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Pyrolysis kinetics of soybean straw usingthermogravimetric analysis

Article in Fuel April 2016

Impact Factor: 3.52 DOI: 10.1016/j.fuel.2015.12.011

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Jingpei Cao

China University of Mining Technology

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Xing Fan

China University of Mining Technology

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Yun-Peng Zhao

China University of Mining Technology

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China University of Mining Technology

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Pyrolysis kinetics of soybean straw using thermogravimetric analysis

Xin Huang, Jing-Pei Cao , Xiao-Yan Zhao, Jing-Xian Wang, Xing Fan, Yun-Peng Zhao, Xian-Yong Wei

Key Laboratory of Coal Processing and Efficient Utilization, Ministry of Education, China University of Mining & Technology, Xuzhou 221116, Jiangsu, China

h i g h l i g h t s

The decomposition zone of soybean straw could be divided into three stages.

The kinetic of soybean straw pyrolysis was studied by three models.

Simulation of pyrolysis showed a good agreement with experimental data.An insight for future applications of soybean straw was provided.

a r t i c l e i n f o

Article history:

Received 25 September 2015

Received in revised form 30 November 2015

Accepted 10 December 2015

Available online 17 December 2015

Keywords:

Kinetics

Pyrolysis

Thermogravimetric analysis

Soybean strawBiomass

a b s t r a c t

Thermochemical conversion of crops straw is receiving renewed attention, due to the energy and mate-

rial recovery that can be achieved. However, it still lacks the kinetic background which is of great impor-

tance for a successful design of thermochemical process. In this work, pyrolysis test for soybean straw

was performed in a non-isothermal thermogravimetric analysis (TGA) in order to determine the thermal

degradation behavior. Pyrolysis experiments were carried out under inert conditions and operated at dif-

ferent heating rates (5, 10, 20, and 30 K/min). Three different kinetic models, iso-conversional Kissinger

AkahiraSunose (KAS), OzawaFlynnWall (OFW) models, and CoatsRedfern method were applied on

TGA data of soybean straw to calculate the kinetic parameters including activation energy, pre-

exponential factor, and reaction order. The activation energy values were 154.15 and 156.22 kJ/mol based

on KAS and OFW models, respectively. Simulation of the soybean straw thermal decomposition using theobtained kinetic parameters and comparison with experimental data are in good agreement.

2015 Elsevier Ltd. All rights reserved.

1. Introduction

The utilization of biomass wastes including agricultural straw,

municipal solid waste, and livestock manure has attracted more

and more attention owing to their potential energy property. Agri-

cultural application, landfill, and incineration are the most com-

mon disposal processes for the wastes, whereas the traditional

disposal routes are becoming increasingly unacceptable due to

land limitations and stringent regulations [1,2]. Alternatively,using biomass wastes as energy feedstock for bio-fuel production

through thermochemical conversion is regarded as an environ-

mentally friendly disposal route with potential economic benefits.

Several thermochemical conversion technologies, including pyrol-

ysis, gasification, and liquefaction conversion, are currently under

development[3,4]. Among the techniques, pyrolysis is most widely

used in thermal decomposition process and effective for converting

biomass waste into useful products of gas, oil, and solid char in an

oxygen-free atmosphere [3,5]. The combustible gas and the oil

products can be used as fuel due to their high calorific values[6].

Moreover, the bio-oil consists of various organic compounds which

can be used as feedstock for value-added chemicals [7,8].

Non-isothermal thermogravimetric analysis (TGA) is one of the

most common methods to investigate the kinetics of pyrolysis.

Accurate pyrolysis kinetic models are essential to the full utiliza-

tion of particular biomass wastes. It also contributes to design rea-

sonable, efficient, and competent process and scale to establish inreal industrial applications for energy generation. The degradation

of biomass wastes is a tremendously complex process, mainly due

to the large number and diverse nature of thermochemical reac-

tions. Previous studies[9]showed that the pyrolysis of agricultural

biomass consists four individual conversion stages, i.e., moisture

evolution, decomposition of hemicellulose, cellulose, and then lig-

nin. Hemicellulose, which is a branched polymer with a low degree

of polymerization, is normally decomposed at 493588 K, where

cellulose maintains highly ordered and stable crystalline

structures. As a result this, the cellulose undergoes decomposition

at 588673 K [10,11]. Lignin is a three-dimensional polymer

http://dx.doi.org/10.1016/j.fuel.2015.12.011

0016-2361/2015 Elsevier Ltd. All rights reserved.

Corresponding author. Tel./fax: +86 516 83591059.

E-mail address:caojingpei@cumt.edu.cn(J.-P. Cao).

Fuel 169 (2016) 9398

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consisting of hydroxyl phenylpropane monomers, and bound adja-

cent to the cellulose fibers to form a lignocellulose. The lignin is

more refractory than the other two components, while having

functional groups with widely distributed thermal stability, and

this results in a board temperature range of decomposition from

433 to 1173 K[11,12].

In the present work, we investigate the kinetics of pyrolysis

process for soybean straw using TGA technique at different heatingrates (5, 10, 20, and 30 K/min) under argon atmosphere and deter-

mine the kinetic parameters through iso-conversional Kissinger

AkahiraSunose (KAS) and OzawaFlynnWall (OFW) models

and CoatsRedfern method. The physicochemical properties are

firstly determined by elemental analyzer and FTIR spectrometer.

2. Materials and methods

2.1. Material

The soybean straw sample was obtained from fields of Xuzhou,

China. It was pulverized to pass through a 40-mesh sieve followed

by drying at 380 K for 24 h and then stored in an airtight container

before use. Ultimate analysis was conducted with an Elementarvario MACRO cube CHNS elemental determinator.Table 1summa-

rizes main characteristics of the soybean straw sample.

2.2. TGA

Pyrolysis tests were carried out in a simultaneous differential

thermogravimetric analyzer, which combines a heat flux type

DSC with a TGA (Mettler Toledo TGA/DSC Stare ESI-0910,

Switzerland; with a precision of temperature measurement

0.15 K, DSC sensitivity 0.1 mW and microbalance sensitivity

0.1 lg). Temperature programmed pyrolysis for soybean straw

was conducted under a dry argon atmosphere with a flow rate of

50 mL/min. Sample with mass of 1520 mg was inserted directly

into a ceramic crucible and temperature was ramped from room

temperature to 380 K and holding for 10 min, then heated to

1173 K with different heating rates of 5, 10, 20, and 30 K/min,

respectively. The data of thermogravimetry (TG) and derivative

thermogravimetry (DTG) were obtained using the software of the

analyzer. The experiments were replicated at least twice.

2.3. FTIR analysis

FTIR spectrum of soybean straw was recorded using a Nicolet

Magna 560 spectrometer, by collecting 128 scans at a resolution

of 4 cm1 in reflectance mode with measuring regions of

4000400 cm1.

2.4. Kinetic study

Biomass pyrolysis is a complex process because more than one

reaction is involved. A full kinetic analysis of the complex system is

generally not feasible, but some kinds of effective or average

kinetic description are still needed. In this work, pyrolysis reaction

kinetics was investigated by three different kinetic models, the

iso-conversional KAS and OFW models and CoatsRedfern method

were applied on TGA data of soybean straw.

2.4.1. The KAS model

The KAS model [13,14] is one of the most widely used iso-

conversional methods to calculate pyrolysis kinetics, which is

based on the following expression:

ln bT2

ln AE

Rga

ERT

whereb is the heating rate (K/min), A is the pre-exponential factor

(min1),a is conversion rate which can be calculated with the lostweight dividing by the total weight of soybean straw, and g(a) is afunction depending on the decomposition mechanism. In the plot of

lnb=T2 versus 1=T, slope gives E=R where R numerical valued

8.314 J/(molK) is the gas constant. Doing so for the whole range

of conversion (01) will produce the activation energy for the pro-

gressing values of conversion.

2.4.2. The OFW model

Another most common and widely accepted methods in scien-

tific community to compute thermo-kinetic parameter from exper-imental data is the OFW model [15,16]. The OFW model used a

correlation of the heating rate of the samples, activation energy

and inverse temperature, which was originally used the Doyles

[17] approximation for the temperature integral. The final form

of the OFW equation is expressed as:

logb log AE

Rga

2:315 0:457

E

RT

It is evident that a linear plot of logb versus the inverse

temperature is sufficient in order to obtain the activation energy

corresponding to each conversion step.

2.4.3. The CoatsRedfern method

The CoatsRedfern integral method[18]which is derived from

Arrhenius equation was used to analyze the characters of pyrolysis

kinetics. Considering the KAS and OFW models are accurate

enough for the calculation of activation energy, the CoatsRedfern

method can be used to determine the pre-exponential factor as

well as the reaction order. The equations for numerical determina-

tion of the kinetic parameters using the CoatsRedferns method

are given below.

ln ln1 a

T2

ln

AR

bE 1

2RT

E

E

RT; n 1

ln 1 a1n 1

n 1T2

" # ln

AR

bE 1

2RT

E

E

RT; n1

wheren is the reaction order which describes the pyrolysis model.

As for first-order reaction (n= 1), the slope of curve ln ln1aT2

h iver-

sus 1=T produce E=R, then the activation energy is derived. For

multistep reaction (n 1), activation energy can be calculated

through KAS or OFW models. Assuming 2RTE, the intercept

can be arranged as lnAR=bE where A can be calculated.

3. Results and discussion

3.1. FTIR analysis

The assignment of structural feature of the soybean straw is

based on published interpretation for FTIR spectral data and the

standard spectra in FTIR libraries, e.g., Aldrich Condensed Phase

FTIR Library[1922]. As shown inFig. 1, the spectrum of the soy-bean straw shows a very strong and board absorption band at

Table 1

Characteristics of soybean straw.

Proximate analysis (wt.%)a Ultima te a na lysis (d af , wt .%) H /C

Mar Ad VMd FCdb C H N S Ob

1.8 4.7 75.5 19.8 47.8 6.9 1.0 0.1 44.3 1.73

a

A: ash; M: moisture; VM: volatile matter; FC: fixed carbon.b Calculated by difference.

94 X. Huang et al./ Fuel 169 (2016) 9398

https://www.researchgate.net/publication/229343954_Characteristics_of_Hemicellulose_Cellulose_and_Lignin_Pyrolysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/222411568_Pyrolysis_of_Biomass_to_Produce_Fuels_and_Chemical_Feedstocks?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-https://www.researchgate.net/publication/231197082_Reaction_Kinetics_in_Differential_Thermal_Analysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/284662595_Joint_convention_of_four_electrical_institutes?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/238141732_A_New_Method_of_Analyzing_Thermogravimetric_Data?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/229785195_A_Quick_Direct_Method_for_the_Determination_of_Activation_Energy_from_Thermogravimetric_Data?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-https://www.researchgate.net/publication/232751367_Series_Approximations_to_the_Equation_of_Thermogravimetric_Data?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/232802746_Kinetic_Parameters_From_Thermogravimetric_Data?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/38114020_Effect_of_acid_pretreatment_of_rice_straw_on_structural_properties_and_enzymatic_hydrolysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/222074687_Enzymatic_hydrolysis_of_pretreated_soybean_straw?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/223789206_Evaluation_of_Sewage_Sludge-Based_Compost_by_FT-IR_Spectroscopy?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-https://www.researchgate.net/publication/232802746_Kinetic_Parameters_From_Thermogravimetric_Data?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/223789206_Evaluation_of_Sewage_Sludge-Based_Compost_by_FT-IR_Spectroscopy?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/232751367_Series_Approximations_to_the_Equation_of_Thermogravimetric_Data?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/229343954_Characteristics_of_Hemicellulose_Cellulose_and_Lignin_Pyrolysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/222074687_Enzymatic_hydrolysis_of_pretreated_soybean_straw?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/284662595_Joint_convention_of_four_electrical_institutes?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/231197082_Reaction_Kinetics_in_Differential_Thermal_Analysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/238141732_A_New_Method_of_Analyzing_Thermogravimetric_Data?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/38114020_Effect_of_acid_pretreatment_of_rice_straw_on_structural_properties_and_enzymatic_hydrolysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/229785195_A_Quick_Direct_Method_for_the_Determination_of_Activation_Energy_from_Thermogravimetric_Data?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/222411568_Pyrolysis_of_Biomass_to_Produce_Fuels_and_Chemical_Feedstocks?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-

7/25/2019 1-s2.0-S0016236115012648-main

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3400 cm1, which is ascribable to H-bonded OAH groups of cellu-

lose[19]. Besides, the band at 2920 cm1 is corresponded to CAH

stretching within the methylene of cellulose. The absorption peaks

at 1733 and 1246 cm1 can be associated with the alkyl ester of

acetyl group in hemicellulose structure and/or the linkage between

hemicellulose and lignin[19]. The C@O groups in alkyl groups oflignin side chains are suggested to conjugate with the aromatic

structure and result in an adsorption peak at 1636 cm1. The bands

at 2850, 1636, and 1422 cm1 have been attributed to absorption

due to CAH deformation within the methoxyl groups of lignin

[19,20]. The characteristic peaks of lignin were observed at

1320 cm1 (CAO of syringyl ring) and 1510 cm1 (aromatic skele-

tal vibration) [21]. The vibrations of these bands overlapped the

CAOAC glycosidic bond stretching at 1160 cm1, CAOAC ring

skeletal vibration at 1105 cm1 and CAOAH stretching of primary

and secondary alcohols at 1055 cm1 [19]. The absorption bands in

9301200 cm1 region correspond to the valent CAO, CAC, and

deformation vibrations of ring structures of CH2OH origin[22].

3.2. Characteristics of thermal decomposition

The TG and DTG curves obtained from the pyrolysis of soybean

straw at heating rates of 5, 10, 20, and 30 K/min are shown inFig. 2.

The decomposition zone of soybean straw could be divided into

three stages. The first one ranging from 383 to 453 K corresponded

to the moisture that the samples contained owing to the hygro-

scopic nature of soybean straw and very light volatiles [23]. The

main decomposition step took place between 453 and 673 K with

one strong peak around 613 K, which may be caused by the pyrol-

ysis of hemicellulose and cellulose[11], whose existence is proved

by FTIR analysis inFig. 1.Following the stage, a shoulder between

673 and 1173 K was observed, which should be associated with the

decomposition of lignin[24,25].

As shown inFig. 2, the increase of heating rate contributes to

the deceleration of thermal degradation processes towards high

temperatures, which could be explained that a high heating rate

led the sample to the given temperature in a short time as a result

of increased thermal lag [26,27]. In addition, the yield of volatile

matter decreased slightly with increasing heating rate. At the tem-perature range from 453 to 773 K, the volatilizing yield is 76.3% at

5 K/min. This yield significantly decreases to 72.1%, 68.7% and

67.5% at 10, 20 and 30 K/min, respectively. On the other hand,

the decrease in heating rates only transferred the peak tempera-

ture to lower value without altering thermal profile of decomposi-

tion, which could be due to the increase in heat changing efficiency

at lower heating rates compared to higher heating rates. This con-

clusion is in good agreement with the study by Kim et al.[28], who

proposed that the maximum rate of decomposition increases with

increasing heating rates owing to increasing thermal energy.

3.3. Determination of activation energy

In order to determine the active energy of the pyrolysis of soy-bean straw, two different model-free, iso-conversion methods (KAS

and OFW models) were carried out. According to KAS and OFW

models, the activation energy can be determined from lnb=T2

and logb, respectively.Fig. 3a illustrates the liner plot of lnb=T2

versus 1=T where slopes give E=R at progressing conversion

degrees, while Fig. 3b depicts the linear plot of logb versus 1=T

where slopes give 0:457E=R at progressing conversion degrees.

It should be noticed that using the available experimental data, lit-

tle or no correlation was observed for values of conversion below

0.1 and above 0.7 [29]. Due to a good correlation was observed

for most sets of experimental data and following the assumption

of the iso-conversional models concerning the constancy of activa-

tion energy, we could reliably exclude of some experimental data

4000 3600 3200 2800 2400 2000 1600 1200 800 400

Wavemunber (cm-1)

A

bsorbance

1160

1055

1246

1422

1636

1733

2850

2920

3400

Fig. 1. FTIR spectrum of soybean straw.

DTG(%.s-1)

0.00

-0.15

-0.30

-0.45

-0.60

373 473 573 673 773 873 973 1073 11730

20

40

60

80

100 5 K/min10 K/min20 K/min30 K/min

Weightloss(%)

Temperature (K)

Fig. 2. TG and DTG curves of soybean straw.

= 0.7 0.6 0.5 0.4 0.3 0.2 0.1

-11.6

-11.0

-10.4

-9.8

(a)

1/T (mK-1)

0.6

0.8

1.0

1.2

1.4

1.6

1.1 1.3 1.5 1.7 1.9

(b)

-9.2

log

ln(

T2

Fig. 3. Kinetic plot for soybean straw using KAS (a) and OFW models (b).

X. Huang et al. / Fuel 169 (2016) 9398 95

https://www.researchgate.net/publication/38114020_Effect_of_acid_pretreatment_of_rice_straw_on_structural_properties_and_enzymatic_hydrolysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/38114020_Effect_of_acid_pretreatment_of_rice_straw_on_structural_properties_and_enzymatic_hydrolysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/38114020_Effect_of_acid_pretreatment_of_rice_straw_on_structural_properties_and_enzymatic_hydrolysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-https://www.researchgate.net/publication/222074687_Enzymatic_hydrolysis_of_pretreated_soybean_straw?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/38114020_Effect_of_acid_pretreatment_of_rice_straw_on_structural_properties_and_enzymatic_hydrolysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/223789206_Evaluation_of_Sewage_Sludge-Based_Compost_by_FT-IR_Spectroscopy?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-https://www.researchgate.net/publication/255733446_Effect_of_water_washing_on_the_thermal_behavior_of_rice_straw?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/229343954_Characteristics_of_Hemicellulose_Cellulose_and_Lignin_Pyrolysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-http://-/?-https://www.researchgate.net/publication/204978228_The_influence_of_the_percentage_of_oxygen_in_the_atmosphere_on_the_thermal_decomposition_of_lignocellulosic_materials?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/41012526_Simulation_of_the_thermogravimetry_analysis_of_three_non-wood_pulps?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-https://www.researchgate.net/publication/24274216_Pyrolysis_characteristics_and_kinetics_of_Arundo_donax_using_thermogravimetric_analysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/223955525_Pyrolysis_Characteristics_and_Kinetics_of_Biomass_Residuals_Mixtures_With_Lignite?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/236691523_Thermogravimetric_characteristics_and_pyrolysis_kinetics_of_Alga_Sagarssum_sp_biomass?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-http://-/?-https://www.researchgate.net/publication/50376300_Thermal_degradation_studies_and_kinetic_modeling_of_cardoon_Cynara_cardunculus_pyrolysis_using_thermogravimetric_analysis_TGA?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/41012526_Simulation_of_the_thermogravimetry_analysis_of_three_non-wood_pulps?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/24274216_Pyrolysis_characteristics_and_kinetics_of_Arundo_donax_using_thermogravimetric_analysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/223789206_Evaluation_of_Sewage_Sludge-Based_Compost_by_FT-IR_Spectroscopy?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/229343954_Characteristics_of_Hemicellulose_Cellulose_and_Lignin_Pyrolysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource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because the lack of correlation would not greatly affect the quality

of the computed activation energy[29]. The calculated activation

energies and the respective correlation factor using KAS and

OFW models are listed inTable 2. The mean active energies calcu-lated from KAS and OFW models are 154.15 and 156.22 kJ/mol,

respectively. Satisfactory agreement with a deviation of 1.32% indi-

cated that the activation energy calculated using KAS and OFW

models were believable. Islam et al. [10]investigated the activation

energy with a deviation below 4% using KAS and OFW models in

pyrolysis of fruit hulls. The high agreement validated the reliability

of calculations and confirmed the predictive power of KAS and

OFW models[30].

The results of kinetic analysis show that activation energy is

highly depended on conversion which means that pyrolysis of soy-

bean straw is a complex process involving different reactions. As

shown inFig. 4, the activation energy values increased smoothly

as the conversion value increased from 0.1 to 0.3, and then kept

at a level within conversion range of 0.30.6, which should be

due to the pyrolysis of hemicellulose and cellulose. As the pyrolysis

proceeded, the activation energy values significantly decreased

from 179.31 to 46.32 kJ/mol when the conversion value further

increased from 0.6 to 0.7 for KAS model. Same trend was founded

in activation energy values calculated from OFW model. The con-

version from 0.6 to 0.7 corresponded to the temperature range

from 673 to 873 K, during the interactive pyrolysis of main lignin

and low residual cellulose occurred. Vamvuka et al. [27] reported

that cellulose decomposition had the highest activation energies

(145285 kJ/mol), whereas lignin decomposition had the lowest(3039 kJ/mol) ones, which could be responsible for the obvious

decrease in activation energy with a conversion range of 0.60.7.

In addition, for conversion around 0.7 and temperatures higher

than 723 K, analysis of the activation energy values showed that

the soybean straw may be assigned to direct gasification, where

the kinetic parameters and rate limiting step could be controlled

by oxygen accessibility towards the residual solid [31]. On the

other hand, as the pyrolysis proceeded, a high heating rate led

the sample to the given conversion in high temperature by the rea-

son of thermal lag. As a consequence of this, the absolute value of

slopes in the plot of lnb=T2 or logb versus 1=T may decrease

slightly, resulting in the decrease of activation energy. Damartzis

et al. [29] reported that the values of activation energy brought

the highest value difference close to 100120 kJ/mol for the pyrol-

ysis of stems. Thus, the lower the activation energy is, the faster

the reaction rate is, because the activation energy is the minimum

energy requirement to start a reaction.

3.4. Determination of pre-exponential factors

The pyrolysis kinetic parameters as a function of conversion

could be determined using CoatsRedfern method. Since KAS and

OFW models are reliable enough, the activation energies calculated

by these models were fitted in order to estimate the

pre-exponential factor and the reaction order. In plot of

ln ln1aT2

h i versus 1=T (for n= 1) or ln 1a

1n1

n1T2

h i versus 1=T (for

n 1), slop gives E=R and the intercept is lnAR=bE where A

can be calculated [29]. Calculated pre-exponential factor values

and the reaction order are listed inTable 3. With raising the pyrol-

ysis heating rate, the pre-exponential factor increased significantly

for both KAS and OFW models because of more complex reactions

involved in a shorter time. Reaction order was calculated through

mean value of activation energy and pre-exponential factor

obtained from KAS model [32].

3.5. Validation

After calculating the kinetic parameters of activation energy,

pre-exponential factor and reaction order estimated using KAS

model and CoatsRedfern method, the accuracy of these parame-

ters should be proven in a satisfactory way. The simulation wascarried out for all of the experimental heating rates from 383 to

1173 K and the results are presented in Fig. 5. The simulation

Table 2

Activation energies (E) for different conversion values obtained by KAS and OFW

models.

Conversion (a) KAS model OFW model Difference (%)

E(kJ/mol) R2 E(kJ/mol) R2

0.1 154.42 0.9188 155.20 0.9270 0.50

0.2 169.56 0.9655 170.07 0.9690 0.30

0.3 175.06 0.9827 175.64 0.9844 0.33

0.4 176.86 0.9862 177.63 0.9876 0.43

0.5 177.54 0.9836 178.58 0.9853 0.58

0.6 179.31 0.9839 179.71 0.9828 0.22

0.7 46.32 0.9779 56.69 0.9871 18.29

Average 154.15 156.22 1.32

0.1 0.2 0.3 0.4 0.5 0.6 0.7

30

60

90

120

150

180

E(kJ.

mol-1

)

Conversion ( )

KAS modelOFW model

Fig. 4. Change in activation energy with progressing conversion for KAS and OFW

models.

Table 3

Calculated pre-exponential factors (A, min1).

KAS model OFW model

E(kJ/mol) b (K/min) n A(min1) E(kJ/mol) b (K/min) n A(min1)

154.15 5 1 8.61E+14 156.22 5 1 9.93E+14

8.19 5.16E+14 8.30 8.92E+14

10 1 3.85E+14 10 1 4.42E+14

11.26 6.73E+14 11.43 1.22E+15

20 1 4.76E+13 20 1 5.47E+13

15.46 4.08E+15 15.72 8.05E+15

30 1 4.26E+13 30 1 4.89E+13

17.04 5.78E+15 17.31 1.09E+16

96 X. Huang et al./ Fuel 169 (2016) 9398

https://www.researchgate.net/publication/50376300_Thermal_degradation_studies_and_kinetic_modeling_of_cardoon_Cynara_cardunculus_pyrolysis_using_thermogravimetric_analysis_TGA?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-https://www.researchgate.net/publication/270053530_Pyrolysis_kinetics_of_raw_and_hydrothermally_carbonized_Karanj_Pongamia_pinnata_fruit_hulls_via_thermogravimetric_analysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/256996254_Pyrolysis_of_orange_waste_A_thermo-kinetic_study?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-https://www.researchgate.net/publication/223955525_Pyrolysis_Characteristics_and_Kinetics_of_Biomass_Residuals_Mixtures_With_Lignite?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/272368653_Thermo-oxidative-kinetic_study_of_cinnamyl_diesters?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/50376300_Thermal_degradation_studies_and_kinetic_modeling_of_cardoon_Cynara_cardunculus_pyrolysis_using_thermogravimetric_analysis_TGA?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/50376300_Thermal_degradation_studies_and_kinetic_modeling_of_cardoon_Cynara_cardunculus_pyrolysis_using_thermogravimetric_analysis_TGA?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-http://-/?-https://www.researchgate.net/publication/227413302_Thermochemical_treatment_of_E-waste_from_small_household_appliances_using_highly_pre-heated_nitrogen-thermogravimetric_investigation_and_pyrolysis_kinetics?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-https://www.researchgate.net/publication/227413302_Thermochemical_treatment_of_E-waste_from_small_household_appliances_using_highly_pre-heated_nitrogen-thermogravimetric_investigation_and_pyrolysis_kinetics?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/256996254_Pyrolysis_of_orange_waste_A_thermo-kinetic_study?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/272368653_Thermo-oxidative-kinetic_study_of_cinnamyl_diesters?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/50376300_Thermal_degradation_studies_and_kinetic_modeling_of_cardoon_Cynara_cardunculus_pyrolysis_using_thermogravimetric_analysis_TGA?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/50376300_Thermal_degradation_studies_and_kinetic_modeling_of_cardoon_Cynara_cardunculus_pyrolysis_using_thermogravimetric_analysis_TGA?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/50376300_Thermal_degradation_studies_and_kinetic_modeling_of_cardoon_Cynara_cardunculus_pyrolysis_using_thermogravimetric_analysis_TGA?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/223955525_Pyrolysis_Characteristics_and_Kinetics_of_Biomass_Residuals_Mixtures_With_Lignite?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==https://www.researchgate.net/publication/270053530_Pyrolysis_kinetics_of_raw_and_hydrothermally_carbonized_Karanj_Pongamia_pinnata_fruit_hulls_via_thermogravimetric_analysis?el=1_x_8&enrichId=rgreq-4f951c52-e449-49fd-a399-f99ce5059154&enrichSource=Y292ZXJQYWdlOzI4Nzc5NjcyNjtBUzozNTAwMzY2NjA2Mzc2OThAMTQ2MDQ2NjY1MTkwMw==http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/?-http://-/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7/25/2019 1-s2.0-S0016236115012648-main

6/7

results were in a good agreement with the experimental data for

conversion values. As described inFig. 5, no obvious effect on the

predictive behavior of the model can be found with the increase

of pyrolysis heating rate in the plots of first-order reaction

(n= 1). In this case, CoatsRedfern method lacks of accuracy for

representing experimental results because the activation energyand the pre-exponential factor were based solely on both heating

rate and pyrolysis temperature. On the other hand, for multistep

reactions (n 1), the pseudo-order n has no physical meaning

but plays an important role to fit parameter as can be thought as

correlation parameter of pyrolysis model.

4. Conclusions

In this study, the pyrolysis of soybean straw has been investi-

gated under argon atmosphere by means of TGA at different heat-

ing rate (5, 10, 20, and 30 K/min). The maximum weight loss of

soybean straw was restricted in the temperature range of 453

673 K owing to the pyrolysis of hemicellulose and cellulose. Simu-

lation of the materials thermal decomposition using obtained

kinetic parameters showed a good agreement with the experimen-

tal data. Taking into account of the low moisture and ash content,

the thermochemical system may provide an insight for future

application of soybean straw as a potential candidate for a resource

of energy and chemicals.

Acknowledgements

This work was subsidized by the Fundamental Research Funds

for the Central Universities (China University of Mining & Technol-

ogy, Grants 2015XKQY05 and 2015QNA18), National Natural

Science Foundation of China (Grant 21206189), Natural Science

Foundation of Jiangsu Province (BK20151141), the PriorityAcademic Program Development of Jiangsu Higher Education

Institutions, and the Strategic Chinese-Japanese Joint Research Pro-

gram (Grant 2013DFG60060).

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0.0

0.2

0.4

0.6

0.8

1.05 K/min

Con

version()

373 473 573 673 773 873 973 1073 1173

0.0

0.2

0.4

0.6

0.8

1.020 K/min

Temperature (K)

n = 1

n = 8.2experiment

10 K/min

30 K/min

Conversion()

373 473 573 673 773 873 973 1073 1173

Temperature (K)

n = 1

n = 11.3experiment

n = 1

n = 15.5experiment

n = 1

n = 17.0experiment

Fig. 5. Simulation of soybean straw pyrolysis using the kinetic data calculated from KAS model.

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