Accepted Manuscript

Comparative evaluation of thermal oxidative decomposition for oil–plant resi-dues via thermogravimetric analysis: Thermal conversion characteristics, ki-netics, and thermodynamics

Jianbiao Chen, Yanhong Wang, Xuemei Lang, Xiu'e Ren, Shuanshi Fan

PII: S0960-8524(17)30920-3DOI: http://dx.doi.org/10.1016/j.biortech.2017.06.033Reference: BITE 18270

To appear in: Bioresource Technology

Received Date: 19 April 2017Revised Date: 5 June 2017Accepted Date: 6 June 2017

Please cite this article as: Chen, J., Wang, Y., Lang, X., Ren, X., Fan, S., Comparative evaluation of thermal oxidativedecomposition for oil–plant residues via thermogravimetric analysis: Thermal conversion characteristics, kinetics,and thermodynamics, Bioresource Technology (2017), doi: http://dx.doi.org/10.1016/j.biortech.2017.06.033

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1

Comparative evaluation of thermal oxidative decomposition for

oil–plant residues via thermogravimetric analysis: Thermal

conversion characteristics, kinetics, and thermodynamics

Jianbiao Chen, Yanhong Wang, Xuemei Lang, Xiu’e Ren, Shuanshi Fan*

Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Ministry Education, School of Chemistry

and Chemical Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China

*Corresponding author: Shuanshi Fan, Tel: +86 2022236581; E-mail address: ssfan@scut.edu.cn

Notes

The authors declare no competing financial interest.

Abstract

Thermal oxidative decomposition characteristics, kinetics, and thermodynamics of rape straw

(RS), rapeseed meal (RM), camellia seed shell (CS), and camellia seed meal (CM) were evaluated

via thermogravimetric analysis (TGA). TG-DTG-DSC curves demonstrated that the combustion of

oil-plant residues proceeded in three stages, including dehydration, release and combustion of

organic volatiles, and chars oxidation. As revealed by combustion characteristic parameters, the

ignition, burnout, and comprehensive combustion performance of residues were quite distinct

from each other, and were improved by increasing heating rate. The kinetic parameters were

determined by Coats-Redfern approach. The results showed that the most possible combustion

mechanisms were order reaction models. The existence of kinetic compensation effect was clearly

observed. The thermodynamic parameters (∆H, ∆G, ∆S) at peak temperatures were calculated

through the activated complex theory. With the combustion proceeding, the variation trends of ∆H,

∆G, and ∆S for RS (RM) similar to those for CS (CM).

Keyword: Oil–plant residues; Thermal oxidative decomposition; Coats–Redfern approach;

Kinetic compensation effect; Thermodynamic parameters

1. Introduction

With increasing awareness of the whole world on energy security, ecological environment,

and global climate change, quickening the development and utilization of renewable energy has

been the general consensus and concerted action by all countries (Zhou et al., 2011; Omer, 2008;

Hassan et al., 2016). As pointed out by the UN’s Intergovernmental Panel on Climate Change

(IPCC), International Energy Agency (IEA), and International Renewable Energy Agency (IREA),

the promotion of renewable energy sources like solar, tide, geothermal, and biomass will be one of

the vital measures to control climate change (Ogle et al., 2014; Bhattacharya et al., 2016; Kravanja

et al., 2015). To establish a clean and low–carbon energy system, the Chinese Government has set

a goal to increase the share of non–fossil energy in total primary energy consumption to 15% by

2020, and to 20% by 2030 (Chen et al., 2015a). Among various renewables, biomass is deemed as

the only carbon–based alternative energy, which can be transformed into a series of high

value–added biofuels or intermediate compounds for energy and chemical industry (Werther et al.,

2000; Yahya et al., 2015). Although CO2 emissions are inevitable during the consumption of

biomass, the same amount of CO2 was fixed from the atmosphere through photosynthesis in the

process of their growth. The potential of CO2 neutral will be gained by using biomass as fuels.

Furthermore, it was also proved that utilizing biomass as an alternative fuel would release less SO2,

NOx, particulate matters, and other gaseous pollutants (Wu et al., 2014; Gai et al., 2015).

Therefore, promoting biomass can not only reduce the reliance on fossil fuels, but also reduce the

atmospheric pollution and CO2 greenhouse effect.

In theory, all biomass resources can be treated as feedstocks to mitigate the shortage of fossil

fuels and environmental pollution. However, there is some controversy when biomass resources

are implemented into energy products (Gai et al., 2015). If vigorously promoting the cultivation of

energy crops, a good deal of arable land will be encroached, and then the production of food will

be affected. Especially in China, the huge challenge of population has forced people to produce as

much grain as possible on the limited arable land rather than develop raw materials for bioenergy.

Alternatively, the agricultural residues have a potential to be feedstocks for the next generation of

biofuels and chemicals, which are inescapable during the production of various crops. According

to some statistics, the predicted availabilities of various agricultural residues are abundant every

year in China, but these resources have not been used reasonably for a long time (Chen et al.,

2015a, c). As two most popular oil–bearing crops, rape and camellia are widely distributed in

southern China. Their processing wastes, i.e. rape straw (RS), rapeseed meal (RM), camellia seed

shell (CS), and camellia seed meal (CM), were casually abandoned or burned in the farmland,

which resulted in serious environmental pollution and resource waste. As a relatively common,

simple, efficient, and cheap thermochemical conversion (TCC) technology, combustion is

responsible for around 97% bioenergy production in the world, which can realize the resource

utilization of agricultural residues.

In order to provide a correct guidance on the design, upgrading, and feasibility of industrial

combustor, combustion (or called as thermal oxidative decomposition) characteristics with regard

to oil–plant residues are of great importance (Xu and Chen, 2013). Up to now, the thermal

oxidative decomposition of various common biomass resources has been widely investigated and

reported. Munir et al. (2009) have compared thermal degradation properties and devolatilization

kinetics of cotton stalk (CS), sugar cane bagasse (SB), and shea meal (SM) in nitrogen and air

atmospheres. It was found that the weight loss rates in an oxidative atmosphere were higher than

those in nitrogen. The reactivity orders for these agricultural residues were ranked by CS>SB>SM

in nitrogen and CS≈SM>SB in air. Based on Chen et al. (2008), the thermal behaviors of rice

straw, corn straw and corncob were evaluated under inert and oxidative conditions. The results

indicated that the combustion pathway of biomass samples was highly depended on the oxygen

concentration and fuel types. Moreover, kinetics analysis showed that most of thermal data can be

simulated by the first order reaction model. Considering that agricultural biomass has a high

content of volatile matter, it can help in improving the combustion performance of low–grade

fuels through co–disposal. Thus, Zhou et al. (2014) investigated the thermal behaviors of coal

gangue, agricultural biomass (peanut shell, wheat straw) and their blends during combustion. The

thermal oxidative degradation process of agricultural biomass could be distinguished as moisture

evaporation, release and combustion of volatile components, and char oxidation. The combustion

performance of coal gangue was clearly improved by blending certain agricultural biomass.

Additionally, Fournel et al., (2015) employed thermodynamic equilibrium and Gibbs free energy

minimization to predict gaseous emissions (CO, CO2, NOx, SO2 and HCl) from the small–scale

combustion of agricultural biomass feedstocks. The comparison between the predicting values of

gaseous emissions and experimental data showed a good consistency. The summary and review of

above research results have fully exhibited the energy resource potential from biomass residues.

Due to the lack of related investigation on oil–plant residues, the information from this work

would be vital and meaningful, which can supply some knowledge on the future development and

utilization of agricultural residues as an alternative resource.

The objective of this article was to quantitatively evaluate thermal conversion characteristics,

kinetic properties, and thermodynamic parameters of four common oil–plant residues (RS, RM,

CS, and CM) in air atmosphere. The thermal analysis experiments were performed in a

thermogravimetric analyzer (TGA) at four different heating rates. On basis of Coats–Redfern

approach, the kinetic triplets of the thermal oxidative decomposition of RS, RM, CS, and CM

were carefully determined. The existence of kinetic compensation effect at different heating rates

was also analyzed. Finally, the thermodynamic parameters, i.e. the changes of enthalpy (∆H),

Gibbs free energy (∆G), and entropy (∆S), of individual conversion zone of RS, RM, CS, and CM

at peak temperature were calculated by the activated complex theory.

2. Materials and methods

2.1. Raw materials and characterization

As two common oil–bearing crops cultivated in southern China, rape and camellia have four

typical processing residues (RS, RM, CS, and CM), which were selected as the feedstocks for

combustion. Before the thermal oxidative tests, these raw materials were carefully milled and

sieved to get uniform particle with a range of 180–350 µm. After above treatments, the samples

were placed in a desiccator for subsequent evaluations.

To characterize the basic properties of raw materials, the proximate analysis and ultimate

analysis were carried out by the Laboratory Analytical Procedures (LAP) proposed by National

Renewable Energy Laboratory (NREL). As shown in Table 1, all oil–plant residues have a high

content of volatile matter (VM) and low content of fixed carbon (FC) and ash (A). It can be seen

that the contents of VM and A of RS (CS) were higher than those of corresponding meal (RM,

CM), but the content of FC of RS (CS) were lower. As for the ultimate analysis, the contents of C,

H, N, and S in RS (CS) were relatively lower than those in RM (CM). Beyond that, the main

chemical functional groups on the surface of four oil–plant residues were detected by the Nicolet

6700 Flex Fourier–transform infrared (FTIR) spectrometer (Thermo Fisher Scientific, USA). The

FTIR spectra of oil–plant residues were gained by the potassium bromide (KBr) method with 0.09

cm–1

resolution and 4000–400 cm–1

wavelength.

2.2. Experimental apparatus and procedures

Thermal oxidative decomposition experiments of RS, RM, CS, and CM were performed on a

STA 449C simultaneous thermogravimetric analyzer (NETZSCH, Germany). For minimizing the

heat and mass transfer influences, the initial weight of oil–plant residues was maintained at a very

small value of 8±0.2 mg. The biomass materials placed into the crucible of TGA were heated up

from indoor temperature to 1273 K at the heating rates of 5, 10, 20, and 40 K min–1

. The oxidative

atmosphere was supplied by the carrier gas of dry air. The air flow rate was kept at 80 mL min–1

,

which was high enough to effectively eliminate secondary gas-fuels reactions. For each test, the

control experiment was conducted for TG baseline correction without sample in the crucible. All

the thermal oxidative decomposition experiments were repeated at least twice to guarantee the

reproducibility of thermal data.

2.3. Characterization of combustion performance

For quantitatively evaluating the effects of biomass type and heating rate on the combustion

performance of oil–plant residues, the combustion characteristic parameters including the ignition

temperature (Ti), peak temperature (Tp), burnout temperature (Tb), maximum weight loss rate (–Rp),

average weight loss rate (–Rv), and temperature interval at the half value of –Rp (∆T1/2) were

required. The detailed definitions of these parameters can be referred to the previous publications

(Meng et al., 2013; Chen et al., 2015b).

Moreover, some combustion indices were also recommended to reflect the ignition, burnout,

and comprehensive combustion performances of oil–plant residues under disparate conditions.

These indices are the volatile matter release index (Dv) (Liu et al., 2013), ignition index (Ci) (Li et

al., 2011), burnout index (Cb) (Li et al., 2011), and comprehensive combustibility index (S) (Liu et

al., 2013). They can be described as the functions of characteristic temperatures and weight loss

rates (Chen et al., 2015b).

p

v

v p 1/2

RD

T T T

−=

× × ∆ (1)

p

i

i p

RC

t t

−=

× (2)

pb

1/2 p b

RC

t t t

−=

∆ × × (3)

vp

2

i b

( ) ( )R RS

T T

− × −=

× (4)

where ti, tp, tb, and ∆t1/2 represent the ignition time, peak time, burnout time, and time interval at

the half value of –Rp, respectively. Tv represents the initial devolatilization temperature. The

detailed definitions for Ti, Tp, Tb, –Rp, –Rv, and ∆T1/2 are same as above.

3. Results and discussion

3.1. FTIR analysis of raw materials

The surface chemical structure of raw oil–plant residues (RS, RM, CS, and CM) was further

investigated by FTIR technology. On basis of literature, the characteristic functional groups in the

FTIR spectra can be divided into several regions (Gai et al., 2015; Li et al., 2015a, b): (1)

3700–3000 cm–1

, O–H stretching vibration for moisture and carbohydrate; (2) 2975–2845 cm–1

,

C–H stretching vibration for methyl and methylene; (3) 1750–1680 cm–1

, C=O stretching

vibration for peptide bonds; (4) 1670–1620 cm–1

, C=C for alkenes; (5) 1600, 1585, 1500, and

1450 cm–1

, stretching vibration for aromatics; (6) 1500–1350 cm–1

, C–H bending vibration for

aliphatic hydrogen; (7) 1350–1000 cm–1

, C–O stretching vibration for secondary alcohols, C–C

stretching vibration for skeletal vibration, C–N stretching vibration for peptide bonds; (8)

1000–650 cm–1

, C–H out–plane deformation for aromatics hydrogen.

The FTIR spectra of RS, RM, CS, and CM were available in Supplementary data. It can be

observed that the locations of adsorption peaks for four oil–plant residues were almost consistent,

meaning that the functional group types of RS, RM, CS, and CM were extremely close. The only

exception was the adsorption peak of –CH2 symmetric stretching variation at 2850 cm–1

. As seen

from FTIR spectra, the adsorption peak of –CH2 at 2850 cm–1

for oil–plant meals was negligible,

but the same functional group in RS and CS was obvious. The prominent adsorption peaks of O–H

at 3700–3000 cm–1

indicated the presence of moisture and carbohydrate. Compared with the

absorption peak of free O–H at 3650–3580 cm–1

, the O–H peaks in oil–plant residues appeared at

lower wavenumbers, which could be ascribed to hydrogen–bond interactions. The stretching

vibration of C–H at 2925 cm–1

and bending vibration of C–H at 1440 cm–1

were due to the

presence of methyl (–CH3). The carbonyl stretching C=O appeared at 1740 cm–1

suggested the

presence of proteins in oil–plant residues, where existed peptide bonds (–CONH–). The absorption

peaks at 1620–1650 cm–1

were related to C=C stretching vibration of the lignin compounds. In

addition, the absorption peaks of oil–plant residues at around 1050 cm–1

were ascribed to C–O

stretching in carbohydrates.

3.2. Thermal oxidative decomposition characteristics

The thermal oxidative decomposition process of RS, RM, CS, and CM in air atmosphere at the

heating rate of 10 K min–1

was elaborated in the present section with representative TG-DTG-DSC

curves [i.e. thermogravimetry (TG), differential thermogravimetry (DTG), and differential

scanning calorimetry (DSC)]. As shown in Fig. 1, the thermal events from the combustion of

various oil–plant residues could be roughly divided into three stages: an initial weight loss of

around 7.0 wt. % due to the dehydration and release of light volatiles at the temperature below

423.1–447.5 K (depending on biomass species), a major weight reduction of 48.5–62.9 wt. % at

the second stage (depending on biomass species) corresponded to the release and combustion of

carbon containing volatiles, and a continuous reduction in the weight of agricultural residues at the

temperature above 631.1–710.8 K (depending on biomass species) where chars oxidizing occurred.

Further, the devolatilization and oxidization process of RM and CM at the second stage could be

subdivided into two and three zones, respectively. As reported in literature (Xu and Chen, 2013;

Quan et al., 2016), biomass materials mainly consist of several components, such as protein,

hemicellulose, cellulose, and lignin. Thus, different weight reduction zones during the second

stage might be attributed to the release and combustion of these components.

In this study, the heat events during the thermal oxidative decomposition of RS, RM, CS, and

CM in air at 10 K min–1

were also evaluated by DSC technology. As shown in Fig. 1, the locations

of endothermic/exothermic peaks in DSC were slightly greater than those of weight loss rate peaks

in DTG. It can be observed that there existed a slight endothermic peak at the first stage. This was

because the contents of moisture and light volatiles in samples are small, and their evaporations

needed absorb external heat. With the combustion proceeding, two noticeable exothermic peaks

appeared, corresponded to the heat released from volatiles combustion and chars oxidizing,

respectively. As for RS, the exothermic peak heights of volatiles combustion and chars oxidizing

were identical, meaning that the contribution of heat from two stages was same. For other three

samples, the heat released from the combustion was highly depended on chars oxidizing.

The thermal oxidative degradation characteristic parameters of RS, RM, CS, and CM in air at

10 K min–1

were listed in Table 2. For convenience, the thermal events during the combustion

were subdivided into five zones on basis of above analysis, and took no account of the moisture

release zone. Overall, the characteristic combustion temperatures (Ti, Tp3, Tp5, and Tb) of RS were

lower than those of RM. Similarly, the characteristic temperatures of CS were lower than those of

CM. With respect to the characteristic combustion rates (–Rp3, –Rp5, and –Rv), the values for RS

(CS) were greater than those for RM (CM). Since RS and CS had lower combustion temperatures

and higher combustion rates, the devolatilization, ignition, burnout, and comprehensive

combustion performance were no doubt better than corresponding oil–plant meal.

3.2.2. Effect of heating rate

The effect of heating rate (β) on the thermal oxidative decomposition process of RS, RM, CS,

and CM in air was investigated with four disparate heating rates of 5, 10, 20, and 40 °C min–1

. As

depicted in Fig. 2, increasing β only moved TG curves of various oil–plant residues to a higher

temperature region, without affecting the pattern of the thermal oxidative decomposition. These

temperature shifts were the thermal hysteresis. It was due to better heat transfer effect would be

gained at lower β. The similar findings have also been reported in the combustion of low–lipid

microalgae (Gai et al., 2015), Tetraselmis suecica (Tahmasebi et al., 2013), fermentation residue

(Du et al., 2013), Chlorella vulgaris (Chen et al., 2011), rice straw (Xie and Ma, 2013), and

oil–palm biomass (Idris et al., 2012) at different β values. As shown in Fig. 3, with β increasing,

the DTG curves extended to higher temperatures, and their values increased evidently.

Taking RS as an example, the variation extents of characteristic combustion temperatures (Ti,

Tp3, Tp5, and Tb) and characteristic combustion rates (–Rp3, –Rp5, and –Rv) at diverse β values were

quantitatively identified in Table 3. As expected, when β went up from 5 to 40 K min–1

, Ti, Tp3, Tp5,

and Tb shifted to a higher temperature region, and –Rp3, –Rp5, and –Rv increased. Additionally, the

combustion performance of solid fuels could be further evaluated by several combustion indices

(Dv, Ci, Cb, and S). It can be seen that the values of Dv were 2.01×10–7

, 4.52×10–7

, 1.04×10–6

,

1.43×10–6

wt. % min–1

K–3

at 5, 10, 20, and 40 K min–1

, respectively. The results suggested that

the higher β would result in a more concentrated combustion region of chars and better burnout

performance. With β increasing, the indices of Ci, Cb, and S increased significantly, implying that a

better ignition and burnout performance could be obtained at higher β. These influences of β on

the thermal conversion characteristics of various solid fuels in the TGA were very common, but

data for thermal oxidative decomposition of oil–plant residues have not yet been reported.

3.3. Thermal oxidative decomposition kinetics analysis

The thermal oxidative decomposition kinetics analysis of oil–plant residues can acquire more

information from the thermogravimetric experiments (Zhu et al., 2015). The kinetic parameters

can be employed in the prediction of thermal conversion characteristics and optimization of the

combustion process. Using thermal data, the thermal oxidative decomposition process of RS, RM,

CS, and CM can be simulated by the decomposition rate equation (Vyazovkin et al., 2011).

2( ) ( ) (O ) exp ( )

dx dx A Ek T f x h f x

dt dT RTβ

= ⋅ ⋅ ⇒ = ⋅ − ⋅

(5)

where dx/dt represents the decomposition rate, which can be calculated as a function of the rate

constant k(T)=k0·exp(–E/RT), reaction model f(x), and O2 partial pressure function h(O2). x is the

conversion degree for individual thermal oxidative decomposition zone, x=(wi–w)/(wi–wf) where

wi, w, and wf are the initial weight, weight at time t, and final weight of each zone, respectively. A,

E, R, and T represent the apparent pre–exponential factor A=k0·h(O2), apparent activation energy,

universal gas constant, and absolute temperature, respectively. For non–isothermal experiments

conducting at constant heating rate β=dT/dt, the decomposition rate equation can be simplified and

rearranged to the right part of Eq. (5).

The integral form of Eq. (5) can be written as follows (Vlaev et al., 2008):

00( ) exp( )

( )

x T

T

dx A Eg x dT

f x RTβ= = −∫ ∫ (6)

where g(x) is the integral form of reaction model.

The integral Coats–Redfern equation was used to approximately estimate the kinetic data of

the thermal oxidative decomposition process of four oil–plant residues. Eq. (6) can be rewritten

(Du et al., 2014; Chen et al., 2015b)

2

( ) 2ln ln 1

g x AR RT E

T E E RTβ

= − −

(7)

The value of 2RT/E for the thermal oxidative decomposition of oil–plant residues was very

close to zero, thus 1–2RT/E≈1. Eq. (7) can be approximated as:

2

( )ln ln

g x AR E

T E RTβ

= −

(8)

Based on Eq. (8), once the most appropriate kinetic model in Supplementary data (Vlaev et

al., 2008) was selected, the regression line of ln[g(x)/T2] against 1/T gave the highest correlation

coefficient (R2). The values of E and A were then determined.

Prior to thermal oxidative decomposition kinetics analysis, the conversion degrees x of the

individual zone of RS, RM, CS, and CM were recalculated as a function of reaction temperature.

The values of x utilized for kinetics analysis were in the range of 0.05–0.95. Taking the thermal

data obtained at 10 K min–1

as the examples, the regression lines of ln[g(x)/T2] against 1/T for

various thermal oxidative degradation zones of RS, RM, CS, and CM were drawn. As illustrated

in Table 4, the highest R2 values for various zones were in the range of 0.9873–0.9991, suggesting

that the kinetic models selected were very suitable. The best kinetic models for various thermal

oxidative decomposition stages of RS and CS were same, in order, (1–x)3/2

, (1–x)2, (1–x)

2. The E

values of the first and third stages from the thermal oxidative decomposition of RS and CS were

almost identical, but the second stage had a significant difference. With regard to RM and CM, the

most possible mechanisms for moisture release (zone 1) and chars oxidation (zone 5) were (1–x)3/2

and (1–x)3/4

, respectively. On basis of Section 3.1, the thermal oxidative degradation process of

oil–plant meals at the second stage could be subdivided into the combustion of hemicellulose,

cellulose, and lignin, respectively. Since the combustion process of cellulose and lignin in RM was

indistinguishable, it meant that these two zones had a same reaction mechanism. It could be seen

that the combustion models for hemicellulose and lignin in RM and CM were identical, but the

models for cellulose were different. Comparing the values of E between RM and CM, it was found

that the values for the release of moisture and light volatiles were almost identical, but the values

for other zones were quite distinct. It was closely related to the contents of chemical components

in RM and CM. To validate the exactness of above kinetics analysis, the fit between experimental

data and theoretical values was checked. Based on Eq. (5), the value of x can be calculated as a

function of reaction temperature T through using the kinetic parameters in Table 4. As seen from

Fig. 4, the calculated line and experimental data were almost similar, which indicated that the

kinetic triplets can accurately predict and reproduce the thermal oxidative degradation process of

RS, RM, CS, and CM.

The kinetic compensation effects (KCE) during the thermal oxidative decomposition of RS,

RM, CS, and CM at distinct heating rates were displayed in Fig. 5. Once the kinetics analysis is

carried out under diverse kinetic models (Budrugeac and Segal, 2001), heating rates (Mui et al.,

2010; Açıkalın, 2012), or reaction atmospheres (Wang et al., 2012), KCE will always exist.

According to KCE, increasing the value of E will lead to a reduction of the decomposition rate at

any temperature, and be partially or completely compensated by the value of A. The relationship

between A and E can be expressed by lnA=m*E+n, where the values of m and n are compensation

coefficients (Budrugeac and Segal, 2001; Mui et al., 2010). As a result, the regression lines of lnA

versus E for different thermal oxidative degradation zones of RS, RM, CS, and CM were shown in

Fig. 5. Data of R2 depicted in Fig. 5 were very high in the range of 0.9808–0.9999, confirming the

existence of KCE.

3.4. Thermodynamic parameters of oil–plant residues combustion

The thermodynamic parameters of the thermal oxidative decomposition process of oil–plant

residues can be determined by the activated complex theory (Boonchom and Puttawong, 2010;

Vlaev et al., 2008). The general formula is expressed as:

B pexp

e k T SA

h R

χ ∆ =

(9)

where A is the pre–exponential factor calculated by Coats–Redfern approach. e, χ, kB, and h are

the Neper number, transition factor, Boltzmann constant, and Plank constant, respectively; Tp is

the peak temperature during the thermal oxidative decomposition. The change of entropy (∆S) can

be calculated by the following equation.

B p

lnAh

S Re k Tχ

∆ =

(10)

Since

∆H=E–RTp (11)

where the value of E was predetermined by Coats–Redfern approach; The change of enthalpy

(∆H) is the state function of a chemical reaction that reflects heat absorbed or released under

constant pressure (Li et al., 2015a, b); The change of Gibbs free energy (∆G) for the activated

complex formation from the reagents can be estimated by the thermodynamic equation (Xu and

Chen, 2013; Li et al., 2015a).

∆G=∆H–Tp∆S (12)

The thermodynamic parameters from the thermal oxidative degradation of oil–plant residues

were very significant to provide information on the design, upgrading, and scaling of industrial

combustors, as well as the optimization of operations. Table 5 illustrated the changes of enthalpy

∆H, Gibbs free energy ∆G, and entropy ∆S from the thermal oxidative decomposition of RS, RM,

CS, and CM at Tp, of 10 K min–1

. The parameter of ∆H revealed the energy barrier between the

activated complex and reagents. If the ∆H value is small, the formation of the activated complex is

more favored. It was found that the values of ∆H at Tp1, Tp3, and Tp5 for RS were greater than those

for RM, meaning that the activated complex formed during the thermal oxidative degradation of

RM was more favored. For CS and CM, the values of ∆H at Tp1 and Tp5 for CS were greater, but

the ∆H value at Tp3 was smaller.

The thermal oxidative decomposition of RS, RM, CS, and CM occurred at Tp1 was mainly

due to the release of moisture and light volatile components. With the oxidizing temperature

increasing, the chemical bonds began to break. The parameter of ∆G reflects the increase in total

energy of the system at the approach of the reagents and the formation of activated complex (Li et

al., 2015a, b). As demonstrated in Table 5, the positive ∆G and negative ∆S at Tp1, Tp2, Tp3, and Tp5

validated that the thermal oxidative degradation of RS, RM, CS, and CM in the TGA was a

non–spontaneous process. The only exception is the positive value of ∆S at Tp4 for the thermal

oxidative degradation of CM. It could be seen that the values of ∆G at Tp1, Tp3, and Tp5 for the

thermal oxidative decomposition of RS were smaller than those of RM. With regard to the

combustion of CS and CM, the values of ∆G for RS were also smaller.

As reported in the literature, the parameter of ∆S is the measure of disorder or randomness of

energy and matter in a system (Xu and Chen, 2013). The absolute values of ∆S at Tp1, Tp3, and Tp5

for the thermal oxidative degradation of RS were 160.61, 59.46, and 9.48 J mol–1

s–1

, respectively.

For the thermal oxidative degradation of CS, the absolute values of ∆S at Tp1, Tp3, and Tp5 were

170.14, 169.05, and 28.79 J mol–1

s–1

, respectively. Since the absolute values of ∆S from the

thermal oxidative degradation of CS were slightly higher than those from the combustion of RS,

meaning that more energy was required for CS to reduce the disorder degree. The absolute values

of ∆S at Tp1 and Tp5 for the thermal oxidative degradation of RM were smaller than those of CM.

On the contrary, the absolute ∆S values at Tp2 and Tp3 for the release and combustion of carbon

containing volatiles in RM were greater than those for CM. The low ∆S value revealed that the

samples have just passed through some physical or chemical aging processes, transforming them

to a state that was close to their own thermodynamic equilibrium. As shown in Table 5, with the

thermal oxidative degradation of RS and CS proceeding, the ∆S values became greater and greater,

which indicated that the state of samples was gradually far from their thermodynamic equilibrium.

However, the values of ∆S for RM and CM were smaller and smaller after dehydration, indicating

the state of samples was gradually close to their thermodynamic equilibrium.

4. Conclusions

TG-DTG-DSC experiments revealed that the thermal oxidative degradation of oil-plant

residues could be distinguished as moisture evaporation, release and combustion of volatiles,

and chars oxidation. The thermal characteristics were highly influenced by biomass species

and heating rates as revealed by several combustion characteristic parameters. Kinetics

analysis results indicated that the most appropriate mechanisms for various thermal zones

were order reaction models. The calculated kinetic parameters could well simulate thermal

data, and the kinetic compensation effect was evident. As for thermodynamic analysis, the

variation trends of ∆H, ∆G, and ∆S at peak temperatures for RS (RM) were similar to CS (CM).

Acknowledgement

Grateful acknowledgement is made to associate Professor Lin Mu and Professor Hongchao Yin

(Dalian University of Technology) whose constructive comments have helped to improve the

quality of this paper. Financial support for this work that has been provided by the Fundamental

Research Funds for the Central Universities (2017BQ062) is also gratefully acknowledged.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version.

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List captions of Figures:

Fig. 1. TG–DTG–DSC curve of thermal oxidative decomposition of (a) RS, (b) RM, (c) CS, and

(d) CM at 10 K min–1

Fig. 2. TG curves for thermal oxidative decomposition of (a) RS, (b) RM, (c) CS, and (d) CM at 5,

10, 20, and 40 K min–1

Fig. 3. DTG curves for thermal oxidative decomposition of (a) RS, (b) RM, (c) CS, and (d) CM at

5, 10, 20, and 40 K min–1

Fig. 4. Experimental x (scatter) and calculated x (line) for thermal oxidative decomposition of (a)

RS, (b) RM, (c) CS, and (d) CM at 10 K min–1

Fig. 5. Kinetic compensation effect analysis for thermal oxidative decomposition of (a) RS, (b)

RM, (c) CS, and (d) CM at different heating rates

300 450 600 750 900 1050 1200 1350 1500

0

20

40

60

80

100

Temperature /K

TG

/w

t. %

-8

-6

-4

-2

0

DS

C /

mW

mg

-1

DT

G /

wt.

% m

in-1

0.0

3.5

7.0

10.5

TG

DTG

DSC

300 450 600 750 900 1050 1200 1350 1500

0

20

40

60

80

100

Temperature /K

TG

/ w

t. %

-3.6

-3.0

-2.4

-1.8

-1.2

-0.6

0.0

DS

C /

mW

mg

-1

DT

G /

wt.

% m

in-1

TG

DTG

DSC

0

2

4

6

8

10

12

14

300 450 600 750 900 1050 1200 1350 1500

0

20

40

60

80

100

Temperature /K

TG

/w

t. %

-6.0

-4.5

-3.0

-1.5

0.0

DS

C /

mW

mg

-1

DT

G /

wt.

% m

in-1

TG

DTG

DSC

0

3

6

9

12

300 450 600 750 900 1050 1200 1350 1500

0

20

40

60

80

100

Temperature /K

TG

/w

t. %

-5

-4

-3

-2

-1

0

DS

C /

mW

mg

-1

DT

G /

wt.

% m

in-1

TG

DTG

DSC

0

3

6

9

12

Fig. 1. TG–DTG–DSC curve of thermal oxidative decomposition of (a) RS, (b) RM, (c) CS, and

(d) CM at 10 K min–1

(b) (a)

(c) (d)

300 450 600 750 900 1050 1200

0

20

40

60

80

100

1 5 K min-1

2 10 K min-1

3 20 K min-1

4 40 K min-1

4

TG

/w

t. %

Temeprature /K

1

300 450 600 750 900 1050 1200

0

20

40

60

80

100

1 5 K min-1

2 10 K min-1

3 20 K min-1

4 40 K min-1

TG

/w

t. %

Temperature /K

1

4

300 450 600 750 900 1050 1200

0

20

40

60

80

1001 5 K min

-1

2 10 K min-1

3 20 K min-1

4 40 K min-1

TG

/w

t. %

Temperature /K

1

4

300 450 600 750 900 1050 1200

0

20

40

60

80

100

1 5 K min-1

2 10 K min-1

3 20 K min-1

4 40 K min-1

TG

/w

t. %

Temperature /K

1

4

Fig. 2. TG curves for thermal oxidative decomposition of (a) RS, (b) RM, (c) CS, and (d) CM at

heating rates of 5, 10, 20, and 40 K min–1

(a) (b)

(c) (d)

300 450 600 750 900 1050 1200

-40

-30

-20

-10

0

1 5 K min-1

2 10 K min-1

3 20 K min-1

4 40 K min-1

DT

G /

wt.

% m

in-1

Temperature /K

1

4

300 450 600 750 900 1050 1200

-20

-15

-10

-5

0

1 5 K min-1

2 10 K min-1

3 20 K min-1

4 40 K min-1

DT

G /

wt.

% m

in-1

Temperature /K

1

4

300 450 600 750 900 1050 1200

-40

-32

-24

-16

-8

0

1 5 K min-1

2 10 K min-1

3 20 K min-1

4 40 K min-1

DT

G /

wt.

% m

in-1

Temperature /K

1

4

300 450 600 750 900 1050 1200

-30.0

-22.5

-15.0

-7.5

0.0

1 5 K min-1

2 10 K min-1

3 20 K min-1

4 40 K min-1

DT

G /

wt.

% m

in-1

Temperaure /k

1

4

Fig. 3. DTG curves for thermal oxidative decomposition of (a) RS, (b) RM, (c) CS, and (d) CM at

5, 10, 20, and 40 K min–1

(a) (b)

(c) (d)

300 400 500 600 700 800 900 1000 11000.0

0.2

0.4

0.6

0.8

1.0

Calculated x

Experimental x

Con

ver

sion

deg

ree

/%

Temperature /K

300 400 500 600 700 800 900 1000 11000.0

0.2

0.4

0.6

0.8

1.0

Calculated x

Experimental x

Co

nv

ersi

on

deg

ree

/%

Temperature /K

300 400 500 600 700 800 900 1000 11000.0

0.2

0.4

0.6

0.8

1.0

Calculated x

Experimental x

Co

nv

ersi

on

deg

ree

/%

Temperature /K

300 400 500 600 700 800 900 1000 11000.0

0.2

0.4

0.6

0.8

1.0

Calculated x

Experimental x

Co

nv

ersi

on

deg

ree

/%Temperature /K

Fig. 4. Experimental x (scatter) and calculated x (line) for thermal oxidative decomposition of (a)

RS, (b) RM, (c) CS, and (d) CM at 10 K min–1

(a) (b)

(c) (d)

20 40 60 80 100 120 140 160 180 200

5

10

15

20

25

30 y=0.096x+11.429

R2=0.9902

y=0.131x+6.978

R2=0.9922

Zone 1

Zone 2

Zone 3

lnA

/ln

[s-1]

E /kJ mol-1

y=0.313x-3.725

R2=0.9855

30 60 90 120 150 180 210 240 2700

10

20

30

40

y=0.156x-7.046

R2=0.9963

Zone 1

Zone 2

Zone 3

Zone 4

y=0.270x-7.431

R2=0.9989

y=0.178x-2.919

R2=0.9996

lnA

/ln

[s-1]

E /kJ mol-1

y=0.327x-5.147

R2=0.9980

40 60 80 100 120 140 160 180 200 220

5

10

15

20

25

30

y=0.180x-7.763

R2=0.9983

y=0.166x-1.723

R2=0.9907

Zone 1

Zone 2

Zone 3

lnA

/ln

[s-1]

E /kJ mol-1

y=0.317x-3.956

R2=0.9808

0 30 60 90 120 150 180 210 240 270 300

0

10

20

30

40

50

y=0.158x-6.687

R2=0.9965

y=0.194x-3.850

R2=0.9999

y=0.281x-8.417

R2=0.9891

y=0.306x-17.498

R2=0.9933

Zone 1

Zone 2

Zone 3

Zone 4

Zone 5

lnA

/ln

[s-1]

E /kJ mol-1

y=0.232x-2.370

R2=0.9887

Fig. 5. Kinetic compensation effect analysis for thermal oxidative decomposition of (a) RS, (b)

RM, (c) CS, and (d) CM at different heating rates

(a) (b)

(c) (d)

List captions of Tables:

Table 1 Proximate analysis and ultimate analysis for four typical oil–plant residues

Table 2 Thermal oxidative decomposition characteristic parameters of oil–plant residues at 10 K

min–1

Table 3 Thermal oxidative decomposition characteristic parameters of RS at 5, 10, 20, and 40 K

min–1

Table 4 Kinetic parameters of the thermal oxidative decomposition of RS, RM, CS, and CM at 10

K min–1

Table 5 Thermodynamic parameters of the thermal oxidative decomposition of RS, RM, CS, and

CM at 10 K min–1

Table 1

Ultimate analysis and proximate analysis for four typical oil–plant residues

Samples Rape straw Rapeseed meal Camellia seed shell Camellia seed meal

Pictures

Proximate analysis (wt. %, arc)

Moisture (M) 6.32 4.07 4.94 4.38

Volatile matter (VM) 71.55 77.33 69.71 74.05

Fixed carbon (FC) 17.05 11.62 21.10 18.96

Ash (A) 5.08 6.98 4.25 2.61

Ultimate analysis (wt. %, dafa)

Carbon (C) 44.39 48.62 46.05 50.64

Hydrogen (H) 6.47 7.45 6.08 7.12

Nitrogen (N) 0.54 5.50 0.37 1.16

Sulfur (S) 0.36 0.97 0.17 0.31

Oxygen (O)b 48.24 37.46 47.33 40.77

a Dry ash free basis.

b Calculated by difference.

c As received basis.

Table 2

Thermal oxidative decomposition characteristic parameters of oil–plant residues at 10 K min–1

Samples Tia

Tpb

–Rpc

–Rvd

Tbe 107×Dv

f 103×Cig 104×Cb

h 108×Si

Tp2 Tp3 Tp4 Tp5 –Rp2

–Rp3

–Rp4

–Rp5

RS 512.3 562.1 675.3 7.73 2.97 3.46 756.5 4.52 12.93 10.09 13.47

RM 493.2 484.4 596.2 810.2 1.69 3.41 2.71 1.83 932.2 1.49 5.53 2.05 2.75

CS 492.2 549.5 715.3 6.33 2.70 2.54 814.7 4.60 12.22 8.55 8.15

CM 518.3 483.9 554.5 616.5 797.9 1.41 4.28 3.72 2.85 2.29 877.7 2.98 7.16 4.91 4.16

aTi, the ignition temperature, K.

bTp, the peak temperature of each zone, K; Tp1, Tp2, Tp3, Tp4, and Tp5 are the peak temperature of

the first, second, third, fourth, and fifth peak, respectively.

c–Rp, the maximum weight loss rate of each zone, wt. % min

-1; –Rp1, –Rp2, –Rp3, –Rp4,and –Rp5 are

the value of the first, second, third, fourth, and fifth peak, respectively.

d–Rv, the average weight loss rate, wt. % min

-1.

eTb, the burnout temperature, K.

fDv, the volatile matter release index, wt. % min

-1 K

-3.

gCi, the ignition index, wt. % min

-3.

hCf, the burnout index, wt. % min

-4.

iS, the comprehensive combustibility index, wt. %

2 min

-2 K

-3.

Table 3

Thermal oxidative decomposition characteristic parameters of RS at 5, 10, 20, and 40 K min–1

βa Ti

Tp

–Rp –Rv Tb 107×Dv

103×Ci 103×Cb

107×S Tp1 Tp3 Tp4 Tp5 –Rp1

–Rp3

–Rp4 –Rp5

5 508.5 567.9 655.7 4.83 1.71 1.86 736.9 2.01 1.99 0.06 0.47

10 512.3 562.1 675.3 7.73 2.97 3.46 756.5 4.52 12.93 1.01 1.35

20 516.5 570.7 672.7 17.27 5.42 6.81 767.7 10.40 109.6 18.57 5.74

40 522.3 589.1 724.3 39.50 4.22 7.32 1033.9 14.29 721.1 186.7 10.25

aβ, heating rate, K min

–1.

Table 4

Kinetic parameters of the thermal oxidative decomposition of RS, RM, CS, and CM at 10 K min–1

Samples Temperature range

∆T /K

Activation energy

E /kJ mol–1

Pre–exponential

factor A /s–1

Kinetic

model f(x)

Correlation

coefficient R2

RS 298.15–432.71 49.05 8.291×104 (1–x)3/2 0.9991

432.91–631.11 135.46 2.497×1010 (1–x)2 0.9977

631.31–756.51 196.42 1.223×1013 (1–x)2 0.9889

RM 298.15–436.71 38.41 1.439×103 (1–x)3/2 0.9991

436.91–493.21 130.10 1.353×1012 (1–x)3/4 0.9984

493.41–710.81 74.62 1.976×104 (1–x)2 0.9968

711.01–932.21 107.33 1.602×104 (1–x)3/4 0.9939

CS 298.15–423.07 45.18 2.616×104 (1–x)3/2 0.9973

423.27–673.67 74.92 4.602×104 (1–x)2 0.9964

673.87–814.67 199.64 1.270×1012 (1–x)2 0.9935

CM 298.15–447.52 36.49 3.924×102 (1–x)3/2 0.9947

447.72–498.32 140.82 1.565×1013 (1–x)3/4 0.9973

498.52–492.92 130.69 1.493×1010 (1–x)3/2 0.9970

593.12–657.32 290.81 6.006×1022 (1–x)2 0.9873

657.52–877.72 77.80 2.138×102 (1–x)3/4 0.9980

Table 4

Thermodynamic parameters of the thermal oxidative decomposition of RS, RM, CS, and CM at 10

K min–1

Parameters Units RS RM CS CM

Tp1 K 358.50 355.40 355.90 366.50

∆H1 kJ mol–1 46.07 35.46 42.22 33.44

∆G1 kJ mol–1 103.65 104.49 102.77 108.69

∆S1 J mol–1 K–1 –160.61 –194.24 –170.14 –205.30

Tp2 K 484.40 483.90

∆H2 kJ mol–1 126.07 136.80

∆G2 kJ mol–1 138.19 139.05

∆S2 J mol–1 K–1 –25.03 –4.66

Tp3 K 562.10 596.20 549.50 554.50

∆H3 kJ mol–1 130.79 69.66 67.16 126.08

∆G3 kJ mol–1 164.21 175.05 160.05 161.36

∆S3 J mol–1 K–1 –59.46 –176.76 –169.05 –63.62

Tp4 K

616.50

∆H4 kJ mol–1

285.68

∆G4 kJ mol–1

176.68

∆S4 J mol–1 K–1

176.81

Tp5 K 675.30 810.20 715.30 797.90

∆H5 kJ mol–1 190.81 100.59 193.69 71.17

∆G5 kJ mol–1 197.21 247.29 214.29 244.17

∆S5 J mol–1 K–1 –9.48 –181.06 –28.79 –216.82

Highlights:

� Thermal oxidative degradation of four oil–plant residues was studied by TG–DTG–DSC

� Thermal behaviors of oil–plant residues were affected by species and heating rates

� Kinetics was analyzed by CR approach, and the kinetic compensation effect was evident

� Thermodynamic parameters (∆H, ∆G, ∆S) were obtained by the activated complex theory.