Thermogravimetric kinetic modelling of in-situ catalytic pyrolytic conversion of rice husk to bioenergy using rice hull ash catalyst

Accepted Manuscript Thermogravimetric kinetic modelling of in-situ catalytic pyrolytic conversion of rice husk to bioenergy using rice hull ash catalyst Adrian Chun Minh Loy, Darren Kin Wai Gan, Suzana Yusup, Bridgid Lai Fui Chin, Man Kee Lam, Muhammad Shahbaz, Pornkamol Unrean, Menandro N. Acda, Elisabeth Rianawati PII: DOI: Reference:

S0960-8524(18)30523-6 https://doi.org/10.1016/j.biortech.2018.04.020 BITE 19800

To appear in:

Bioresource Technology

Received Date: Revised Date: Accepted Date:

26 January 2018 3 April 2018 4 April 2018

Please cite this article as: Loy, A.C.M., Gan, D.K.W., Yusup, S., Chin, B.L.F., Lam, M.K., Shahbaz, M., Unrean, P., Acda, M.N., Rianawati, E., Thermogravimetric kinetic modelling of in-situ catalytic pyrolytic conversion of rice husk to bioenergy using rice hull ash catalyst, Bioresource Technology (2018), doi: https://doi.org/10.1016/ j.biortech.2018.04.020

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Thermogravimetric kinetic modelling of in-situ catalytic pyrolytic conversion of rice husk to bioenergy using rice hull ash catalyst

1 2 3 4 5 6

Adrian Chun Minh Loya,b, Darren Kin Wai Ganc, Suzana Yusupa,b*, Bridgid Lai Fui Chinc, Man Kee Lama,b, Muhammad Shahbaza,b, Pornkamol Unreand, Menandro N. Acdae, Elisabeth Rianawatif

7

a

8

Sustainable Living, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak,

9

Malaysia.

Biomass Processing Lab, Centre for Biofuel and Biochemical Research, Institute of

10

b

11

Seri Iskandar, Perak, Malaysia.

12

E-mail: (1) [email protected] (Adrian Chun Minh Loy)

Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610,

13

(2*) [email protected] (Prof Dr Suzana Yusup)(Corresponding)

14

(3) [email protected] (Man Kee Lam)

15

(4) [email protected] (Muhammad Shahbaz)

16 17

c

18

University Malaysia, CDT 250, 98009 Miri Sarawak, Malaysia.

19

E-mail: (1) [email protected] (Darren Kin Wai Gan)

Department of Chemical Engineering, Faculty of Engineering and Science, Curtin

20

(2) [email protected] (Bridgid Lai Fui Chin)

21 22

d

23

Thailand Science Park Paholyothin Road, Klong 1, Klong Luang, Pathumthani 12120,

24

Thailand.

25

E-mail: [email protected] (Pornkamol Unrean)

National Center for Genetic Engineering and Biotechnology (BIOTEC), 113

26 27

e

28

Baños, College, Laguna 4031, Philippines.

29

Email: [email protected]

Department of Forest Products and Paper Science, University of the Philippines Los

30

1

31

f

32

Indonesia.

33

E-mail: [email protected] (Elisabeth Rianawati)

Resilience Development Initiative, Jl. Imperial Imperial 2, No. 52, Bandung 40135,

34 35 36

Keywords: Rice husk; Rice hull ash; Thermogravimetric analysis; Catalytic pyrolysis;

37

Kinetic parameters; Iso conversional kinetic methods

38 39 40

Abstract

41

The thermal degradation behaviour and kinetic parameter of non-catalytic and

42

catalytic pyrolysis of rice husk (RH) using rice hull ash (RHA) as catalyst were

43

investigated using thermogravimetric analysis at four different heating rates of 10, 20,

44

50 and 100 K/min. Four different iso conversional kinetic models such as Kissinger,

45

Friedman, Kissinger-Akahira-Sunose (KAS) and Ozawa-Flynn-Wall (OFW) were

46

applied in this study to calculate the activation energy (EA) and pre-exponential value

47

(A) of the system. The EA of non-catalytic and catalytic pyrolysis was found to be in

48

the range of 152 to 190 kJ/mol and 146 to 153 kJ/mol, respectively. The results

49

showed that the catalytic pyrolysis of RH had resulted in a lower EA as compared to

50

non-catalytic pyrolysis of RH and other biomass in literature. Furthermore, the high

51

Gibb’s free energy obtained in RH implied that it has the potential to serve as a source

52

of bioenergy production.

53 54 55 2

56 57 58

1.0 Introduction

59

Nowadays, the world’s energy output is mostly generated from fossil fuels and

60

experts have warned about the depletion of this resources in the future. The increase

61

of environmental awareness due to greenhouse effect has imposed restrictions on fuel

62

combustion emission. According to International Energy Agency (IEA), most of the

63

world primary energy supply was contributed by fossil fuel. Only 10.3 % of the world

64

total primary energy was supplied by biomass waste and biofuel (Liu et al., 2017;

65

Winzer et al., 2017). However, biomass waste has been known as one of the attractive

66

source for renewable energy since it is biodegradable, cheap, carbon neutrality and

67

low greenhouse gaseous emission (Raheem et al., 2015). Thus, biomass waste should

68

be utilized as fuel for bioenergy (biochar, biofuel, and biogas) generation. Previous

69

literature had reported that 500 million metric tons of biomass waste could generate

70

approximately 60,000 MW for energy recovery, in which could fulfil the energy

71

deficiency gap of energy world supply (Singh et al., 2014).

72 73

Generally, there are two types of conversion of biomass processes to bioenergy

74

namely biochemical conversion (e.g. anaerobic digestion and fermentation) (Harris et

75

al., 2018) and thermochemical conversion (e.g. gasification, pyrolysis, and direct

76

combustion) (Bach & Chen, 2017). Biomass pyrolysis is defined as a thermochemical

77

process which undergoes either in complete absence of oxygen or in limited supply of

78

oxygen to maximize the bio-oil or syngas production (Mishra & Mohanty, 2018).

79

However, Xiang et al.(2018) had reported that the quality of bio-oil produced from 3

80

non-catalytic pyrolysis was low due to high oxygen and moisture content as well as

81

low heating value (Xiang et al., 2018). Recently, the effect of ash in catalytic

82

pyrolysis process has gained more interest from researchers because it could reduce

83

the reaction time and improve the quality of bio-oil as compared to the conventional

84

non-catalytic pyrolysis. Yildiz et al. (2015) had reported that pine wood ash can

85

improve the bio-oil production and reused more than 8 times in catalytic fast pyrolysis

86

of biomass (Yildiz et al., 2015). Meanwhile, Benedetti et al. (2017) had reported fly

87

ash can enhance the bio-oil yield from 59 to 83 wt%.

88 89

Rice hull ash (RHA), or also known as rice husk ash, is the solid residue obtained

90

from decomposition of rice husk (RH) through burning process. Prasare-A &

91

Gheewala (2017) had reported that RHA is suitable to be the catalyst in pyrolysis

92

since it has high silica (SiO2) content and high mesoporous surface area to enhance

93

the pyrolysis reaction (Prasara-A & Gheewala, 2017). Sutrisno & Hidayat (2016) had

94

reported that RHA catalyst can improve the quality of bio-oil (e.g. high heating value,

95

density and acidity) produced from pyrolysis (Sutrisno & Hidayat, 2016). Whereas,

96

Abu Bakar & Titiloye (2013) also reported that Brunei rice husk ash (BRHA) could

97

enhance the yield of gaseous product from 18.4 to 21.6 wt% at 500 ˚C (Abu Bakar &

98

Titiloye, 2013). However, there are still limited studies related to the effect of RHA

99

catalyst in thermal decomposition and kinetic parameters of pyrolysis of RH. To our

100

best knowledge, most of the literature reported were mainly focuses on pyrolysis of

101

RH with the absence of catalyst; e.g. Lim et al. (2016) had reported the investigation

102

of thermal behavior of RH using Kissinger- Akahira- Sunose method (Lim et al., 2016)

103

meanwhile Ma et al. (2015) had investigated the kinetic of RH using TGA-F-TIR 4

104

equipment at low heating value (Ma. et al., 2015). Therefore, the objective of this

105

research is to conduct comparative studies between the thermal degradation behavior

106

of pyrolysis of RH with the absence and presence of RHA catalyst with four different

107

heating rates such as 10 K/min (slow pyrolysis), 20-50 K/min (intermediate pyrolysis)

108

and 100 K/min (fast pyrolysis) conditions to further understand the catalytic pyrolysis

109

mechanism in RH biomass.

110 111

Furthermore, four different iso-conversional kinetic methods such as Kissinger,

112

Friedman, Kissinger-Akahira-Sunose (KAS) and Ozawa-Flynn-Wall (OFW) were

113

adopted to determine the activation energy (EA) and the pre-exponential value (A) of

114

the pyrolysis reaction. Moreover, the thermodynamic parameters such as enthalpy

115

(ΔH), Gibb’s free energy (ΔG) and change of entropy (ΔS) were calculated using the

116

EA obtained. This information is important to provide basic understanding on the

117

behavior of non-catalytic pyrolysis and catalytic pyrolysis of RH. Previous literature

118

had reported that the thermodynamic parameters such as ΔH could be used to evaluate

119

the feasibility of the pyrolysis process since it represents the energy exchanged during

120

a chemical reaction (Mehmood et al., 2017a). Meanwhile, ΔG can be used as a

121

reference in selecting biomass feedstock for industrial scale pyrolysis process since it

122

is the total increase in energy of the system to convert the biomass to bioenergy (Kim

123

et al., 2010). In future, it is suggested that the kinetic parameters obtained in the

124

catalytic pyrolysis of RH using RHA catalyst can be applied in different application

125

such as pilot-scale pyrolysis reactor.

126 127

2.0 Materials and Methods 5

128

2.1 Biomass and catalyst preparation

129

The RH biomass was collected from Malaysia BERNAS rice mill and sun-dried for

130

36-48 h to remove the moisture content of the biomass. Then, the RH was pulverized

131

to a particle size of < 250 μm to increase the surface area and heat transfer efficiency

132

of reaction. Meanwhile, the amorphous RHA catalyst was obtained by burning the RH

133

under muffle furnace temperature of 773 K and grounded to a particle size < 250 μm.

134

Then, the catalyst was oven dry at 373 K for 12 h and allowed to cool down to 298 K.

135 136

The proximate analysis was performed using thermogravimetric analyzer EXSTAR

137

TG/DTA 6300 (Seiko Instrument Inc.) meanwhile the ultimate analysis of RH was

138

analyzed using LECO CHNS-932 elemental analyzer to determine the carbon,

139

hydrogen, nitrogen, oxygen and sulphur content. Moreover, the heating value of RH

140

was analyzed by using IKA C5000 oxygen bomb calorimeter according to standard

141

protocol of ASTM E711-87, the bulk density of RH was estimated using Technico

142

pycnometer at room temperature, and the lignocellulosic composition (e.g.

143

hemicellulose. cellulose, and lignin) of RH was determined according to the neutral

144

detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL)

145

analysis.The lignocellulosic composition of RH was further confirmed using

146

thermogravimetric analyzer EXSTAR TG/DTA 6300 (Seiko Instrument Inc.)

147

according to the method reported in the literature (Carrier et al., 2011).

148 149

Moreover, the functional group of RH biomass and RHA catalyst were evaluated

150

using fourier transform infrared spectroscopy (FT-IR) in the range of 500 to 4000 cm-

151

1

using JASCO FT-IR-4100 equipment whereas the elemental composition of the RH 6

152

biomass and RHA catalyst were determined by X-ray fluorescene (XRF, S8 Tiger

153

Bruker).

154 155

2.2 Thermogravimetric analysis

156

Thermogravimetric analysis was carried out at four different heating rates 10, 20, 50,

157

100 K/min with the presence and absence of RHA catalyst in the pyrolysis RH using a

158

TG-DTG analyzer (EXSTAR TG/DTA 6300). In each experiment, a flowrate of 100

159

ml/min of nitrogen gas (N2) was introduced into the TGA for 10 to 20 min at

160

temperature of 323 K to avoid unwanted oxidation of sample in the pyrolysis zone.

161

Then, approximately 5 mg of RH and 0.5 mg of RHA catalysts (ratio of RH: RHA

162

10:1) were placed in a ceramic crucible and heated from 323 to 1173 K in N2

163

atmosphere (The conditions were based on preliminary study). Lastly, the samples

164

were kept constant at 1173 K for 10 min (residence time). The experiments were

165

conducted more than 3 times to ensure the data reliability.

166 167

2.3 Kinetic study

168

The pyrolysis process of biomass can be described in one-step global process and the

169

mechanism equation is shown in Eq. (1) (Várhegyi et al., 1997):

170

(1)

171

where volatiles refer to the gas and k is the pyrolysis rate constant.

172

The temperature dependence of rate constant, k, is expressed in Arrhenius equation,

173

Eq. (2):

7

174

(2)

175

where EA is the activation energy (kJ/mol), R is the gas constant (8.314 J/K.mol), T is

176

the absolute temperature in Kelvin (K), and A is the pre-exponential factor (min-1).

177

The kinetics of solid-state decomposition can be expressed as:

178

(3)

179

(4)

180

where α, t, k(T), f(α), g(α) represent the conversion fraction, the reaction time, the rate

181

constant, the differential reaction model and integral reaction model, respectively. The

182

conversion fraction, , for non-isothermal thermogravimetric analysis at any

183

temperature can be known as weight loss of the sample and it is defined in Eq. (5):

184

(5)

185

where mi, mt and mf are initial mass, substrate mass at a given time, t, and final

186

substrate mass remaining after pyrolysis reaction. Substitution of Eq. (2) into Eq. (3)

187

and (4) gives the expression of reaction rate in the form as shown in Eq. (6) and (7):

188

(6)

189

(7)

190 191 192

Non-isothermal method is used for solid-state kinetics and the equation of heating rate, can be used to develop the model free (iso-conversional) kinetic models. Substitution the β into Eq. (6) and (7) to form Eq. (8) and (9): 8

193

(8)

194

(9)

195

2.3.1 Kissinger

196

The Kissinger method is an iso-conversional technique used to determine the kinetic

197

parameters of solid-state reaction without knowing the reaction mechanism. Kissinger

198

proposed the reaction model of kinetic is zero order,

199

Eq. (8) and generate

, and take derivative of

which gives Eq. (10):

200

201

(10)

After integrating Eq. (10) and rewrite the equation as:

202

(11)

203

The activation energy (EA) and pre-exponential factor (A) can be determined from the

204

slope and y-intercept of the plot

versus

.

205 206

2.3.2 Friedman

207

Friedman method is a differential iso-conversional which is the most straightforward

208

technique to determine the effective activation energy (EA). Friedman’s model is

209

derived from Eq. (8) by rearranging the equation into Eq. (12):

210

(12)

9

211

At constant conversion with different heating rates, a plot of

212

gives a straight line where the slope is used to determine the apparent activation

213

energy (EA) while the pre-exponential factor (A) is obtained from its intercept.

versus

will

214 215

2.3.3 Flynn-Wall-Ozawa (FWO)

216

Flynn-Wall and Ozawa independently developed an iso-conversional integral method

217

for non-isothermal data by taking the common logarithm of the non-isothermal rate

218

law in Eq. (9) and form Eq. (13) (Venkatesh et al., 2013):

219

(13)

220

where p(x) is the exponential integral. By substituting Doyle’s approximation (Flynn,

221

1983),

into Eq. (13) and form:

222 223

(14) Substitute

into Eq. (14) and rearranging gives:

224

(15)

225

A plot of

226

activation energy (EA) while the pre-exponential factor (A) is obtained from the

227

intercept.

versus

will gives a slope of

228

229

230

10

. to determine the

231

2.3.4 Kissinger-Akira-Sunose

232

Kissinger-Akira-Sunose method is an integral iso-conversional method that widely

233

used to determine the apparent activation energy (EA). The Kissinger method equation

234

is shown in Eq. (16):

235

(16)

236

A plot of

237

will gives a straight line where the slope is used to determine the activation energy

238

(EA) while the pre-exponential factor (A) is obtained from its intercept-c.

versus

at constant conversion,

with different heating rates,

239 240

Iso-conversional kinetic models can be used to determine the kinetic parameters of

241

biomass such as activation energy (EA) and pre-exponential factors (A) as well as

242

thermodynamic parameters such as enthalpy (ΔH), Gibb’s free energy (ΔG) and

243

change of entropy (ΔS) showed in Eq. 17, Eq.18, and Eq.19, respectively.

244

(17)

245

(18)

246

(19)

247

Where: KB: Boltzman constant (1.381 × 10-23 J/K)

248

: h Plank constant (6.626 × 10-34 Js)

249

: Tm DTG peak temperature

250 251 11

252

2.4 Biot number

253

Biot number is a dimensionless group used to compare the relative transport

254

resistance internally and externally (Ezekoye, 2016). The Biot number can be

255

calculated by dividing the internal resistance to heat penetration with external

256

resistance to heat transfer, which expressed in Eq. (20).

257

(20)

258

Besides that, the microscopic analysis of unsteady state condition of biomass particles

259

is extremely hard due to the complex boundary conditions and irregular shape of

260

biomass particles. General equation used for complex boundary can be expressed by

261

dimensionless analysis as shown in Eq. (21).

262

(21)

263

where Ts is the surrounding temperature, Tc is the temperature in the core of particle,

264

Ti is the temperature of the surface of particle and h is the heat transfer coefficient at

265

the surface of particle (W m-2 K-1).

266

By substituting Eq. (20) into Eq. (21) and reduced to Eq. (22).

267

(22)

268 269

For Bi << 0.1, the temperature throughout the solid particles were approximated to be

270

uniform which is known as lumped capacitance system (Bird et al., 2007). Meanwhile,

271

the intermediate Biot number (0.2 – 1.0) are suitable for pyrolysis biomass particles in

272

fluidized bed (Van de Velden et al., 2010). However, the heat transfer is more

273

complicated when Bi > 1.0 due to transient heat conduction within the particles. 12

274

3.0 Results and Discussion

275

3.1 Physiochemical properties of RH

276

The moisture, volatile matter, fixed carbon and ash content in RH were reported as

277

5.56 wt%, 57.55 wt%, 22.21 wt% and 14.68 wt%, respectively. The proximate

278

analysis showed that the moisture content in RH was low which is suitable for

279

pyrolysis and gasification process for bioenergy production (Ahmad et al., 2017). In

280

addition, the RH has a high volatile matter associated with low ash content which

281

indicated higher ignition for bioenergy fuel generation. The hemicellulose, cellulose

282

lignin content were reported as 18.12 wt%, 36.23 wt% and 24.65 wt% in the present

283

study, which is in a good agreement with the RH biomass reported by previous

284

literature (Balasundram et al., 2017).The ultimate analysis showed that the C, H, O, N,

285

and S contents in RH were 38.47 wt%, 5.75 wt%, 54.09 wt%, 1.68 wt%, and < 0.01

286

wt%, respectively. The relatively low content of S and N in RH indicated low

287

emission of NOx, HCl, and SO2 produced during pyrolysis process. Furthermore, RH

288

exhibited a high heating value (HHV) of 15.49 MJ/kg. This energy value could

289

generate sufficient heat required for small second generation biofuel industrial

290

applications (Ferreiro et al., 2017).

291 292 293 294 295

3.2 FT-IR analysis

296

The FT-IR analysis confirmed the presence of cellulose, hemicellulose and lignin

297

functional groups of RH biomass and RHA catalyst. The peak at 3472 cm-1 implied 13

298

the intermolecular bond between the hydroxyl functional groups. The hydroxyl group

299

was corresponding to the phenolic alcoholic (-OH) of SiOH stretching groups and the

300

absorbed water molecules (Hadipramana et al., 2016; Seddighi et al., 2015). The band

301

around 1640-1650 cm-1 was corresponding to the C=O stretching that may be

302

attributed to the hemicelluloses and lignin aromatic groups in RH. The peak in RH

303

biomass spectrum at 1845-1875 cm-1 connected with C=O indicated the ketone

304

carbonyl and aliphatic xylan groups (Goyal & Pepiot, 2017). However, there is no

305

peak associated to ketone carbonyl and aliphatic xylan groups in the RHA spectrum,

306

suggesting that the functional groups could be destroyed at high temperature.

307

Meanwhile, the high intensity peak at 1034.6 cm-1 indicated the Si-O-Si stretching

308

groups in RH biomass and RHA catalyst.

309 310

3.3 XRF analysis

311

The XRF analysis showed that RH and RHA catalyst contained high composition of

312

SiO2 ( > 90 wt%). The high silica (SiO2) content in RHA catalyst could enhance the

313

surface area for pyrolysis reaction (Prasara-A & Gheewala, 2017). Meanwhile, the

314

presence of minor amount of alkali metal oxides (e.g. CaO, MgO and K2O) in RHA

315

catalyst can limit the coke formation on the catalyst and help in water activation (Ali

316

Zadeh Sahraei et al., 2017). Moreover, the metal oxides such as Fe2O3 and Al2O3

317

always take place in the redox of water gas shift (WGS) reactions which enhance the

318

syngas production.

319

3.4 Thermal degradation behaviour of rice husk

320

The thermal degradation behaviour of RH with and without the presence of RHA

321

catalyst was determined by using thermogravimetric analyser (TGA). The

322

thermogravimetric profile is used to understand the mass loss behaviour in each stage 14

323

at given temperature meanwhile differential thermogravimetric (DTG) profile is used

324

to determine the maximum temperature of degradation, initial degradation

325

temperature and final degradation temperature. Fig. 1 and Fig. 2 show the TG and

326

DTG profiles of non-catalytic pyrolysis and catalytic pyrolysis of rice husk,

327

respectively in the heating rates of 10, 20, 50, and 100 K/min.

328 329

From Fig. 1, it was found that the TG curves were divided into three stages. The

330

primary thermal degradation of RH in Stage I was due to the vaporization of water at

331

low temperature range of 373 – 423 K. In Stage II, the curves displayed high mass

332

loss due to devolatilization. The prominent stiff slope of the TG curves was observed

333

in the temperature range between 450 – 650 K was due to thermal decomposition of

334

hemicellulose and cellulose (Mabuda et al., 2016). Another high mass loss occurred

335

can be observed in Stage III from temperature range of 650 K to 1173 K, which

336

represent the lignin decomposition. This observation was due to the strong aromatic

337

rings such as benzene and phenol structure in lignin. Previous study had reported that

338

lignin is rich in O–CH3, C–O–C stretching and C=C functional groups as shown in the

339

FT-IR spectrum, in which a wide range of temperature was needed to degrade the

340

compounds completely (Yang et al., 2007).

341 342

The mass loss for non-catalytic and catalytic pyrolysis and its thermal degradation

343

temperature at various heating rates were listed in Table 1. It was observed that the

344

mass loss for non-catalytic pyrolysis in Stage I, Stage II and Stage III was estimated

345

to be in the range of 3.31 – 4.92%, 50.92 – 56.83%, and 23.83 – 26.55%, respectively

346

meanwhile the mass loss for catalytic pyrolysis in Stage I, Stage II and Stage III was 15

347

estimated to be in the range of 3.63 – 5.47%, 47.69 – 53.63%, and 20.34 – 24.93%,

348

respectively. Yang et al. (2007) has reported that hemicellulose has the highest C=O

349

compounds which decompose at temperature range of 473 – 588K while cellulose

350

contained two strong functional groups of -OH and C–O that degrade at temperature

351

range of 588 – 673K (Yang et al., 2007). Therefore, it can be concluded that Stage II

352

is the active zone due to the highest mass loss in RH where most of the carbonaceous

353

gaseous (e.g. CO, CO2, H2 and CH4) were produced. Hence, the kinetic study was

354

carried out in this stage to understand the thermal degradation behaviour of RH

355

pyrolysis process for bioenergy production. Most of the previous literature reported

356

that the moisture content of biomass should be less than 10% in order to increase the

357

overall energy efficiency of the pyrolysis process (Kumar et al., 2009). The moisture

358

content of the RH in non-catalytic pyrolysis was in the range of 3.31- 4.92 wt%,

359

which is in a good agreement with (Kumar et al., 2009). Most of the thermal

360

degradation reactions occurred up to 1200 K, in which most of the hemicellulose,

361

cellulose, and lignin was completely converted through secondary reactions such as

362

cracking, re-condensation dehydrogenation process and thus, lead to the formation of

363

char. The mass loss of non-catalytic pyrolysis of RH was observed in the range of

364

79.82-85.94 wt% which indicated that RH biomass is a suitable feedstock for

365

gasification feedstock as compared to elephant grass (65.1 wt%) (Braga et al., 2014) ,

366

camel grass (71.5 wt%) (Mehmood et al., 2017b), and cattle manure (57.0 wt%) (Xu

367

& Chen, 2013).

368

3.5 Effect of heating rate on RH degradation

369

The weight loss curves shifted to higher temperature as heating rates increased as

370

shown in Fig. 1 (Stage II). The right shift of temperature profile might due to the 16

371

thermal lag in the decomposition of heat transfer between the outer and inner structure

372

of the biomass (Luangkiattikhun et al., 2008). At low heating rate (e.g.10 K/min), the

373

temperature profile along the cross-section of RH is linear with the outer surface area,

374

and the inner core of RH obtained the same temperature at a specific time due to

375

longer residence time. On the other hand, the temperature profile was difference

376

between the inner core to the outer core along the cross-section of biomass at high

377

heating rates (e.g 100 K/min). From Table 1, it can be observed that the

378

decomposition in Stage II was kept increasing as heating rates increases in non-

379

catalytic and catalytic pyrolysis of RH which indicated higher overall conversion of

380

volatile matter to bio-gas and bio-oil. Whereas at the lower heating rate, the volatiles

381

will stay in reactor at longer time due to long residence time which favoured the

382

secondary thermal reaction (e.g. cracking, polymerization, and condensation) that

383

could lead to formation of char and coke on the catalyst (Mishra & Mohanty, 2018).

384 385

Moreover, there were three significant peaks of RH were shown in DTG profiles in

386

Fig. 2. The maximum peaks of DTG curves of non-catalytic and catalytic pyrolysis of

387

RH proved that Stage II is dominant in the decomposition profile (Pinheiro et al.,

388

2002). It can be observed that there was a shoulder before the maximum peak as

389

shown in the heating rate of 10 K/min. As the heating increases, the shoulder before

390

the peak disappeared completely and the curves became smoother. This phenomenon

391

can be explained as the weak exothermic decomposition of hemicellulose was

392

overtook by endothermic decomposition of cellulose (Yang et al., 2007). Moreover,

393

an increment of lateral shift in temperature of the maximum degradation peak was

394

observed as the heating rate increased. This occurrence is due to the combined effect 17

395

of heat transfer at different heating rates and the heat conductive property of the

396

biomass particle (Khan et al., 2011).

397 398

The heat transfer to the biomass is mostly governed by the ratio of gas and solid

399

turbulence in the reactor. Previous study had reported that the heat transfer coefficient

400

for static bed, fixed bed with forced gas circulation (same as in TGA) and circulating

401

fluidized bed reactors are in the ranges of 10–50 W m-2 K-1, 50–100 W m-2 K-1, and

402

100–1000 W m-2 K-1, respectively (Soleimanikutanaei et al., 2018). Since the

403

pyrolysis reaction in this study was carried out using TGA equipment, the heat

404

transfer coefficient range should be in 50 –100 W m-2 K-1. Thus, the Biot number can

405

be calculated by using the heat coefficient of 50, 75 and 100 W m-2 K-1. Whereas the

406

heat conductivity, kp of RH was 5.0 W m-1 K-1 as reported by previous study (Aldas et

407

al., 2016) and the size of RH particle used in this study was 250μm (radius of particle

408

= 125μm). Therefore, the Bi values were calculated as shown below:

409

410

411

412

From the results above, it can be clearly noticed that the Biot numbers were <<0.1

413

which implied that the heat resistance was dominated by the external convection heat

414

transfer. In other words, the external convection heat transfer rate was determined by

415

the heating rate, β of the system. This result confirmed the DTG curves shown in Fig.

18

416

2 where the maximum degradation temperature was increased with the increasing

417

values of β by achieving high convection heat transfer rates.

418 419

3.6 Effect of catalyst on RH degradation

420

RHA catalyst was introduced into the pyrolysis of biomass to enhance the secondary

421

reactions such as tar cracking reactions and tar dry reforming reactions (Eqs. (20) and

422

(21)). Previous study had reported that RHA contained high amount of nano-sized

423

amorphous SiO2. The SiO2 could act as an absorbent to absorb the CO2 produced and

424

enhance the hydrogen-enrich syngas yield (Shen et al., 2014). Furthermore, the

425

carbon content in the RH could react with the CO2 to produce CO and CH4 as shown

426

in (Eqs. (22) and Eqs. (23)). Moreover, the RH particles would catalyzed on the active

427

sites of RHA catalyst and crack into gaseous, tar and char product. (Alipour et al.,

428

2017).

429

Tar cracking reaction:

(23)

430

Tar dry reforming reactions:

(24)

431

Boudouard reaction:

(25)

432

Methanation:

(26)

433

The TG and DTG evolution profiles of catalytic pyrolysis of rice husk using RHA

434

catalyst were shown in Fig. 1(b) and Fig. 2(b), respectively. Both TG and DTG curves

435

for non-catalytic and catalytic pyrolysis of rice husk showed similar trends. However,

436

the maximum degradation peak of the catalytic pyrolysis of rice husk was lower than

437

the maximum degradation peak for non-catalytic at all heating rates. This maximum

438

degradation rate had decreased 23.45 % from 81 %/min (non-catalytic) to 62 %/min 19

439

(catalytic) at heating rate of 100 K/min. This observation is in a good agreement with

440

previous study that reported that catalytic pyrolysis of biomass can reduce the rate of

441

maximum degradation and time of thermal degradation (Xiang et al., 2018).

442 443

3.7 Kinetic analysis

444

The kinetic analysis was determined by using four iso-conversional kinetic models

445

such as Kissinger, Friedman, KAS and OFW. The active zone (Stage II) was selected

446

for the subsequent kinetic study of rice husk with a set of experimental data at

447

different heating rates (10, 20, 50 and 100 K/min). The Kissinger model was firstused

448

to determine the kinetic parameters for pyrolysis of RH (with and without the

449

presence of RHA) by using the maximum peak temperatures as shown in Fig. 3. The

450

EA and A values for non-catalytic pyrolysis of rice husk were 152.3 kJ/mol and 1.23 ×

451

1013 min-1, respectively. However, the EA of pyrolysis of RH had reduced to 146.35

452

kJ/mol after addition of RHA catalyst. Since EA is the minimum energy requirement

453

for a reaction to activate, the lower EA indicates higher reaction rate with higher

454

energy efficiency (Özsin & Pütün, 2017). The reduction of EA from 152.3 kJ/mol to

455

146.3 kJ/mol in catalytic pyrolysis of RH was in a good agreement with the molecular

456

collision theory. According to molecular collision theory, the pre-exponential factor

457

(A) represents the collision frequency of reactant molecules that depends on the

458

reactant concentration (Balasundram et al., 2017). The higher A value indicated that

459

there is more collision occurred between molecules and initiate a new reaction

460

effectively. Therefore, the A value of pyrolysis of RH had increased from 1.23 × 1013

461

min-1 to 2.84 × 1013 min-1 after the addition of RHA catalysts was due to more

462

collision between the RH molecules in the active sites of RHA and create cleavage of 20

463

old bonds as well as formation of new bond. However, Kissinger only considers the

464

peak temperatures at different heating rates and does not consider the variations of EA

465

with respect to conversion degree, α. Thus, evaluations of kinetic parameters with

466

respect to conversion degree from other model-free techniques such as Friedman,

467

KAS and FWO are said to be more accurate and reliable. Table 2 shows the α for

468

pyrolysis of RH using Friedman, KAS, and FWO kinetic method. The R2 (regression

469

coefficient) was used to assess the quality of the fitted line and the calculated R2 for

470

all α were > 98.3 %, which indicated high accuracy and significant of the models. The

471

average EA of non-catalytic pyrolysis of RH of Friedman, KAS and FWO methods

472

were 190.8 kJ/mol, 183.9 kJ/mol, and 185.7 kJ/mol, respectively as shown in Fig. 4.

473

The average EA of catalytic pyrolysis of RH using RHA catalyst in Friedman (152.6

474

kJ/mol), KAS (151.2 kJ/mol) and FWO (153.6 kJ/mol) obtained is in a good

475

agreement with the Kissinger model, in which all the EA values obtained in catalytic

476

pyrolysis of RHA catalysts were lower than the EA of pyrolysis of RH.

477 478

The values of α were calculated using Eq. (5) for all curves at all heating rates to

479

determine the kinetic parameters. This α range chosen in the kinetic data was in the

480

range of 0.1 to 0.7. This is because α ranges indicated the removal of moisture,

481

degradation of hemicellulose, cellulose and lignin composition in RH. From Table 3,

482

it can be observed that the EA and A values varied, depending on the  instead of

483

assuming the  as a constant value. The fluctuations in the kinetic parameters

484

indicated the reaction mechanism of biomass varies along the temperature range in the

485

active pyrolysis zone (Özsin & Pütün, 2017). It can be noticed that the initial value of

486

EA was the lowest (α= 0.1), which explained the RH was easily degraded when the 21

487

pyrolysis starts. Meanwhile, the final value of EA was the highest (α= 0.7) in non-

488

catalytic pyrolysis indicated the slow secondary reactions and non-uniform reactions

489

of decomposition of RH (lignin). However, the introduction of RHA catalysts had

490

reduced the EA by increasing the rate of secondary reaction. The molecules of the RH

491

would diffuse on the porous structure of the RHA catalyst, then the metal oxides and

492

char components in the RHA catalyst will further enhance the degradation reaction. It

493

is worthy to mention that catalytic pyrolysis using RHA catalyst is a cheap and

494

feasible catalytic pyrolysis process since RHA catalyst required no cost and has the

495

ability to reduce the EA of pyrolysis of RH. Moreover, the EA attained was lower as

496

compared to Wolffia arrhizal (170.37 kJ/mol), Groundnut shell (218.00 kJ/mol),

497

Sewage sludge (216.04 kJ/mol) and Cellulose (217.79kJ/mol) (Ahmad et al., 2018;

498

Bhavanam & Sastry, 2015; Huang et al., 2018; Xiang et al., 2018).

499 500

Overall, the calculated EA values correspond to  value for Friedman, KAS, and

501

FWO were close to each other and is in a good agreement to confirm the reliability of

502

the experimental data. The variation in A values with  value is due to the complex

503

composition of biomass sample and complex reactions take place during

504

decomposition (Kaur et al., 2018). The A value ranging less than 1010 min-1 indicated

505

that the reaction occurred on the surface or a closed complex reaction. The closed

506

complex reaction in this study was related to the loss of water molecules, in which the

507

reagents (e.g. hemicellulose, cellulose, lignin) and activated complex (the structure of

508

the component at the maximum energy point along the reaction path) can rotate freely.

509

As the A value increased to the range of 1010 to 1011 min-1, a simple complex reaction

510

would take place. In this reaction, the reagents can rotate freely while the activated 22

1011 min-1 )

511

complex cannot rotate (Turmanova, 2008). Meanwhile, the values of A

512

implied a complex reaction where the activated complex and the initial reagent were

513

probably restricted in rotation, suggesting the system was fully occupied by molecules

514

and has high degree of arrangement (Xu & Chen, 2013). The A value has been

515

increasing gradually from α= 0.1 to 0.4 because this range was classified as the active

516

zone to degrade the cellulose and hemicellulose content in biomass. In the conversion

517

range of α= 0.4 to 0.7, the lignin component of RH starts to degrade and produce bio-

518

oil or biogas. It can be observed that A value required for the decomposition of lignin

519

was above 1011 min-1 which indicated a complex reaction was taking place and thus,

520

high EA was needed. However, the addition of RHA catalyst into the pyrolysis process

521

had reduced the A value to the range of 1010 min-1. This phenomenon implied that

522

the RHA catalyst was able to convert the complex reaction to a semi simple-complex

523

reaction (a system where some of the reagents can rotate freely while all the activated

524

complex cannot rotate), in which less EA was required.

525 526

Besides EA and A, the other thermodynamic parameters such as enthalpy (ΔH), Gibbs

527

energy (ΔG) and entropy (ΔS) for non-catalytic and catalytic pyrolysis of RH was

528

described in Table 4. The change of enthalpy is the amount of energy transferred

529

during a chemical reaction. There was little potential energy barrier ( 5 kJ/mol) when

530

compared ΔH with EA values of RH, which reflected the feasibility of the reaction to

531

happen under the pyrolysis condition. Previous study had shown that the lower the

532

difference in EA and ΔH values, the more favourable of the reaction to occur

533

(Mehmood et al., 2017b). This is because enthalpy is the energy used for thermal

534

conversion of biomass to different products. Thus, the lower the difference of ΔH 23

535

values to EA values which indicated the bioenergy production is more likely to be

536

attained. This statement is in a good agreement with the ΔH obtained in catalytic

537

pyrolysis of RH. It was found that RHA catalyst could reduce the ΔH value and

538

minimize the difference between ΔH values and EA value that favour the pyrolysis

539

reaction. The Gibb’s free energy (ΔG) represented the total potential energy increases

540

in the system at the approach of the reagents and the formation of activated complex.

541

The calculated ΔG of non-catalytic pyrolysis of RH and catalytic pyrolysis of RH was

542

in the range of 146 – 196 kJ/mol and 146-189 kJ/mol. Previous studies had shown the

543

same parameters such as rice bran 167.17 kJ/mol (Xu & Chen, 2013), sewage sludge

544

159.65 kJ/mol (Huang et al., 2018), and castor residue (152.05 kJ/mol). From Table 4,

545

the non-catalytic pyrolysis of RH had shown more positive ΔS values throughout the

546

conversion degree which indicated that the RH biomass was in higher disorder and

547

lower the availability of the system's energy to generate bioenergy. However, the

548

catalytic pyrolysis of RH using RHA catalyst has shown a different trend in ΔS values,

549

in which most of the ΔS values attained were in negative values. The negative values

550

of ΔS represented low degree of disorder of products as compared to initial substance.

551

This indicated that the activated complex is in a more “organized” structure than the

552

initial reagents, suggesting that less reactivity was needed in the system since more

553

volatiles could be produced easily on the active sites of RHA catalyst which reduced

554

the degree of disorder of products. Previous study also reported similar trend, in

555

which the ΔS change from positive to negative values after introduction of catalyst

556

(Huang et al., 2018). Moreover, there is obviously a relationship between the values

557

of EA, A and ΔS, in which the low values of A correspond to higher values of EA and

558

less negative values of ΔS (Turmanova, 2008). 24

559 560

4.0 Conclusion

561

Comparative study on the thermal degradation behaviour of non-catalytic and

562

catalytic pyrolysis of rice husk (RH) biomass was successfully investigated using four

563

types of iso-conversional methods (Kissinger, Friedman, KAS, OFW). Based on the

564

results, the attained kinetic data is vital for the feasibility evaluation, design and

565

scaling up industrial plant level to determine the optimum energy recovery in this

566

process. The values obtained from the high HHV (15.49 MJ/kg), low EA attained

567

(146.3- 153.6 kJ/mol) and ΔS (146-189 kJ/mol) have evidently shown that catalytic

568

pyrolysis of RH using RHA catalyst provide potential of converting RH to bioenergy

569

in an energy-efficient manner.

570 571

Acknowledgment

572

This research was funded by Dr. Bridgid Chin Lai Fui through the International

573

Foundation for Science, Stockholm, Sweden project grant which entitled ‘Catalytic

574

fast pyrolysis of rice husk for syngas production’ (Research Grant Agreement No, J-

575

1-C6035-1). Furthermore, the author would like to acknowledge Biomass Processing

576

Lab, Centre of Biofuel and Biochemical, University Teknologi PETRONAS,

577

Malaysia for providing the facilities to carry out the research.

578 579 580

Appendix A. Supplementary Data

581

E-supplementary data for this work can be found in e-version of this paper online 25

582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626

References 1. Abu Bakar, M.S., Titiloye, J.O. 2013. Catalytic pyrolysis of rice husk for biooil production. Journal of Analytical and Applied Pyrolysis, 103, 362-368. 2. Ahmad, M.S., Mehmood, M.A., Al Ayed, O.S., Ye, G., Luo, H., Ibrahim, M., Rashid, U., Arbi Nehdi, I., Qadir, G. 2017. Kinetic analyses and pyrolytic behavior of Para grass (Urochloa mutica) for its bioenergy potential. Bioresource Technology, 224, 708-713. 3. Ahmad, M.S., Mehmood, M.A., Liu, C.-G., Tawab, A., Bai, F.-W., Sakdaronnarong, C., Xu, J., Rahimuddin, S.A., Gull, M. 2018. Bioenergy potential of Wolffia arrhiza appraised through pyrolysis, kinetics, thermodynamics parameters and TG-FTIR-MS study of the evolved gases. Bioresource Technology. 4. Aldas, R.E., Dizon, M.D., Roberto, M.L. 2016. Thermal conductivities of rice hull and ash combinations and its use as insulator for a gasifier reactor. 5. Ali Zadeh Sahraei, O., Larachi, F., Abatzoglou, N., Iliuta, M.C. 2017. Hydrogen production by glycerol steam reforming catalyzed by Ni-promoted Fe/Mg-bearing metallurgical wastes. Applied Catalysis B: Environmental, 219, 183-193. 6. Alipour, R., Osmieri, L., Specchia, S., Yusup, S., Tavasoli, A., Zamaniyan, A. 2017. H2-rich syngas production through mixed residual biomass and HDPE waste via integrated catalytic gasification and tar cracking plus bio-char upgrading. Chemical Engineering Journal, 308, 578-587. 7. Bach, Q.-V., Chen, W.-H. 2017. Pyrolysis characteristics and kinetics of microalgae via thermogravimetric analysis (TGA): A state-of-the-art review. Bioresource Technology, 246, 88-100. 8. Balasundram, V., Ibrahim, N., Kasmani, R.M., Hamid, M.K.A., Isha, R., Hasbullah, H., Ali, R.R. 2017. Thermogravimetric catalytic pyrolysis and kinetic studies of coconut copra and rice husk for possible maximum production of pyrolysis oil. Journal of Cleaner Production, 167, 218-228. 9. Bhavanam, A., Sastry, R.C. 2015. Kinetic study of solid waste pyrolysis using distributed activation energy model. Bioresource Technology, 178, 126-131. 10. Bird, R.B., Stewart, W.E., Lightfoot, E.N. 2007. Transport Phenomena. Wiley. 11. Braga, R.M., Melo, D.M.A., Aquino, F.M., Freitas, J.C.O., Melo, M.A.F., Barros, J.M.F., Fontes, M.S.B. 2014. Characterization and comparative study 26

627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674

of pyrolysis kinetics of the rice husk and the elephant grass. Journal of Thermal Analysis and Calorimetry, 115(2), 1915-1920. 12. Carrier, M., Loppinet-Serani, A., Denux, D., Lasnier, J.-M., Ham-Pichavant, F., Cansell, F., Aymonier, C. 2011. Thermogravimetric analysis as a new method to determine the lignocellulosic composition of biomass. Biomass and Bioenergy, 35(1), 298-307. 13. Ezekoye, O.A. 2016. Conduction of Heat in Solids. in: SFPE Handbook of Fire Protection Engineering, (Eds.) M.J. Hurley, D. Gottuk, J.R. Hall, K. Harada, E. Kuligowski, M. Puchovsky, J. Torero, J.M. Watts, C. Wieczorek, Springer New York. New York, NY, pp. 25-52. 14. Ferreiro, A.I., Giudicianni, P., Grottola, C.M., Rabaçal, M., Costa, M., Ragucci, R. 2017. Unresolved Issues on the Kinetic Modeling of Pyrolysis of Woody and Nonwoody Biomass Fuels. Energy & Fuels, 31(4), 4035-4044. 15. Flynn, J.H. 1983. The isoconversional method for determination of energy of activation at constant heating rates. Journal of thermal analysis, 27(1), 95-102. 16. Goyal, H., Pepiot, P. 2017. A Compact Kinetic Model for Biomass Pyrolysis at Gasification Conditions. Energy & Fuels, 31(11), 12120-12132. 17. Hadipramana, J., Riza, F.V., Rahman, I.A., Loon, L.Y., Adnan, S.H., Zaidi, A.M.A. 2016. Pozzolanic Characterization Of Waste Rice Husk Ash (RHA) From Muar, Malaysia. IOP Conference Series: Materials Science and Engineering, 160(1), 012066. 18. Harris, P.W., Schmidt, T., McCabe, B.K. 2018. Bovine bile as a bio-surfactant pre-treatment option for anaerobic digestion of high-fat cattle slaughterhouse waste. Journal of Environmental Chemical Engineering, 6(1), 444-450. 19. Huang, L., Xie, C., Liu, J., Zhang, X., Chang, K., Kuo, J., Sun, J., Xie, W., Zheng, L., Sun, S., Buyukada, M., Evrendilek, F. 2018. Influence of catalysts on co-combustion of sewage sludge and water hyacinth blends as determined by TG-MS analysis. Bioresource Technology, 247, 217-225. 20. Kaur, R., Gera, P., Jha, M.K., Bhaskar, T. 2018. Pyrolysis kinetics and thermodynamic parameters of castor (Ricinus communis) residue using thermogravimetric analysis. Bioresource Technology, 250, 422-428. 21. Khan, Z., Yusup, S., Ahmad, M.M., Uemura, Y., Chok, V.S., Rashid, U., Inayat, A. 2011. Kinetic Study on Palm Oil Waste Decomposition. Biofuel's Engineering Process Technology. 22. Kim, Y.S., Kim, Y.S., Kim, S.H. 2010. Investigation of Thermodynamic Parameters in the Thermal Decomposition of Plastic Waste−Waste Lube Oil Compounds. Environmental Science & Technology, 44(13), 5313-5317. 27

675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722

23. Kumar, A., Jones, D., Hanna, M. 2009. Thermochemical Biomass Gasification: A Review of the Current Status of the Technology. Energies, 2(3), 556. 24. Lim, A.C.R., Chin, B.L.F., Jawad, Z.A., Hii, K.L. 2016. Kinetic Analysis of Rice Husk Pyrolysis Using Kissinger-Akahira-Sunose (KAS) Method. Procedia Engineering, 148, 1247-1251. 25. Liu, Y., Guo, F., Li, X., Li, T., Peng, K., Guo, C., Chang, J. 2017. Catalytic Effect of Iron and Nickel on Gas Formation from Fast Biomass Pyrolysis in a Microfluidized Bed Reactor: A Kinetic Study. Energy & Fuels, 31(11), 12278-12287. 26. Luangkiattikhun, P., Tangsathitkulchai, C., Tangsathitkulchai, M. 2008. Nonisothermal thermogravimetric analysis of oil-palm solid wastes. Bioresource Technology, 99(5), 986-997. 27. Ma., Z.Q., Zhi., W.J., Ye., J.W., Zhang., Q.S. 2015. Determination of pyrolysis characteristics and kinetics of rice husk using TGA-FTIR and model-free integral methods. Biomass Chem Eng. 49, 27-33. 28. Mabuda, A.I., Mamphweli, N.S., Meyer, E.L. 2016. Model free kinetic analysis of biomass/sorbent blends for gasification purposes. Renewable and Sustainable Energy Reviews, 53, 1656-1664. 29. Mehmood, M., Ye, G., Luo, H., Liu, C.-G., Malik, S., Afzal, I., Xu, J., Ahmad, M. 2017a. Pyrolysis and kinetic analyses of Camel grass (Cymbopogon schoenanthus) for bioenergy. 30. Mehmood, M.A., Ye, G., Luo, H., Liu, C., Malik, S., Afzal, I., Xu, J., Ahmad, M.S. 2017b. Pyrolysis and kinetic analyses of Camel grass (Cymbopogon schoenanthus) for bioenergy. Bioresource Technology, 228, 18-24. 31. Mishra, R.K., Mohanty, K. 2018. Pyrolysis kinetics and thermal behavior of waste sawdust biomass using thermogravimetric analysis. Bioresource Technology, 251, 63-74. 32. Özsin, G., Pütün, A.E. 2017. Insights into pyrolysis and co-pyrolysis of biomass and polystyrene: Thermochemical behaviors, kinetics and evolved gas analysis. Energy Conversion and Management, 149, 675-685. 33. Pinheiro, G.F.M., Lourenco, V.L., Iha, K. 2002. Influence of the heating rate in the thermal decomposition of HMX. Journal of Thermal Analysis and Calorumetry, 67, 8. 34. Prasara-A, J., Gheewala, S.H. 2017. Sustainable utilization of rice husk ash from power plants: A review. Journal of Cleaner Production, 167, 1020-1028. 28

723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770

35. Raheem, A., Wan Azlina, W.A.K.G., Taufiq Yap, Y.H., Danquah, M.K., Harun, R. 2015. Thermochemical conversion of microalgal biomass for biofuel production. Renewable and Sustainable Energy Reviews, 49, 990-999. 36. Seddighi, M., Shirini, F., Mamaghani, M. 2015. Brønsted acidic ionic liquid supported on rice husk ash (RHA-[pmim]HSO4): A highly efficient and reusable catalyst for the synthesis of 1-(benzothiazolylamino)phenylmethyl-2naphthols. Comptes Rendus Chimie, 18(5), 573-580. 37. Shen, Y., Zhao, P., Shao, Q., Ma, D., Takahashi, F., Yoshikawa, K. 2014. Insitu catalytic conversion of tar using rice husk char-supported nickel-iron catalysts for biomass pyrolysis/gasification. Applied Catalysis B: Environmental, 152-153, 140-151. 38. Singh, N.B., Kumar, A., Rai, S. 2014. Potential production of bioenergy from biomass in an Indian perspective. Renewable and Sustainable Energy Reviews, 39, 65-78. 39. Soleimanikutanaei, S., Ghasemisahebi, E., Lin, C.-X. 2018. Numerical study of heat transfer enhancement using transverse microchannels in a heat sink. International Journal of Thermal Sciences, 125, 89-100. 40. Sutrisno, B., Hidayat, A. 2016. Upgrading of bio-oil from the pyrolysis of biomass over the rice husk ash catalysts. IOP Conference Series: Materials Science and Engineering, 162(1), 012014. 41. Turmanova, S. 2008. Non-isothermal degradation kinetics of filled with rise husk ash polypropene composites. 42. Van de Velden, M., Baeyens, J., Brems, A., Janssens, B., Dewil, R. 2010. Fundamentals, kinetics and endothermicity of the biomass pyrolysis reaction. Renewable Energy, 35(1), 232-242. 43. Várhegyi, G., Antal, M.J., Jakab, E., Szabó, P. 1997. Kinetic modeling of biomass pyrolysis. Journal of Analytical and Applied Pyrolysis, 42(1), 73-87. 44. Venkatesh, M., Ravi, P., Tewari, S.P. 2013. Isoconversional Kinetic Analysis of Decomposition of Nitroimidazoles: Friedman method vs Flynn–Wall– Ozawa Method. The Journal of Physical Chemistry A, 117(40), 10162-10169. 45. Winzer, F., Kraska, T., Elsenberger, C., Kötter, T., Pude, R. 2017. Biomass from fruit trees for combined energy and food production. Biomass and Bioenergy, 107, 279-286. 46. Xiang, Z., Liang, J., Morgan, H.M., Liu, Y., Mao, H., Bu, Q. 2018. Thermal behavior and kinetic study for co-pyrolysis of lignocellulosic biomass with polyethylene over Cobalt modified ZSM-5 catalyst by thermogravimetric analysis. Bioresource Technology, 247, 804-811. 29

771 772 773 774 775 776 777 778 779 780 781

47. Xu, Y., Chen, B. 2013. Investigation of thermodynamic parameters in the pyrolysis conversion of biomass and manure to biochars using thermogravimetric analysis. Bioresource Technology, 146, 485-493. 48. Yang, H., Yan, R., Chen, H., Lee, D.H., Zheng, C. 2007. Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel, 86(12), 1781-1788. 49. Yildiz, G., Ronsse, F., Venderbosch, R., Duren, R.v., Kersten, S.R.A., Prins, W. 2015. Effect of biomass ash in catalytic fast pyrolysis of pine wood. Applied Catalysis B: Environmental, 168-169, 203-211.

782 783 784 785 786

30

787

List of Figures

788

Figure 1

TGA graphs of a) non-catalytic pyrolysis of RH and b) catalytic pyrolysis of RH using RHA catalyst.

Figure 2

DTG graphs of a) non-catalytic pyrolysis of RH and b) catalytic pyrolysis of RH using RHA catalyst.

Figure 3

Kissinger plot of non-catalytic pyrolysis of RH and catalytic pyrolysis of RH using RHA catalyst.

Figure 4

Kinetic Plots of non-catalytic pyrolysis of RH using a) Friedman, b) KAS and c) FWO method.

789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 31

a)

Removal of moisture

Devolatilation of Lignin content

Devolatilation of hemicellulose and cellulose content

809

b)

810 811 812

Fig. 1: TGA graphs of a) non-catalytic pyrolysis of RH and b) catalytic pyrolysis of RH using RHA catalyst.

813

32

814

a)

Devolatilation of hemicellulose and cellulose content

Devolatilation of Lignin content

Removal of moisture

815 816

b) 817 818 819 820 821 822 823 824 825 826 827 828 829

Fig. 2: DTG graphs of a) non-catalytic pyrolysis of RH and b) catalytic pyrolysis of RH using RHA catalyst.

830

33

831 1000/ Tm2 0 1.54

1.56

1.58

1.6

-2

ln (𝛽/Tm2)

1.66

1.68

rice husk ash

Linear (rice husk) Linear (rice husk ash)

-6

-10

1.64 rice husk

-4

-8

1.62

y = -18.328x + 20.326 R² = 0.7731

y = -17.604x + 19.085 R² = 0.7215

-12

832 833 834

Fig. 3: Kissinger plot of of non-catalytic pyrolysis of RH and catalytic pyrolysis of RH using RHA catalyst.

835

34

836 837

a)

838 839 840 841 842 843 844 845 846 847 848 849 850

b)

851

35

c)

852 853 854

Fig.4: Kinetic Plots of non-catalytic pyrolysis of rice husk using a) Friedman, b) KAS and c) FWO.

855

36

856

List of Tables Table 1

Mass loss during different stages of decomposition of noncatalytic and catalytic pyrolysis of RH using RHA catalyst.

Table 2

Activation energy (EA) and pre-exponential value (A) correspond to conversion degree of non-catalytic pyrolysis and catalytic pyrolysis of RH using RHA catalyst

Table 3

Relationship between conversion plots, pyrolysis temperature and activation energies for non-catalytic and catalytic pyrolysis of RH using RHA catalyst.

Table 4

Enthalpy, Gibbs energy and entropy with respect to conversion degree of non-catalytic pyrolysis and catalytic pyrolysis of RH using RHA catalyst.

857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 37

876 877

Table 1: Mass loss during different stages of decomposition of non- catalytic and catalytic pyrolysis of rice husk of RH using RHA catalyst.

878

Heating rate (K/min) 10 20 50 100

Tinitial (K) 323.4 324.4 323.7 323.2

Tfinal (K) 432.4 435.9 444.2 441.2

Mass loss (%) 4.92 3.31 4.63 3.51

Stage 2

10 20 50 100

432.4 435.9 444.2 441.2

710.4 739.4 729.7 750.2

50.92 53.89 54.76 56.83

Stage 3

10 20 50 100

710.4 729.4 729.7 750.2

1197 1199 1193 1200

23.98 25.83 26.55 24.55

Final residue

10 20 50 100 Heating rate (K/min) 10 20 50 100

-

-

Tinitial (K) 323.3 323.3 323.2 333.3

Tfinal (K) 433.3 443.3 443.2 453.3

20.18 16.97 14.06 15.11 Mass loss (%) 4.69 5.47 5.14 3.63

Stage 2

10 20 50 100

433.3 443.2 443.3 453.3

733.3 733.3 723.2 783.3

50.91 51.69 53.63 52.75

Stage 3

10 20 50 100

733.3 703.3 723.2 783.3

1193 1193 1193 1193

20.48 21.15 25.01 20.34

Final Residue

10 20 50 100

-

-

23.92 21.69 16.22 23.28

Noncatalytic Stage 1

Catalytic Stage 1

38

Table 2: Activation energy (EA) and pre-exponential value (A) correspond to conversion degree of non-catalytic pyrolysis and catalytic pyrolysis of RH using RHA catalyst. α

Friedman EA (J/mol)

KAS A (min-1)

Non-catalytic pyrolysis of RH 0.1 1.299×105 1.28×1010 0.2 1.620×105 5.38×1012 0.3 1.848×105 3.01×1014 0.4 2.111×105 2.64×1016 0.5 2.063×105 5.72×1015 0.6 1.940×105 2.51×1014 0.7 2.477×105 6.25×1016

FWO

R2

Ea (J/mol)

A (min-1)

R2

Ea (J/mol)

A (min-1)

R2

0.987 0.991 0.989 0.990 0.987 0.991 0.983

1.190×105 1.570×105 1.794×105 2.087×105 2.034×105 1.876×105 2.320×105

3.20×1010 5.49×1013 3.03×1015 5.03×1017 8.74×1016 1.63×1015 9.22×1016

0.983 0.991 0.990 0.989 0.983 0.994 0.992

1.218×105 1.584×105 1.800×105 2.081×105 2.033×105 1.886×105 2.398×105

7.05×1010 7.79×1013 3.56×1015 4.65×1017 8.83×1016 2.04×1015 3.61×1017

0.985 0.992 0.991 0.990 0.984 0.995 0.984

Average 1.908×105 1.36×1016 -

1.839×105 9.81×1016 -

Catalytic pyrolysis of RH using RHA catalyst 0.1 1.077×105 4.46×108 0.980 1.040×105 0.2 1.465×105 9.73×1011 0.996 1.462×105 5 12 0.3 1.580×10 6.21×10 0.996 1.568×105 0.4 1.531×105 1.72×1012 0.995 1.474×105 5 13 0.5 1.735×10 5.99×10 0.996 1.733×105 0.6 1.826×105 1.72×1014 0.994 1.846×105 5 10 0.7 1.471×10 1.71×10 0.983 1.461×105 Average 1.526×105 3.43×1013 -

1.30×109 4.64×1012 2.25×1013 2.64×1012 2.30×1014 9.16×1014 5.12×1010

0.986 0.992 0.992 0.995 0.992 0.992 0.982

1.512×105 1.68×1014 -

39

1.857×105 1.31×1017 -

1.076×105 1.482×105 1.585×105 1.498×105 1.747×105 1.860×105 1.503×105

3.70×109 7.70×1012 3.46×1013 4.80×1012 3.21×1014 1.20×1015 1.29×1011

0.989 0.993 0.993 0.996 0.993 0.993 0.985

1.536×105 2.24×1014 -

Table 3: Relationship between conversion plots, pyrolysis temperature and activation energies for non-catalytic and catalytic pyrolysis of RH using RHA catalyst. Conversion Temperature Reactions (α) (K) Non-catalytic pyrolysis of RH α ≤ 0.1 298 – 574 Moisture removal and degradation of simple sugar molecules 0.1 ≤ α ≤ 0.4 574 – 672 Degradation of hemicellulose and cellulose 0.4 ≤ α ≤ 0.7 672 – 745 Degradation of lignin 0.7 ≤ α ≤ 1.0

745 – 1200

Formation of char and residue of lignin decomposition

Activation energy, EA (kJ/mol) Increased from starting point to 129 Increased from 129 to 211 Increased/ fluctuated from 211 to 247 Decreased from 247 to end point

Catalytic pyrolysis of RH using RHA catalyst α ≤ 0.1

298 – 574

0.1 ≤ α ≤ 0.4

574 – 672

0.4 ≤ α ≤ 0.7

672 – 745

0.7 ≤ α ≤ 1.0

745 – 1200

Moisture removal and degradation of simple sugar molecules Degradation of hemicellulose and cellulose Degradation of lignin Formation of char and residue of lignin decomposition

40

Increased from starting point to 107 Increased from 107 to 153 Decreased/fluctuated from 153 to 147 Decreased from 147 to end point

1 2 3

α

Table 4: Enthalpy, Gibbs energy and entropy with respect to conversion degree of non-catalytic pyrolysis and catalytic pyrolysis of RH using RHA catalyst.

Friedman ΔH ΔG ΔS (J/mol) (J/mol) (J/mol.K) Non-catalytic pyrolysis of RH 0.1 1.25×105 1.63×105 -64.82 0.2 1.57×105 1.66×105 -15.05 0.3 1.80×105 1.69×105 18.14 0.4 2.06×105 1.71×105 55.10 0.5 2.01×105 1.74×105 42.20 0.6 1.89×105 1.78×105 15.93 0.7 2.42×105 1.96×105 60.91 Ave 1.86×105 -

KAS ΔH (J/mol)

ΔS (J/mol.K)

FWO ΔH (J/mol)

ΔG (J/mol)

ΔG (J/mol)

ΔS (J/mol.K)

1.14×105 1.52×105 1.74×105 2.04×105 1.98×105 1.82×105 2.26×105 1.78×105

1.47×105 1.50×105 1.51×105 1.53×105 1.56×105 1.61×105 1.78×105 -

-57.23 4.27 37.37 79.61 64.86 31.48 64.14 -

1.17×105 1.54×105 1.75×105 2.03×105 1.98×105 1.83×105 2.34×105 1.80×105

1.46×105 1.49×105 1.51×105 1.53×105 1.56×105 1.61×105 1.78×105 -

-50.66 7.18 38.69 78.97 64.95 33.37 75.49 -

Catalytic pyrolysis of of RH using RHA catalyst 0.1 1.03×105 1.56×105 -92.72 9.95×104 5 5 0.2 1.42×10 1.59×10 -29.32 1.41×105 0.3 1.53×105 1.62×105 -14.18 1.52×105 5 5 0.4 1.48×10 1.64×10 -25.04 1.42×105 0.5 1.68×105 1.65×105 4.26 1.68×105 5 5 0.6 1.77×10 1.69×10 12.75 1.79×105 0.7 1.41×105 1.89×105 -64.69 1.40×105 Ave 1.48×105 1.48×105

1.48×105 1.51×105 1.54×105 1.56×105 1.58×105 1.61×105 1.81×105 -

-83.84 -16.33 -3.48 -21.46 15.44 26.68 -55.58 -

1.03×105 1.43×105 1.53×105 1.45×105 1.70×105 1.81×105 1.44×105 1.46×105

1.46×105 1.51×105 1.54×105 1.55×105 1.58×105 1.61×105 1.80×105 -

-75.12 -12.11 0.10 -16.49 18.21 28.90 -47.89 -

4 5 6

41

7 8

Highlights -

Comparison between non-catalytic and catalytic pyrolysis were studied.

-

Iso-conversional kinetic models (e.g. Kissinger, Friedman, KAS and OFW) were

9 10

analysed.

11 12 13

-

RHA catalyst could lower the activation energy of the system.

14 15

42