Assessment of Chopped Corn Straw Lengths for Combustion in a

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Biofuels and Biomass

Assessment of Chopped Corn Straw Lengths for Combustion in a Fixed Bed using a Numerical Model Xiaoxiao Meng, Rui Sun, Xiang Liu, Tamer Mohamed Ismail, Wei Zhou, M. Abd El-Salam, and Xiaohan Ren Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b00162 • Publication Date (Web): 30 Mar 2018 Downloaded from http://pubs.acs.org on March 31, 2018

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Energy & Fuels

1

Assessment of Chopped Corn Straw Lengths for Combustion in a Fixed Bed

2

using a Numerical Model

3

Xiaoxiao Meng1, Rui Sun1*, Xiang Liu1, Tamer M. Ismail2*, Wei Zhou1, M. Abd El-Salam3, and

4

Xiaohan Ren1, 4

5

1

School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, PR China

6

2

7

Department of Mechanical Engineering, Suez Canal University, Ismailia, Egypt 3

8

4

Department of Basic Science, Cairo University, Giza, Egypt

Institute of Thermal Science and Technology, Shandong University, Jinan 250061, PR China

9 10

Abstract

11

In this paper, both a numerical model and an experimental study were developed to

12

determine the important parameters of corn length for combustion behavior in a fixed-bed

13

reactor. As an important factor impacting thermal conversion, changes in the burning rate

14

follow variations in corn length, which then affect gas emissions. Due to insufficient

15

knowledge concerning the mechanisms of complex combustion, the development of a

16

combustion system has been restricted. Modeling of this combustion system will

17

complement experimental data; however, improving such a model is challenging due to

18

corn’s unique characteristics, such as its moisture content and porosity. The results show

19

that corn straw with a shorter length has a shorter ignition time, increased bed

20

temperature, and reduced amounts of unburned carbon in the ash residues. Furthermore,

21

the burning of shorter corn straw causes high emission concentrations from pyrolysis

22

products such as CH4, CO, and most prevalently NO near the grate, which indicates the 1

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beginning of the char oxidation stage. Corn straw with longer lengths increases the

24

difficulty of accurately modeling the irregular shape of corn straw particles for theoretical

25

calculations. In addition, in an actual bed, local bed structures that have not been

26

uniformly mixed result in uncertainties in the flame propagation as well as the time at

27

which the fuel is ignited. The application of numerical modeling allows for a more

28

detailed description of the corn combustion process and can be used as a reference to

29

develop biomass combustion in a large system.

30

Keywords: corn straw; fixed bed; combustion; numerical model; straw length

31

1. Introduction

32

Of the many techniques using biomass energy, combustion remains the oldest and most

33

common. It is imperative to improve the combustion process to adapt industrial

34

applications and minimize pollutant emissions. Therefore, some of the requirements for a

35

good combustion process are the following: a proper controlling system to allow a

36

homogeneous mixture of devolatilizing gases and air; complete carbon combustion;

37

sustainable development; and reduction of NOx via a primary (staging of an oxidant

38

and/or fuel) or secondary (secondary combustion air flow) technique 1. In addition, the

39

use of biomass has attracted increasing interest as an environmentally friendly renewable

40

energy resource because biomass combustion is considered to be carbon-neutral (a

41

renewable source of fixed carbon) 2,3.

42

The biomass combustion process is composed of four combustion sub-processes: drying,

43

devolatilization and volatile combustion, char combustion, and oxidation

44

the individual physicochemical properties of a biomass, especially for agricultural 2


4,5,6,7

. However,

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Energy & Fuels

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residues, render their conditioning and combustion more difficult compared to

46

conventional fossil fuels. Biomass has a low density, high volatile matter content, low ash

47

fusibility, and high moisture content, which result in low flammability and affect the

48

combustion efficiency of boilers; its high humidity can also delay the devolatilization

49

step, which is critical for combustion 8,9.

50

Over the years, the direct combustion of biomass has been considered the primary

51

method for developing the use of biomass, of which the moving grate combustion

52

technique is thought to be the simplest approach, with the lowest cost and highest level of

53

ease for biomass preparation

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the bed layer in a working biomass chamber are critical to the biomass combustor design

55

and operation. As a smaller laboratory model, a fixed bed can be used for bed layer

56

combustion because it is comparable to a moving grate system due to the limited travel

57

distance. It is easier to collect data for a fixed bed, and the process can be performed with

58

a lower cost. However, when used, there are some overall parameters that influence the

59

fixed bed performance, such as the main air flow parameters, the air distribution system

60

(primary and secondary air flow), the particle size, and the fuel specifications 12,13.

61

In general, the fuel characteristics of a biomass in a fixed bed are significant factors for

62

thermal conversion. Changkook et al.

63

biomasses in a fixed bed. These authors found that large fuel particles can increase the

64

local ignition speed in the bed layer with an unstable flame propagation speed. Yang et al.

65

6

66

burning rate of pine, with smaller sizes showing higher burning rates. Studying the

67

particle size influence in a packed bed, Thunman

10,11

. However, the combustion rate and gas evolution from

14

studied the fuel size and density of different

showed that for pine combustion in a fixed bed, the size had an apparent effect on the

15

showed a significant temperature

3


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difference between the particle surface and the gas. In addition, the fuel size can affect

69

flue gas emissions; for example, the combustion of larger particles in a fixed bed will

70

result in a lower burning rate, which leads to a higher level of CO2 emissions 16.

71

Therefore, both experimental and numerical studies should be conducted to evaluate the

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important parameter of biomass size in fixed bed combustion. CFD modeling is prevalent

73

in simulation applications of biomass combustion in a fixed bed and can describe the

74

combustion process in detail

75

air supply distributions, have been modeled. Proportional evaluations of combustion

76

chamber design are always associated with cold flow experiments using two-dimensional

77

(2D) or three-dimensional (3D) water flow models. Such models are expensive and

78

limited in their applications. Conversely, numerical calculations can rapidly provide

79

accurate results. Such calculations are particularly suitable for complicated geometries

80

and for evaluating the current conditions in a chamber. Currently, simulations allow for

81

detailed parametric alterations to achieve optimal designs.

82

In the combustion process, the elimination of pollutant emissions is the main objective, as

83

well as the mixture of hot gas and cooling air; thus, the organic pollutant needs to be

84

considered in devolatilization. Accordingly, numerical simulations of the flow field

85

within a combustion chamber provide data on the velocity, temperature, and species

86

concentration at each node point of the computational grid. In this study, increased

87

biomass burning is based on detailed mathematical modeling with an understanding of

88

the combustion process, which can be obtained from equations describing the mass, heat

89

transfer, and sub-models

90

SO2, and NOx) by focusing on efficient biomass conversion technologies 18.

21

17,18,19,20

. Currently, various combustor designs, as well as

. This approach will help reduce harmful gas emissions (CO,

4


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Energy & Fuels

22,23,24,25,26

91

In recent years, many researchers

92

of biomass. However, most of the common homogeneous models assume particles with a

93

specific volume, without including the impact of particle shrinkage on the process of

94

pyrolysis during their thermal degradation. Porteiro et al. 27 considered particle shrinkage

95

in their model for large and densified wood combustion during the drying and pyrolysis

96

processes but did not consider char shrinkage due to burning. In addition, fuel size and

97

shape influence the packing level, and changes in porosity arise due to particle shrinkage,

98

which causes a change in the thermal conversion 28. Furthermore, volume shrinkage will

99

occur during all sub-processes of thermal conversion for a single particle inside the 29

have developed models for the combustion

100

burning biomass

101

reaction while ignoring side reactions. Identification of the actual reactions of biomass

102

pyrolysis is extremely complicated due to the formation of several intermediate products.

103

Biomass pyrolysis is generally modeled on the basis of apparent kinetics. Different

104

kinetic patterns are used by different researchers, some of whom were mentioned earlier

105

30,31

106

In a recent study by Menard 32, bed calculations and calculations of the post-combustion

107

chamber were made for wood combustion. This model was written entirely in FLUENT,

108

with user-defined functions to describe the drying, pyrolysis, combustion, and

109

gasification of the bed, considering the interactions between the fuel bed and the gas

110

phase above the bed. However, several critical phenomena are ignored, such as the

111

gasification of the residual carbon and the mixing of the load. In addition, numerous

112

modeling studies on the grid incineration process have been performed 33,34,35,36. The bed

113

model (FLIC software) is coupled to a 3D model of the incineration furnace in FLUENT.

. Some researchers have modeled the pyrolysis process via a single

.

5


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Page 6 of 42

114

The latest version of the model was applied to an agricultural waste grid (straw)

115

incineration

116

on the characteristics of the flow and temperature, including combustion, and the

117

emissions of pollutants such as NOx and CO.

118

In the present work, a theoretical and experimental study was conducted on a fixed bed

119

for corn straw combustion. Compared to our previous mathematical model and

120

simulations

121

model has been improved and is applied to assess the complicated combustion behavior

122

of corn in a current reactor based on changes in particle size. Using a current

123

experimental furnace, the results were obtained to validate the present COMMENT-Code

124

(Combustion Mathematics and Energy Transport), based on comparisons between the

125

existing experimental and model results. Meanwhile, the challenge of temperature-

126

dependent solid conversion processes was overcome. In addition, a shrink core model

127

was implemented to illustrate the char burning phase inside the particles; this model can

128

be used to predict mass loss during the total conversion process of the biomass particles.

129

In this way, the current study makes it easier to improve our understanding of the

130

biomass combustion processes, including the bed temperature change, gas evolution, and

131

combustion efficiency. Furthermore, this work can aid in the study of other parameters,

132

such as the moisture and ash content of the biomass in detailed models, which can then

133

be used in engineering fields and in the optimization of numerical models.

134

2. Computational modeling approach

37

for a sensitivity study of the operating parameters of an incinerator based

38,39,40,41

and our experimental work on a fixed bed

6


42

, the mathematical

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Energy & Fuels

135

The results of the fixed bed combustion output are similar to those of the precise moving

136

grate because the slight temperature gradients in the horizontal direction and the slow

137

moving speed for the grate can be ignored. Several assumptions can be made, as follows:

138

•

The model is uniform for fuels moving in the horizontal direction.

139

•

The rate of fuel mass flow entering the grate is steady.

140

•

Mass and heat transfers occur only in the vertical direction.

141

Based on these three points, the model can be used to simulate the biomass combustion

142

process in a continually moving bed layer. It is difficult to model advanced thermal and

143

chemical processes in a straw-burning furnace while including pollutant emissions and

144

different processes (drying, devolatilization, volatile combustion, and char conversion),

145

as shown in Fig. 1. Numerical models are available; however, a comprehensive and

146

sophisticated computer program must be developed to predict the formation of important

147

pollutants 39.

148

Fig. 1

149

2.1 Governing equations

150

In this study, a more detailed scale is applied than in our previous study and the entire

151

bed is divided into several small volumes. The COMMENT (Mathematics of Combustion

152

and Energy Transport) code was built based on the 2D conservation equations for mass,

153

energy, and momentum with a uniform structured mesh. Discrete equations were written

154

using the volume method and were solved using the SIMPLE algorithm. Therefore, the

155

k–ε turbulence model was used. This model has two transport equations for the turbulent

156

kinetic energy and its degree of dissipation. The proposed k–ε model also describes the 7


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Page 8 of 42

157

turbulent kinetic energy and the dissipation rate, i.e., the exchange between the amount of

158

turbulence and the computation time 38.

159

This code should achieve a converged solution over 1500 iterations; given the limits of

160

our computer capacity and other issues, we stopped at 1500 iterations. The interactions

161

between the gas and solid phases are simulated, and the convective heat exchange and

162

momentum (the drag in the gas and solid phases) are incorporated into the model. In

163

addition, radiation is important when developing combustion models. The definition of

164

the radiation and convective heat transfer is based on the Rosseland model

165

governing equations, momentum conservation, and heat transfer are given in Tables S1–

166

S3 in the supplementary files.

167

2.2 Chemical reaction model

168

It is assumed that two steps occur during the combustion of corn straw within a fixed bed.

169

First, rapid devolatilization occurs, which transfers the volatile matter to the gaseous

170

phase. Second, char particles are burned by oxygen entering with the air from the bottom

171

of the bed. Throughout the devolatilization phase, volatile components are produced from

172

the fuel, i.e., the CO2, CO, CH4, NO, SO2, and HCN components are computed.

173

2.2.1 Drying

174

The speed of moisture emission from solid particles can be given as 12,40

R = A h C, − C, T < 100℃, or R =

T

43

. The

(1)

= 100℃,

(2)

8


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Energy & Fuels

% Q = A h, T − T + ϵδ#T$ − T% &'.

(3)

175

2.2.2 Devolatilization

176

Devolatilization is a pivotal process in combustion, where the biomass is considered to

177

convert to char and volatile species 44.

)* = −+,-

./0 .1

7

= +,- 2* 3* 456 9 0 '

3* = 1.56 × 10>? @ A> ,

(4)

8:

B* = −16600 D )9

(5)

178

From the rapid pyrolysis, the mass fraction EF of each component in the volatile matter

179

can be calculated as follows. MN O

MN

EGHI = 0.201 − 0.469 >??' + 0.241 >??' EHP

TU TU O = 0.157 − 0.868 S V + 1.388 S V 100 100

EGXP = 0.135 − 0.900 S EGX

TU TU O V + 1.906 S V 100 100

TU TU O = 0.428 − 2.653 S V + 4.845 S V 100 100

EHP X = 0.409 − 2.389

MN

' + 4.554

>??

MN O

'

>??

MN O

MN

E1YZ = −0.325 + 7.279 >??' − 12.880 >??'

(6)

(7)

(8)

(9)

(10)

(11)

180

Moreover, during the devolatilization, the released volatile nitrogen and sulfur can be

181

expressed as described by Fine et al. 45: 9


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Page 10 of 42

T, = 0.001[ − 0.6 g/g (d.b.).

(12)

182

2.2.3 Combustion of volatiles

183

The yielded amount of volatile matter is mixed with the surrounding air before the

184

chemical reaction can occur. The volatile species reaction rates are presented as 40

) = min_)`Fa , )bFc d.

(13)

185

It is assumed that the mixing rate inside the bed is proportional to the energy loss through

186

the bed. Recalling the equations of Ergun, the mixing rate can be calculated as follows 46:

187

)bFc = ebFc +f g150

hi #>Aj&P/l Pj .m

+ 1.75

ni #>Aj&o/l .m j

Gtuvw GyP , p. tuvw xyP

p × qrs gx

(14)

188

The mass diffusion coefficient zf is calculated according to the following empirical

189

equation 38: :i }O~.>{ >.{

zf = 1.5 × 10A{ |

190

O

>.%> .

(15)

The combustion process of the volatile components considers the following reactions: >

(16)

>

(17)

O + O → O , O

e + O → eO , O

(18)

>

(19)

e% + O → e + 2O , O

eO + O → eO , O

10


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Energy & Fuels

{

eO + O → 2e + 3O .

(20)

O

191

The reaction rates for each species are given by the following expressions 47:

)HP = 9.87 × 10 456

−3.1 × 10~ eXP eHP , )9 [f ->.~?O×>? ?.{ ?.O{ V C C C , P P

R = 2.239 × 10>O exp S

AO×>? ?.~ ?. V eGH e , I XP 89 :i

)GHI = 5.012 × 10>> 456 S

−1.702 × 10 = 5 × 10 456 eGXP , )9 [f

)GXP

)GP H

−6.6512 × 10~ ?.O~ >.O = 1.0729 × 10 456 eGP H eXP . )9 [f >?

(R1)

(R2)

(R3)

(R4)

(R5)

192

In the above equations eGX , eGXP , eGHI , eGP H , and eHP are the gas species concentrations.

193

2.2.4 Char burnout

e + O → 2#1 − &e + #2 − 1&eO = 194

GX

GXP

= 12 × 456

(21)

A?? :

'

(22)

For temperatures between 730 K and 1170 K,

) =

yP o o }

D. =

{.?×>?¡¢

,

.m

(23)

×

:£ }:i ?.~{ O

'

,

(24)

11


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DZ = 3 [, 456 ¤

−B ¥, )[,

Page 12 of 42

(25)

195

where Ac=3 kg/m² s kPa and Ec/R=10,300 K.

196

2.2.5 NOx emissions model

197

In general, nitrogen in the biomass is released in an uncertain way. In addition, there is a

198

slight amount of biomass remaining in the char state based on the characteristics of the

199

fuel. Therefore, the nitrogen emission is unpredictable and complicated. The detailed

200

reactions considered are as follows 48,49:

¦ + e = 0.5¦O + O ,

(26)

¦ + 0.5e = 0.5¦O + 0.5eO ,

(27)

¦ + e = 0.5¦O + eO ,

(28)

¦ + ¦ + 0.5O = ¦O + O O , )§XA§Hl = 1.07 × 10>O 456

(29)

AO%?? :

' ëXP ë§Hl ë§X .

(R6)

201

2.2.6 SO2 emissions model

202

In this case, the emission of SO2 primarily arises from the volatile matter combustion, as

203

in the following conversion reaction:

© + O → ©O.

(30)

204

The main reaction for the above equation is applied to show the formation of SO2 for the

205

considered reaction rate terms, and the Arrhenius expression is employed as follows 50:

12


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Energy & Fuels

)xXP = ª, 4 A7⁄8: ex eXP .

(31)

206

For the SO2 emission process, the reaction rates are heterogeneous and are determined by

207

the Arrhenius equation, with the activation energy values and the pre-exponential factor

208

describing the reaction rate as follows:

)xXP = 7.29 × 10{ 4 A{{??⁄8: ex eXP .

(R7)

209

2.3 Particle shrinkage model

210

Studies of the biomass diameter and char morphology indicate that the value of the

211

swelling coefficient will impact the biomass particle shrinkage and the particle size in the

212

devolatilization process (Fig. 1). For example, the diameter doubles if the swelling

213

coefficient is 2.0. The swelling coefficient is obtained from morphological analysis and

214

can be calculated from Eq. (32). .m #1& .m,¬

#>ANG¬ &bm,¬ Abm

= 1 + #e, − 1& MN

¬ #>ANG¬ &bm,¬

(32)

215

Here, Ue? is the initial corn straw moisture content, and TU? is the initial volatile

216

material content, which can be obtained from a proximate analysis. The term

217

#>ANG¬ &bm,¬ Abm

218

from the particle.

MN¬ #>ANG¬ &bm,¬

e, =

is the ratio between the total volatile mass and the devolatilized mass

®¯ ®¯°

(33)

219

Here, dp is the average diameter of the particles, and dpo is the average diameter of the

220

parent fuel. 13


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Page 14 of 42

221

Based on the morphological results, the studied biomass swelling factor is 0.7. As a

222

result, this parameter is difficult to measure experimentally. For the purposes of this

223

study, ranges from 0.5–1 are considered uncertain.

224

The char oxidation rates are predicted according to the following equation50: .bm .1

= 3¯ ª S±XP ,$ −

.bm >

.1 xm h

a

V ,

(34)

225

where mp is the particle mass; Ap is the external particle surface area, which can be

226

calculated based on the size of the particle dp; PP,$ is the partial pressure of oxygen; k is

227

the apparent kinetic rate; n is the apparent reaction order; and D is the external diffusion

228

rate coefficient and can be calculated as follows 50:

ª = 3Y 456 S−

BY V, )[

(35)

³ [¯ + [$ /2´ z = 2.57 × 10A~ ®¯ 229

.

(36)

The evaluated diameter is modeled based on the following equation: .m

.m,¬

230

?.~{

= #1 − µ&¶ ,

(37)

where 3Y is the apparent pre-exponential factor; BY is the apparent activation energy; ®¯

231

is the particle diameter (the subscript 0 denotes the initial value); and µ is the degree of

232

burnout.

233

The burning mode #& ranges between 0 and . If the mode decreases to the minimum, 0,

234

the situation corresponds to regime I, which has a decreasing density with constant

>

14


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Energy & Fuels

>

235

particle size. Conversely, if the burning mode reaches the maximum of , this situation

236

refers to regime III, which has a constant density with decreasing particle size. In regime

237

II, the burning mode depends on both the combustion conditions and the particle size. In

238

this model, the burning mode is adjusted to

239

prediction at the stages of late combustion 50.

240

2.4 Numerical method and boundary conditions

241

The finite volume method was used to discretize and solve the governing equations in the

242

present model. The model was executed using a large step of 0.15 mm in both

243

dimensions. The time step used was 0.5 × 10−3 s. The numerical model calculated the bed

244

shrinkage. In addition, an iterative scheme was used to approximate the discrete solution

245

value for the new positions of each node

246

part of model development, and this mesh density was found to be a reasonable

247

compromise between the competing requirements of manageable accuracy and timing. In

248

general, a continuously increasing mesh density will result in slightly different results,

249

depending on the network. Based on previous studies of one-dimensional (1D) or 2D

250

simulations, a medium-sized grid can be applied to save computational time with

251

relatively good accuracy. Conversely, finer meshes result in better computational results

252

with higher calculation times. The currently available Eulerian–Eulerian model is

253

generally closed, with behavioral laws based on the assumption of homogeneity at the

254

level of the computational cells. In addition, there is no need to provide the oxidants of

255

the oxidizing models for mixed gas and mixtures due to the uniform air rate. The gas

256

phase is only allowed to leave the reactor due to the assumption of uniformly sized

51

> %

due to its influence on the burnout

. Mesh sensitivity studies were performed as

15


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257

particles and ash in the gas phase. The output pressure is equal to normal atmospheric

258

pressure.

259

3. Experimental test rig

260

The 1D experimental system is shown in Fig. 2, hanging in a weighing sensor with a 1%

261

measure error. The 1.3-m-high vertical cylinder combustion chamber has a 180-mm inner

262

diameter and consists of three layers of material: the inner layer is a 50-mm-thick high

263

alumina refractory material and can withstand a flame temperature of 1300°C; the middle

264

layer is a 150-mm-thick refractory silicate cotton; and the outer layer is a 5-mm-thick arm

265

protector made of stainless steel 1Cr18Ni9Ti. The grate was placed at the bottom of the

266

chamber and was made of porous stainless-steel plates with 178-mm diameters. The total

267

grid hole surface represents 14.7% of the grate, with a diameter of 7 mm for each hole 42.

268

Table 1 shows the positions of K layer thermocouples, T1 to T10, with a measuring range

269

of 0–1390°C and an accuracy of ±2.5°C. The primary air was supplied through the grate

270

surface, whose temperature is represented by T1. The temperature changes in each layer

271

can be recorded via a digital controller.

272

Tab. 1

273

Throughout the entire experiment process, a Fourier transform infra-red (FTIR) Gasmet

274

4000 analyzer monitored the variations in flue gas emissions from a freeboard above the

275

bed and the layers in the bed. A stainless-steel sampling gun is used to continue to extract

276

gas samples from the bed layers. This approach is used to correct the device and to ensure

277

that the test data are reliable before using the gas measurements. The measured gas

278

species concentration error is approximately ±5% for the entire measurement range. 16


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279

Fig. 2

280

4. Preparation of raw materials

281

Corn feedstock, which is a major agricultural waste, was selected for our experiments. In

282

this experiment, the total corn straws were collected from North China. The harvested

283

corn straw was dried in a natural atmosphere and was regularly turned to prevent the

284

growth of microorganisms and uneven drying. Once the quality of the straw changed by

285

less than 1% per day, the external moisture of the corn straw was regarded as being

286

uniform under room conditions. The basic properties of the prepared corn straw are

287

presented in Table 2, along with the analysis methods used. Based on the actual biomass

288

combustion conditions, the prepared corn straw was prepared at three different lengths,

289

and the quality of the biomass was measured after the biomass was poured into a 5000-ml

290

plastic cylinder and then divided by 5000 ml. Therefore, the natural stacking densities of

291

the various sizes of corn straw can be calculated, as shown in Table 3.

292

Tab. 2

293

Tab. 3

294

5. Results and Discussion

295

5.1. Main flame front characteristics for different corn straw lengths

296

The different sizes of corn straw affect the combustion process at three principal points in

297

the equations. (1) In the heat transfer calculations, the main effects are included via the

298

effective conductivity and the gas–solid convective exchange area, and in the kinetics of 17


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Page 18 of 42

299

the reaction, the effects are included via the specific surface area. (2) For the heat

300

exchange, only the outer surface of the particles is important. (3) For the chemical

301

reactions, the internal volume of the particles should be taken into account. Moreover,

302

compared to a shorter length, corn straw with a longer length results in a small natural

303

bulk density in the chamber; in general, the void volume for the particles becomes large.

304

Therefore, much greater cooling effects of the primary air are exerted based on the

305

increasing average air flow between samples, as well as the decreased global temperature

306

near the grate. In addition, it can be shown that a closer radiation source leads to more

307

heat being absorbed and that the heat from the upper layer ash can be accumulated layer

308

by layer 28.

309

Fig. 3

310

As seen in Fig. 3, which shows the theoretical results for the temperature contour

311

variations of the bed layers in cases 1 and 2, as the length of the corn straw decreases, the

312

times to reach the peak temperature and burnout become shorter, i.e., less than 2000 s for

313

the 5-cm corn straw burnout. It can be seen that high temperatures have a longer duration

314

due to stable combustion in the zone with shorter flames

315

good agreement between the experiment and the present model of the bed temperature

316

variation, which is desirable for model validation. When the flame front reaches each

317

layer, the temperature sharply increases to the peak temperature, which indicates that the

318

mass and heat transfer between the volatiles and the particle surfaces occurs rapidly.

319

Yang et al.

320

moisture inside the solid at a temperature of 100℃, with a long time for drying. Once the

18

42

. In addition, Fig. 4 shows

reported that heat radiation from an upper bed layer could drive out the

18


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Energy & Fuels

321

moisture evaporation in the drying process is complete, the local temperature quickly

322

rises to the temperature of devolatilization and the mass starts to burn.

323

Fig. 4

324

Fig. 5 presents the combustion characteristics of corn straw for the experimental and

325

theoretical studies in a fixed bed. As the corn length increased continuously, the time of

326

corn ignition increased and then decreased when the length reached 15 cm, which is too

327

long to pack uniformly in the chamber, as shown in Fig. 5a. In addition, Figs. 5b and 5c

328

present the average ignition rate (or average ignition flame front velocity) and burning

329

rate in the bed. The average ignition rate can be calculated from the time spent on the

330

ignition flame front to reach a specific pre-determined temperature difference between

331

the top and bottom thermocouple

332

the mass per cross-sectional area unit according to the ignition 52. The results show that

333

the longest corn straw takes the shortest time to burn, with a remarkably higher average

334

ignition rate than the other cases. Because the 15-cm corn straw was packed irregularly

335

into the chamber, some samples closer to the radiation source started to ignite faster. In

336

addition, there is an increasing trend for the average burning rate of the 15-cm corn straw,

337

with a value of 0.034 kg·m−2s−1. The larger void volume also makes it easier to react with

338

the air, leading to a faster burnout rate.

339

In addition, the shortest corn straw (5 cm) started ignition faster than the 10-cm corn

340

straw, as shown in Fig. 5a. This result occurred primarily because the corn straw with the

341

shortest length (5 cm) in the upper bed layer can enhance the heat transfer area from the

342

thermal radiation flux, which accelerates the heat transfer and drying processes. The

343

small straw size can then accelerate devolatilization when the thermal source is the same

42

. The burning rate can be defined as the velocity of

19


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

16

344

in the freeboard space

345

increase in the corn straw length may result from rapid combustion of the local bed layer

346

in the chamber, as shown in Figs. 5b and 5c. According to Ref. 41, large material particles

347

cause unstable bed phenomena, such as ducting, and these elements enhance the local bed

348

flame speed and combustion. In general, there is agreement between the experiment and

349

the theoretical calculations. However, all of the predictions in Fig. 5 present slightly

350

lower values compared to the experiment data, likely because it is difficult to accurately

351

model the irregular particle shapes of corn straw 16.

352

The present mathematical model can be evaluated by comparing the results from the

353

present model and the experimental data. This comparison illustrates the contrast between

354

the numerical model and the existing experimental results for different corn straw lengths.

355

There is good agreement between the model results and the experimental results based on

356

the bed temperatures, average ignition rate, ignition time, and average burning rate.

357

Based on the above comparisons, the numerical model presented is valid and provides a

358

promising method for simulating the combustion of a solid biomass in a fixed bed, which

359

is the dominant technique for combustion.

360

. In addition, the increasing ignition and burning rates with an

Fig. 5

361

5.2. Effects of corn straw length on gas species emissions

362

Figs. 6 shows that the emission concentration contours of CO2 and CO gradually

363

decreased with an increase in the corn straw length. The primary reason for this trend is

364

the concentration of O2 around the corn straw. In addition, the burning rate is another

365

factor that impacts the variation in CO2 and CO; the rate increased and then slightly 20


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Energy & Fuels

366

decreased as the corn straw length was increased. When the length is shorter, the average

367

concentrations of CO2 and CO primarily resulted from the chemical reaction in the

368

pyrolysis and volatile combustion processes. However, the properties of heat exchange

369

between the volatile matter and the raw materials have a dominant effect on the release of

370

CO2 and CO in the chamber once the corn straw length is increased beyond a limited

371

level.

372

The shorter corn straw generated more CO, in contrast to the findings of Yang et al.

373

The main reason for this finding is that the shorter length corn straw in the bed

374

accelerates the pyrolysis reaction, which results in the release of volatile substances,

375

including an emission increase of CO from the fuels. In addition, the high consumption of

376

O2 by the smaller corn straw generates CO2. A lack of O2 into the reactants causes

377

incomplete combustion, producing CO

378

variation, which nearly reached zero at the primary combustion stage, as shown in Fig. 7.

379

In addition, when the length of the fuel particles increases, the density of the sample

380

decreases and the void fraction increases, which increases the O2 supply time between

381

particles for the CO reaction with O2.

53

16

.

, as in the case of the 5-cm O2 concentration

382

Fig. 6

383

Fig. 7

384

Fig. 8 presents experimental and theoretical results for the unburned carbon content in the

385

ash residues. With increasing corn length, a larger amount of unburned carbon remained

386

in the ash residue; the incomplete burning level for the corn straw increased because large

387

amounts of unburned carbon were packed at the bottom in the char oxidation stage, 21


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

388

compared to the emission of CO2 and CO. The reasons for this result are as follows: the

389

porosity can be enhanced by the shorter corn straw, which accelerates the processes of

390

heat transfer and drying, and the shorter corn straw can accelerate pyrolysis and

391

combustion when the thermal source is the same in the freeboard space 15.

392

Fig. 8

393

It can be deduced from Fig. 9 that according to the changes in the corn straw length, the

394

peak value of the CH4 concentration first increases and then decreases. The emission of

395

CH4 can be used to indicate the end of devolatilization and the beginning of the char

396

oxidization stage. The pyrolysis reaction rate increased with decreasing corn straw length,

397

which can rapidly accelerate volatile matter emissions from fuels. In addition, a higher

398

temperature was recorded in the chamber when smaller corn straw was burning, which

399

caused a secondary reaction of tar decomposition 54, with more CH4 released. Meanwhile,

400

a smaller corn straw can enhance volatile oxidization because char burning releases more

401

heat beyond the bed layer, contributing to tar cracking from pyrolysis.

402

Therefore, in an actual biomass combustion process, to cause the feedstock to combust as

403

thoroughly as possible, the corn straw length should not be too short during the

404

pretreatment stage. Conversely, the corn straw should not be too long either. If so, an

405

incomplete combustion process may occur and the content of unburned carbon may

406

increase the ash residue, as shown in Fig. 8. In summary, a greater oxidation speed can be

407

obtained for the carbon residues by decreasing the initial length of the corn straw, in

408

agreement with the observations of Shin and Choi 55.

409

Fig. 9 22


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Energy & Fuels

410

In addition, the nitrogen-containing compounds were studied, with NO being the most

411

prevalent. Therefore, we primarily focused on the emission of NO, as shown in Fig. 10.

412

The influence of corn length on the formation of NO is extremely variable according to

413

the simulations. As the straw length increases, NO emissions decrease due to a very

414

limited heterogeneous reduction by the thin layer of residual carbon above the pyrolysis

415

front and the absence of a second reduction zone in which the base case overcomes the

416

residual carbon oxidation front. NO emissions also decrease because the heterogeneous

417

reduction becomes more efficient due to the presence of many inlets at high temperature.

418

The reduction of NO emission is attributed to a larger residual carbon reduction zone,

419

especially in the hottest burning region. In addition, all cases showed a high

420

concentration near the grate layer because the volatile-N and char-N reactions occurred at

421

the same time.

422

Fig. 10

423

In the current study, the emission of SO2 from the burning corn straw is relatively small.

424

The SO2 concentration variation with increasing corn length can be seen in Fig. 11.

425

Knuden et al.

426

fuels to be released. Meanwhile, once the temperature rises to 850℃, there is a

427

remarkably high release rate of sulfur. In general, there are two existing forms for sulfur,

428

organic and inorganic S, in the feedstock; organic S is emitted at low-temperature

429

conditions, while inorganic S can crack via interactions with a carbonized matrix at

430

temperatures beyond 850°C

431

adsorption effects due to char, which results in organic sulfur formation at temperatures

56

found that temperatures above 1150℃ cause most of the sulfur in the

57

. Reporters

58

also mentioned that there are some SO2

23


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

432

from 700°C to 900°C; these effects will decrease at temperatures above 800°C.

433

Consequently, corn straw longer than 10 cm releasing SO2 has only a low conversion of

434

sulfur to SO2 throughout the entire corn combustion process, due to incomplete

435

combustion and higher biochar. In addition, at increasing straw length, large amounts of

436

semi-char are produced by combustion and pyrolysis in comparison to the other cases;

437

the semi-char can be used to adsorb SO2, and then the SO2 formation decreases.

438

For a corn length of 5 cm, the results of the experimental and theoretical studies can be

439

compared, as shown in Fig. 12; these studies obtained satisfactory agreement for the

440

average emission of gas species. However, the calculated average CO concentration is

441

lower than the measured value, which might be concentrated in the channeling

442

phenomenon in an actual bed. Therefore, some local bed parameters, such as the porosity,

443

particle size distribution, and particle orientation, are not uniform in an actual bed, which

444

results in some air flow passing the particles without a reaction 52. Thus, it is challenging

445

to analyze the experiments and simulations because large particles can increase the

446

instability. In summary, it can be seen that the theoretical results agree with the available

447

experimental data, which validates the numerical model study, and thus, gas species can

448

be compared to obtain a basis in the model.

449

Fig. 11

450

Fig. 12

451

Conclusions

452

Both simulations and experiments are important to study the effects of different corn

453

straw lengths on combustion characteristics. The currently available Eulerian–Eulerian 24


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Energy & Fuels

454

model is generally closed, with behavioral laws based on the assumption of homogeneity

455

at the level of the computational cells. Good agreement between the experimental and

456

theoretical studies was observed in the current study.

457

(1) A decreased corn straw length can shorten the ignition time and increase the bed

458

temperature, with less unburned carbon in the ash residues. Higher bed temperatures

459

lead to larger amounts of pyrolysis CH4 and CO products released in later

460

combustion; meanwhile, the most prevalent NO showed high concentrations due to

461

the concurrent volatile-N and char-N reactions.

462

(2) Compared to experimental studies, there are slightly different values for the

463

theoretical calculations because it is difficult to accurately model the irregular shapes

464

of the particles and some local bed structures are not uniformly mixed in the actual

465

bed. In addition, the extra-long corn (15 cm) packed in the bed led to uncertainties in

466

the flame propagation as well as the time at which the fuel ignited.

467

(3) The presented code can be used to evaluate the different parameters of corn straw

468

combustion. Therefore, the presented model represents a useful way to visualize and

469

evaluate the complicated behavior of the gas–solid flow and the chemical reactions

470

that occur in the biomass combustion process. This study provides a detailed

471

description of the corn combustion process that can be used as a reference for

472

developing biomass combustion in a large system.

473

Acknowledgements

25


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474

This work has been financed by Innovative Research Groups of the National Natural

475

Science Foundation of China (Grant No. 51476046). In addition, thanks for Prof. Yiannis

476

Angelo Levendis’ valuable suggestions and comments on this paper.

477

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580

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Nomenclatures

A

pre-exponent factor, particle surface area

1/s, m²

Cp

specific heat capacity

J/kg K

Cmix mixing rate constant Cw,g moisture concentration in the gas phase

kg/m³

Cw,s moisture concentration at the solid phase

kg/m³

Dg

mass diffusion coefficient of gas

m²/s

DO2 mass diffusion coefficient of oxygen dp

m²/s

particle diameter

m

31


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

E

activation energy

e

coefficient of restitution for particle collisions

·¸

radial distribution function

Page 32 of 42

kJ/mol

Hevp evaporation heat of the solid material

J/kg

hf

enthalpy of formation

J/kg

ℎZ,

radiation heat transfer coefficient

m/s

ℎZ* effective radiation heat transfer coefficient of the voids

m/s

hs

convective mass transfer coefficient

hs'

convection heat transfer coefficient

K

turbulent kinetic energy

kd

diffusion rates

kf

thermal conductivity of the fluid

W/mK

ks

thermal conductivity of the pure

W/mK

kp

absorption coefficient

keff

effective thermal conductivity

W/m² K m²/s² kg/atm m² s

W/mK

keff,0 thermal conductivity for no fluid flow

º,

W/mK

equivalent thickness a layer of solid

m

32


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Energy & Fuels

M

molecular weight

N

number of particles in each control volume

(–)

Qcr

heat absorbed by the solid

W

»Z

radiative flux density

W

)9

gas universal constant

J/kmol K

kg/kmol

Revp moisture evaporation rate

kg/s

Rc

char consumption rate

kg/s

Rv

volatile matter in solid rate

kg/s

©

Source term

Tenv environment temperature

K

Tg

gas temperature

K

Ts

solid temperature

K

U

velocity component

m/s

Greek Letters

absorption coefficient

¼

Interphase exchange coefficient

Θ,

granular temperature

kg/m3 s m2/s2

33


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

¾

dynamic viscosity

void fraction

¿

dissipation rate of turbulent kinetic energy

À

emissivity

Á¯

scattering coefficient

δ

Boltzmann constant

+

density

Âf

thermal dispersion coefficient

Ã

dependent variable

φ

combustion stoichiometry

Ä,

stress tensor

kg/m s

Bulk

C

char burnout

eff

Effective

f

Fluid

g

Gas

m-2s-3

W/m² K4 kg/m³

Pa

Subscripts b

Page 34 of 42

34


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Energy & Fuels

p

Particle

s

Solid

sg

solid to gas

584 585

Tables

586

Tab. 1 Position to grate surface of each thermocouples No.

Position to grate surface (mm)

No.

Position to grate surface (mm)

1

-90

6

388

2

28

7

478

3

118

8

560

4

208

9

748

5

298

10

968

587 588

Tab. 2 Proximate analysis and ultimate analysis. Value

Method

Reference

/

/

GB/T 476-2001

44.46

Carbon dioxide absorption

/

Har%

5.997

Water absorption

/

Oar%

30.35

Calculation

/

Nar%

0.455

Pyrolysis and titration

/

0.11

Pyrolysis and coulometer

GB/T 214-1996

/

/

GB/T 211-1996

Ultimate analysis/wt% （as received basis） Car%

Sar% Proximate analysis /wt%（drybasis）

35


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 42

Aad%

12.39

Slow ashing

/

Mad%

6.23

Air drying

GB/T 212-2001

FCad%

6.23

Calculation

GB/T 213-2003

Vad%

75.15

Thermostatic firing

/

Heating value (MJ/kg)

16.8

calorimeter

GB/T 213-2008

589 590 Tab. 3 Stacking density of different lengths/kg·m-3.

591 Case no.

length/cm

Stacking density/kg·m-3

1

5

81.07

2

10

70.46

3

15

61.61

592 593

Figures

594 36


Corn straw type

Page 37 of 42 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

595

Energy & Fuels

Fig. 1 Modeling of solid particles in a packed bed.

596 597

Fig. 2 The schematic of the one-dimensional fixed bed experimental reactor.

598

599 600

Fig. 3 Effect of different lengths on temperature, T3, versus time along a fixed-bed

601

reactor: (a) 5 cm and (b) 10 cm.

602

37


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

603 604

Fig. 4 Experimental and theoretical bed temperatures versus time along a fixed-bed

605

reactor at different height from the gate (corn straw length as 5 cm).

606

607 608

Fig. 5 Combustion characteristics between experimental and predicted of different

609

parameters within a fixed-bed reactor: (a) ignition time, (b) average ignition rate, (c)

610

average burning rate. 38


Page 38 of 42

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Energy & Fuels

611

612 613

Fig. 6 Concentration of CO2/CO contour (% vol.) inside the fuel bed versus time for

614

different lengths: (a) 5 cm and (b) 10 cm.

615 616

Fig. 7 Concentration of O2 contour (% vol.) inside the fuel bed versus time for the case of

617

5 cm corn straw.

39


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

618 619

Fig. 8 Experimental work and theoretical work of unburned carbon content comparison

620

in the ash residues at different corn length combustion.

621

622 623

Fig. 9 Concentration of CH4 contour (% vol.) inside the fuel bed versus time for different

624

lengths: (a) 5 cm, (b) 10 cm and (c) 15 cm. 40


Page 40 of 42

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Energy & Fuels

625

626 627

Fig. 10 Concentration of NOx contour (% vol.) inside the fuel bed versus time for

628

different lengths: (a) 5 cm, (b) 10 cm and (c) 15 cm.

629

630 631

Fig. 11 Concentration of SO2 contour (% vol.) inside the fuel bed versus time for

632

different lengths: (a) 5 cm, (b) 10 cm and (c) 15 cm. 41


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

633 634

Fig. 12 Experimental and theoretical average concentrations of gas species in a fixed-bed

635

(corn straw length as 5 cm).

636

42


Page 42 of 42

Assessment of Chopped Corn Straw Lengths for Combustion in a

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