{(K bc )b + (K ce )b + Kf cw }∈b / ⎤ dC cw / dZ + ⎢ ⎥ ⎣U cw ⎦

bulk flow of gas through the emulsion and cloud-wake phases is assumed to be negligibly small. There exist a number of two-phase models that predicts the performance of fluidized-bed coal combustors. These differ in assumptions made on flow behavior, extent of gas mixing and mode of interphase gas transfer. Some of the models have been developed in order to allow examination of the relative importance of bed operating conditions and of model parameters, while others have attempted to simulate smallscale experimental units. A comprehensive model of a large-scale plant is presented here. The model incorporates bubble-size variation with height and a reaction rate constant based on the feed particle-size distribution. It is validated with data from a 10 MW AFBC commercial power plant at Jamadoba.

Emulsion phase:

MODEL ASSUMPTIONS

C b = Ccw = Ce = C0 at Z=0

•

The mathematical equations for estimation of various hydrodynamic parameters in the model are presented in Table 2.

• • • •

Bubbles are carbon free, uniform in size across a given cross-section and well distributed throughout the bed. The gas flowing through the bubble phase is considered to be in plug now. Reaction is isothermal, first order and does not involve a change in the number of modes. Interphase gas exchange occurs in two stages from bubble to cloud-wake and from cloud- wake to emulsion phase. Bed consists of three phases, the bubble phase, emulsion phase and cloud-wake phase. The gas flow rate through the emulsion phase is assumed to remain the same as that under minimum Fluidization conditions.

Model Description The material balance equations for the reactant gas over the differential element of ( dZ ) shown in Figure 1 for each phase are written as;

Bubble phase:

dC b / dZ + [(K bc )b ∈b / U b ]C b =

[(K )

bc b

∈b / U b ]C cw

Cloud-wake phase:

(2)

C cw = [(K bc )b ∈b / U cw ] + [(K ce )b ∈b / U cw ]C e

dCe

/ dZ + [(K ce )b ∈b + K {1− ∈b (1 + f cw )}]

[

]

C e / U mf = (K ce )b ∈b / U mf C cw

(3)

Here K is the reaction–rate constant based on unit volume of the emulsion and cloud-wake (dense) phases. At Z = 0 (bottom), the concentration is that of the incoming feed gas, i.e.

(4)

Equivalent bubble diameter, Db We used a correlation of Stubington et al. (1984) because it is suitable for large-scale beds with tuyer-cap type distributor plate, viz,

Db = 0.43 (U0-Umf) 0.4 (Z+ 4

A0 )0.8 g −0.2

(5)

Volume ratio of the cloud-wake phase to the bubble phase, fcw Rowe and Partridge (1965) studied the behavior of bubbles in a fluid bed by using X-rays and found that the size of the wake (ratio of wake to bubble volume, fw) averages one quarter of the total sphere volume and tends to increase as the particle size decreases. The value of fw was taken to be 0.25 in order to estimate the size of the cloud fc, for which the Davidson and Harrison (1963) correlation is widely used, i.e.

fc= 3Umf / ( ∈b u br − U mf )

(6)

(1) In our case Eq. (16) gives absurd values because of the coarse particles used in the Jamadoba plant and consequently high minimum Fluidization velocity required. We have therefore taken fc as an adjustable parameter, the value of which lies between 0.025 and 0.15. The ratio of the volume of the cloud wake phase to the volume of the bubble is then given by

STUDY OF SOME PERFORMANCE PARAMETERS OF AN ATMOSPHERIC BUBBLING FLUIDIZED BED BOILER

217

Ccw+dCcw

Cb+dCb

Ce+dCe

dZ Cb

Ce

Ccw

Bubble phase

Cloud-wake phase

Emulsion phase

FIGURE 1 Schematic representation of oxygen mass balance with bubble growth

TABLE 2 Hydrodynamic parameters Parameter Minimum fluidization velocity

Theoretical or empirical correlation

[{

Equation No.

U mf = (μ / d p ρ g ) (33.7 ) + 0.0408d p3 p g ( p s − p g )g / μ 2 2

}

1/ 2

− 33.7

]

Wen

18

and Yu (1966) Gas Viscosity Gas density Ratio of the cloud-wake volume to the bubble volume Gas velocity through the bubble phase

μ = 1.4(10 −5 )(Tb )1 / 2 Bird et al. (1960) ρ g = 353.2(10 −3 ) / Tb Bird et al. (1960)

[

f cw = 0.25 + 3U mf / (∈mf u br − U mf

19 20

)]Davidson and Harrison (1963)

U b = (U 0 − U mf )/ (1 + f cw ∈mf ) El-Halwagi and El-Rifai (1988)

[

]

21

22

Gas velocity through the cloud wake phase

U cw = (U 0 − U mf ) / (1 + f cw ∈mf ) f cw ∈mf

Volume fraction of the bubble phase

∈b = U b / u b

Rise velocity of crowd of bub-

u b = U o − U mf + U br Davidson and Harrison (1963)

25

u br = 0.711 gDb

26

bles

(ubr )

Rise velocity of an isolated bubble

(u ) br

Gas-interchange coefficients

( K bc )b and ( K ce )b

(K bc )b (K ce )b

El-Halwagi and El-Rifai (1988)

24

El-Halwagi and El-Rifai (1988)

Davidson and Harrison (1963)

( )

= 4.5(U mf / Db ) + 5.85 De g 0.25 / Db

(

= 6.78 ∈mf Db u b / Db

3 0.5

0.5

23

1.25

)

27 Kunni and Levenspiel (1968)

cient and is given by

f cw = f w + f c

(7)

Reaction- rate constant K K is controlled by surface–reaction and gas diffusion coeffi-

218

1/K= (1/Kg) + (1/Ks)

(8)

Here, Ks is the surface-reaction rate constant for carbon particles and is given by

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Ks= (4.32 x 1011/ T

p

) exp (-22153.086/Tp)

(9)

In terms of Sherwood number (Sh ) ,

K g = (ShD g ) / D p

(10)

We used the correlation of La Nauze et al. (1984) for Sh, which was developed on the assumption of frequent renewal of gas at the particle surface. It was assumed that the particle environment changes so often that a steady-state concentration boundary layer is never achieved. The correlation is

[

Sh = 2 ∈mf + 4 ∈mf d p (U mf / ∈mf +U b ) / πD g for..d p / d pbed ≥ 4.0

]

0.5

(11)

[

Cavg = (U bCb + U cwCcw + U mf Ce )/ U 0

(13)

CO2 = C O − C avg

(14)

H 2 O = [XH (1 − XW )Wcoal / 4U O At ] +

(15)

[( XW )Wcoal / 18U O At ]

N 2 = (0.79 / 22400)(273 / Tb ) +

(16)

[{XN (1 − XW )}/ 28U O At ]

Using equations (13)-(16), the gas composition can be easily calculated. The conversion of oxygen (X) is given by

X = 1 − (Cavg / C0 )

and

Sh = 2 ∈mf + 4d pU mf / πD g

ing relations:

]

(17)

0.5

(12)

for..d p / d bed ≥ 4.0

RESULTS AND DISCUSSION

In the calculations (d p / d pbed ) = 4 has been assumed as the transition point.

SOLUTION PROCEDURE The model was solved numerically by making use of the Runge-Kutta fourth order technique. The average gas composition at the top of the bed is determined by using the follow-

The Jamadoba plant uses coal washery rejects as feed. Proximate and ultimate analyses of the feed are given in Table 3. The fraction of cloud-wake phase fcw has been used as an adjustable parameter. For values of fcw ranging from 0.275 to 0.4, the predicted flue gas analysis and actual plant data are given in Table 4. From the table it is clear that fcw =0.275 yields model values close to the actual values, which suggests that the commercial plant has three-phase Fluidization.

TABLE 3 Analysis of feed of the Jamadoba power plant Jharkhand Proximate analysis

Ultimate analysis

Components

Feed coal, % by weight

Components

Feed coal, % by weight

VM

15.8

C

26.99

FC

22.0

H

1.92

Moisture

2.86

S

0.5

Ash

59.96

N

0.48

O

7.28

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TABLE 4 Flue-gas analysis Components

fcw=0.4

fcw = 0.3

fcw =0.275

Real plant

CO2

16.13

15.39

14.84

12-14

O2

4.18

4.92

5.47

5-7

N2

76.63

76.63

76.63

76-78

The fcw value has been varied and the model prediction for oxygen conversion at different fcw values with bubble growth has been shown in Figure 2. At lower bed levels, there is no distinctive influence of fcw on O2 conversion because, in the lower bed portions, the bubble size is small and therefore there may not be a distinctive cloud-wake phase. However, as the bed height increases, the bubble size also increases, thus causing distinctive cloud-wake formation to take place which, in turn, influences combustion. Figure 3 shows the average O2 concentration as a function of the bed height for different amounts of excess air. As expected, the model predicts the decrease of O2 concentration as the bed height increases for all excess air values. The O2 concentration is higher at all bed levels for higher excess air factors. Variations of the O2 concentrations in different phases and along the bed height are shown in Figure 4. The O2 concentration in the bubble phase decreases gradually but it is steep in the cloud-wake and emulsion phases at the lower bed

levels. As expected, the O2 concentration is highest in the bubble phase, followed by the cloud-wake phase and emulsion phases, because the main combustion reaction occurs in the emulsion phase, as a result of which O2 consumption in this phase is greatest and the O2 concentration lowest.

CONCLUSION • • •

Fractional Conversion of Oxygen

1.0

Exit gas composition predicted through this model considering bubble growth agrees reasonably well with the real plant data. Oxygen concentration decreases as bed height increases for all excess air factors. Oxygen concentration is higher at all bed levels for higher excess air factors.

fcw 0.4 fcw 0.3

Tb=1173 K Ex air = 0.3

fcw 0.275

SHN=2.36

0.8 0.6 0.4 0.2 0.0 10

20

30

40 Bed Height

50

60

70

FIGURE 2 Variation of average oxygen concentration with bed height

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Exair Exair Exair Exair

Average oxygen concentration(gm/mole/cm3)*10-6

2.0

0.2 0.25 0.3 0.35

1.6

1.2

0.8

0.4

0.0 10

20

30 40 Bed Height

50

60

70

FIGURE 3 Variation of average oxygen concentration with bed height

Bubble phase(Cb) Cloud Wake phase(Ccw) Emulsion phase(Ce) Average cancentration(Caveg)

1.6

Average oxygen

3

concentration(gm/mole/cm )*10

-6

2.0

1.2 0.8 0.4 0.0 10

20

30

40

50

60

70

Bed Height

FIGURE 4 Variation of average oxygen concentration with bed height

ACKNOWLEDGEMENT The co-operation extended by the officials of the Jamadoba plant at Jharkhand is highly appreciated and acknowledged.

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NOMENCLATURE: Ao = At = Cavg = Cb = Ccw = Ce = Co = Db = Dg = dp = dpbed = dZ = fcw = g= K= ( Kbc ) b = (Kce)b = Sh = Tb = Tp = ub = ubr = Uo = Ub = Ucw = Umf = Wcoal = X= XH, XW, XN= Z=

Distributor area (cm2) Cross- sectional area of bed (cm2) Average gas concentration of reactant at height Z (mol/cm3) Gas concentration of reactant at height Z of the bubble phase (mol/cm3) Gas concentration of reactant at height Z of the cloud-wake phase (mol/cm3) gas concentration of reactant at height Z of the emulsion phase (mol/cm3) Initial gas concentration of reactant (mol/cm3) equivalent bubble diameter (cm) Gas- phase diffusivity of oxygen (cm2 / /sec) Diameter of the screen aperture or particle diameter ( cm ) Average diameter of bed material (cm) Differential height Fraction of the cloud wake phase in the bed Gravitational acceleration (cm/s2) Reaction rate constant based on unit volume of the dense phase (s–1) Volumetric rate of gas exchange between the bubble and cloud–wake phases (s-1) Volumetric rate of gas exchange between the cloud-wake and emulsion phases (s-1) Sherwood number Bed temperature (K) Surface temperature of the char particle (K) Absolute rise velocity for many bubbles ( cm/s) Absolute rise velocity of a single bubble (cm/s) Superficial gas velocity (cm/s) Superficial gas velocity of the bubble phase (cm/s) Superficial gas velocity of the cloud-wake phase (cm/s) Superficial velocity of fluidizing gas under minimum Fluidization (cm/s) Coal feed rate (gm/s) Fractional conversion of reactant gas leaving bed. Percentage composition by weight of ultimate hydrogen, moisture and nitrogen of the feed, weight (%) Height above the gas distributor (cm)

Greek symbols ∈b = ∈mf = μ= ρg =

Volume fraction of bubbles Mean voidage under minimum Fluidization conditions Viscosity of gas (m.Pa.s.) Density of gas (gm/cm3)

Subscripts b= cw = e=

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Bubble phase Cloud-wake phase Emulsion phase

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