Combustion behaviors of three bamboo residues: Gas emission, kinetic, reaction mechanism and optimization patterns

Journal of Cleaner Production 235 (2019) 549e561

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Combustion behaviors of three bamboo residues: Gas emission, kinetic, reaction mechanism and optimization patterns Jinwen Hu a, Youping Yan a, Fatih Evrendilek b, c, Musa Buyukada d, Jingyong Liu a, * a

Guangzhou Key Laboratory Environmental Catalysis and Pollution Control, Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou, 510006, China b Department of Environmental Engineering, Bolu Abant Izzet Baysal University, Bolu, 14052, Turkey c Department of Environmental Engineering, Ardahan University, Ardahan, 75002, Turkey d Department of Chemical Engineering, Bolu Abant Izzet Baysal University, Bolu, 14052, Turkey

a r t i c l e i n f o

a b s t r a c t

Article history: Received 4 April 2019 Received in revised form 27 June 2019 Accepted 29 June 2019 Available online 2 July 2019

This study focused on the assessment of gas emissions and bioenergy potential of the combustions of bamboo leaves (BL), shoot leaves (BSL) and branches (BB) in the air atmosphere. The main combustion stage of the three residues occurred at between 200 and 600
C, with three peaks of mass loss. The pattern of mass loss rate was BSL > BB > BL, with BSL having the best combustion characteristic parameters. The main evolved gases were CO2 and H2O at between 200 and 600
C. Organic gaseous compounds were decomposed in the range of 200e400
C. Air pollutants were produced in the range of 200e500
C. N-containing gas pollutants were 0.01e0.1 times CO2, while SO2 was produced in a very small amount. BL produced more gas pollutants than did BSL and BB, while the controls over the gas pollutants should be more concentrated in the range of 200e400
C. The joint optimizations of derivative thermogravimetry, differential scanning calorimetry, remaining mass, and conversion degree showed 653.2
C and 5
C/min as the optimum operational conditions for bioenergy utilization, while BB performed as the best feedstock. Among three iso-conversion methods used to estimate activation energy, Flynn-Wall-Ozawa led to best correlation. The Coats-Redfern method pointed to the second order reaction model (f (a) ¼ (1a)2) as the most likely reaction mechanism. Overall, the bamboo residues were promising as the environmentally friendly and renewable feedstock. Our findings can provide the basis for bioenergy generation, pollution control, and optimal efficiency when the industrial-scale combustions of the bamboo residues are adopted. © 2019 Published by Elsevier Ltd.

Handling Editor: Sandro Nizetic Keywords: Bamboo residues Combustion Thermogravimetric analysis TG-MS Renewable energy

1. Introduction Currently, the global mean dependence on fossil fuels is about 78.4% (Pradhan et al., 2018) although fossil fuels are expected to be exhausted in 70 years (Ahmad et al., 2017). Their non-renewability and associated environmental issues such as global climate change, and air pollution have prompted researchers to intensify their search for alternative, clean and sustainable energy technologies (Chong et al., 2019). Owing to the widespread geographical distribution of biomass, and its carbon neutrality (Shah et al., 2018), energy generation from biomass (bioenergy) currently provides 10% of the world's total energy supply at an annual increase rate of

* Corresponding author. E-mail address: [email protected] (J. Liu). https://doi.org/10.1016/j.jclepro.2019.06.324 0959-6526/© 2019 Published by Elsevier Ltd.

2.5% globally (Edrisi and Abhilash, 2016). The common feedstocks for bioenergy generation consist of algae, energy crops, and lignocellulosic materials including forestry and agricultural residues (Williams et al., 2012). However, not only are agricultural crops for bioenergy expensive, but also they cause land use conflicts among food, fuel and feed needs (Ahmad et al., 2017). It is crucial to find a sustainable feedstock that grows on biophysically marginal lands, is not competitive with human food, and can alleviate the pressure on timber supplies. Globally, the feasible potential of energy crops sustainably grown on marginal lands was estimated to range from 130 to 270 EJ (EJ ¼ 1018 J) per year by 2050 (Haberl et al., 2011). Bamboo as one of the fastestgrowing plants is such an energy crop with a short growth cycle (Chen et al., 2014) and a daily growth rate of 7.5e100 cm (Bahari and Krause, 2016) that is most likely not to interfere with

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agricultural and forest lands. Thanks to its high yields, bamboo has been recognized as one of “Clean Development Mechanisms” to contribute to the clean and sustainable industrial production (Bahari and Krause, 2016). The spatial distribution of bamboo in the world ranges from 51
N to 47
S covering about 31.5 million ha of land (0.8% of the world's total forest area) (Yuen et al., 2017) where the tropical region accounts for 80% of the world's bamboo resources (Chang et al., 2018). One of the main high-tech processes for bioenergy generation is the thermochemical conversion of (co-)combustion, pyrolysis, and gasification (Abdulrahman and Huisingh, 2018). Combustion as an efficient, simple, and inexpensive technology accounts for over 90% of the world's bioenergy production (Bach et al., 2017). Biomass combustion can reduce greenhouse emission, and air pollution (Shah et al., 2018) if gas emissions are monitored and controlled properly. This in turn requires that the combustion performance and kinetics of specific biofeedstocks be quantified to orient the design and optimization of industrial-scale reactors, incinerators, and purification equipment for gases (Musellim et al., 2018). Studies were conducted about the ignition and burnout temperature of the bamboo combustion (Lu and Chen, 2015), the kinetics (Liang et al., 2018), products (Chen et al., 2015) and mechanisms (Wu et al., 2018) of the bamboo pyrolysis, and the torrefaction (Liu et al., 2016) and hydrothermal carbonization (Yan et al., 2017) of bamboo. In so doing, thermogravimetric (TG), differential scanning calorimetry (DSC), TG-mass spectrometry (MS) and TG-Fourier transform infrared spectroscopy (FTIR) analyses were used to characterize combustion performance (Cai et al., 2018), gas evolution (Musellim et al., 2018), kinetic parameters, and reaction mechanisms (Xie et al., 2018a). However, most studies have focused on the pyrolysis of bamboo trunks, without the consideration of the bamboo residues from forests and processing plants. Bamboo leaves (BL), and shoot leaves (BSL) that fall off are largely discarded during the forest growth, while bamboo branches (BB) are discarded during the processing of bamboo trunks. The reduction of these waste streams, and the bioenergy generation from them can be achieved by their combustion. However, not only does there exist no study about the combustions of the bamboo residues for bioenergy, but also a large knowledge gap still remains to be filled about gas emission from their combustions. Therefore, the objectives of this study were to (1) quantify the combustion characteristics of the three bamboo residues (BL, BSL, and BB) using TG analysis at four heating rates in the air atmosphere; (2) characterize evolved gases using TG-FTIR and TG-MS analyses in terms of pollution control; (3) for the first time, jointly optimize the four responses of derivative thermogravimetric (DTG), DSC, remaining mass (RM), and conversion degree (a) as a function of the three operational settings of temperature, heating rate, and bamboo residue type in terms of energy use and efficiency; and (4) estimate kinetic parameters using the three isoconversional and one model-fitting methods in terms of a better understanding of their combustion reaction mechanisms.

2.2. Physicochemical analysis Three parallel tests were carried out to determine their moisture (M), volatiles (V) and ash (A) contents on an air-dried basis according to “the Proximate Analysis of Solid Biofuels of China (GB/T 28731e2012)”. The relative standard deviation was less than 0.5%, while fixed carbon (FC) was calculated thus: FC (%) ¼ 100%  (M þ A þ V). The main elements of C, H, N, and S were measured on an air-dried basis using an elemental analyzer (vario EL cube by Elementar). Their O content was calculated thus: O (%) ¼ 100%  C  H  N  S  M e A (Xie et al., 2018b). The higher heating values (HHV) were measured using a WZR-1T-CII microcomputer calorimeter.

2.3. TGA-DSC experiments The TGA-DSC measurements of the samples were made using a TG analyzer (NETZSCH STA 449F5, Germany). The experiments were conducted in the air atmosphere at the flow rate of 50 mL/min in response to 5, 10, 20 and 40
C/min in the range of room temperature (RT) to 1000
C. A blank test was performed on each heating rate prior to the experiment in order to obtain a baseline to avoid a systematic error. The samples were placed in an oven at 105
C for 24 h to remove their moisture before the experiments. About 6 ± 0.5 mg of the dry samples were placed in an alumina crucible for each measurement.

2.4. TG-FTIR and TG-MS analyses TG-FTIR analysis was carried out using a TG analyzer (TG209 F1, Netzsch, Germany) coupled to a FTIR spectroscopy (IS50 FTIR, Thermo, America). This analysis simulated the air atmosphere at the flow rate of 57 mL/min. About 10 mg of the samples were heated from RT to 1000
C at 20
C/min. The generated emissions were transferred to the gas cell through the transfer line. The transfer line and the gas cell were maintained at 260
C. The resolution of FTIR was set to 8 cm1, with the spectral range of 4000 to 600 cm1. The OMNIC software was used to process the test data. TG-MS analysis was performed using a Thermo Mass Photo TGDTA-PIMS 410/S (Rigaku Corporation, Tokyo, Japan) based on 20
C/ min, the 80% Heþ20% O2 atmosphere, and the gas flow rate of 300 mL/min. The intensity of small molecular gases released from the combustions of the bamboo residues was measured from RT to 1000
C. The real-time monitoring of temperature-dependent ion current intensity, the identification of key ion signals by mass-tocharge ratio (m/z), and the standardization of data by ion current intensity-to-mass ratio (A/mg) allowed the different samples to be compared in terms of their release intensity.

2.5. Combustion parameters

2. Materials and methods 2.1. Sample preparation The bamboo residues used in this study belonged to BL, BSL and BB of the species “Phyllostachys pubescens”. All the residues were sampled from the Henan province of China. They were sun-dried, kept indoors for 24 h, pulverized using a pulverizer and then passed through a sieve with a mesh size of 74 mm.

The following combustion parameters were derived from the TG experiments: ignition temperature (Ti), burnout temperature (Tb), maximum weight loss rate (Rp), and peak temperature (Tp). These were in turn used to estimate the performance indices of comprehensive combustion (S) (Sun et al., 2019), flammability (C), and ignition (Di) thus (Zhang et al., 2019b):


S¼

  Rp  ð  Rv Þ T 2i  Tb

(1)

J. Hu et al. / Journal of Cleaner Production 235 (2019) 549e561

C¼

Rp T 2i

Rp Di ¼ ti  tp

(2)

(3)

where Rp is maximum weight loss rate (%/min); Rv is average weight loss rate (%/min); Ti is ignition temperature (oC); Tb is burnout temperature (oC); and ti and tp refer to ignition and peak times (min), respectively. 2.6. Statistical analyses The simultaneous optimization of the four responses of DTG (%/min), DSC (mW/mg), RM (%), and conversion degree (%) was based on the composite desirability (D) as a function of temperature, heating rate, and bamboo residue type. Composite desirability is a geometrical average of individual desirabilities (d) that range from 0 to unity (the ideal condition). Heating rate, and bamboo residue type were used as the interval and nominal predictors with the four and three levels of 5, 10, 20 and 40
C/min, and BB (1), BL (2), and BSL (3), respectively. Joint optimization was derived from the best-fit multiple non-linear regression (MNLR) models selected according to a stepwise procedure with a p-value-to-enter and toremove of 0.001. All the statistical analyses were performed using Minitab 17.1.

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(FWO), Kissinger-Akahira-Sunose (KAS), and Friedman (FR) were derived to estimate Ea in this study. 2.7.1. Flynn-Wall-Ozawa (FWO) method The FWO method is a linear integration method using the Doyle's approximation (Ding et al., 2017). The integral of the separation variable is performed with Eq. (7) to obtain Eq. (8):

GðaÞ ¼

ða 0

∞ ð ðT da A E=RT AEa AEa ¼ ,pðxÞ e dT ¼ ex x2 dx ¼ f ðaÞ b bR bR 0

(8)

x

Ea where x ¼ RT ; G(a) is the conversion function integral; and p (x) is temperature integral with no analytical solution but a numerical approximation. The Doyle's approximation was used here to obtain lg [p (x)] z 2.3150.4567x. Substituting this into Eq. (8) yields the FWO model thus (Jiang et al., 2015):

lgb ¼ lg



 AEa Ea  2:315  0:4567 RGðaÞ RT

when the same a is selected at different b values,lg

(9) 

a constant. Therefore, lg b is linearly related to of 0.4567

Ea R



AEa RGðaÞ 1 T

becomes

with a slope

from which Ea can be estimated.

Based on the TG analysis with the heating rates, the thermal degradation process can be expressed thus (Huang et al., 2018a):

2.7.2. Kissinger-Akahira-Sunose (KAS) method The KAS method is a linear integration method based on the Coats-Redfern approximation. When the Coats-Redfern approximation is applied to the above integral p (x), p (x) z x2ex can be obtained and substituted into Eq. (8) whose natural logarithm on both sides leads to the KAS model thus (Chen et al., 2017a):

da ¼ kðTÞ,f ðaÞ dt

    b AR Ea  ln 2 ¼ ln Ea GðaÞ RT T

2.7. Kinetic analyses

(4)

where t is time; k(T) is constant rate; and f(a) is the reaction model depending on the actual reaction mechanism (Chong et al., 2019). Conversion degree (a) was defined thus:

a¼

m0  mt m0  mf

(5)

where m0, mt and mf are initial, instantaneous and final sample masses, respectively. The temperature dependence of k can be described according to the Arrhenius equation as follows (Ozsin and Putun, 2019):

  Ea kðTÞ ¼ A,exp  RT

(6)

where Ea is apparent activation energy, kJ/mol; T is absolute temperature, K; R is the universal gas constant, 8.314 J K1$mol1; and A is the pre-exponential factor, s1. In the non-isothermal reaction process performed at a constant heating rate (b ¼ dT/dt), the following can be inferred according to Eqs. (4) and (6):

  da A Ea ¼ exp  ,f ðaÞ dT b RT

(7)

The model-free methods are the iso-conversional ones commonly used in kinetic studies that ensure the determination of Ea independently from having to solve an unknown reaction mechanism that governs the transformation (Mureddu et al., 2018). From Eq. (7), the three model-free methods of Flynn-Wall-Ozawa

(10)  

The same a is selected at different bs so that ln Tb2 is linearly related toT1, then Ea can be derived from its slope of 

Ea R.

2.7.3. Friedman (FR) method The FR method is a differential conversion method without any mathematical approximation (Huidobro et al., 2016). Taking the natural logarithm of the two sides of Eq. (7) results in the Friedman method thus:

  da Ea ¼ lnðAf ðaÞÞ  ln b dT RT

(11)

Ea can be estimated from the slope of the linear relationship   between ln b ddTa and T1. 2.7.4. Model-fitting method The Coats-Redfern (CR) method is the most common modelfitting method used to determine the mechanism functions as well as the kinetic triplets (Ea, A, f(a)). Assuming Ea [ RT, RT/Ea z 0 can be considered. The simplified equation based on the Arrhenius equation is as follows (Zhang et al., 2018):

    GðaÞ AR Ea ¼ ln  ln 2 bEa RT T

(12)

The slope and intercept values of the best-fit linear relationship   between ln GðT a2 Þ and T1 yield Ea and A estimates. The best-fit model

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Table 1 Common reaction model (Fernandez-Lopez et al., 2016). Symbol Diffusion model D1 D2 D3 D4 Geometrical contraction model R2 R3 Reaction order model F1 F2 F3 Fn Nucleation model A2 A3 A4

Mechanism

f (a)

G(a)

One-dimension diffusion Two-dimension diffusion Three-dimension diffusion Ginstling-Brounshtein

1/(2a) [-ln(1-a)]1 [(3/2)(1-a)2/3 ]/[1-(1-a)1/3] [(3/2)(1-a)1/3 ]/[1-(1-a)1/3]

a2

(1-a)ln(1-a)þa [1-(1-a)1/3]2 (1-2a/3)-(1-a)2/3

Contracting cylinder Contracting sphere

2(1-a)1/2 3(1-a)1/3

1-(1-a)1/2 1-(1-a)1/3

First order reaction Second order reaction Third order reaction nth order reaction

1-a (1-a)2 (1-a)3 (1-a)n

-ln(1-a) (1-a)1-1 [(1-a)2-1]/2 [1-(1-a)1n]/(1-n)

Avarami-Erofeev Avarami-Erofeev Avarami-Erofeev

2(1-a)[-ln(1-a)]1/2 3(1-a)[-ln(1-a)]2/3 4(1-a)[-ln(1-a)]3/4

[-ln(1-a)]1/2 [-ln(1-a)]1/3 [-ln(1-a)]1/4

with the highest coefficient of determination (R2) that is closest to the Ea value estimated by the model-free method is chosen as the optimal mechanism function. Several common reaction models for solid state reaction kinetics are shown in Table 1. 3. Results and discussion 3.1. Physicochemical analyses Generally, volatiles are easier to ignite, but higher ash content may affect the stability of the thermal degradation adversely and reduce the burning rate (Wang et al., 2019). The volatiles content of BSL (72.95%) was higher than that of BL (64.55%) and BB (69.26%). Their FC contents were similar (13e14%) (Table 2), while BSL had a significantly lower ash content (3.81%) than BL (14.55%) and BB (10.18%). The HHV of BSL (18.23 MJ/kg) was higher than that of BL (17.74 MJ/kg) and BB (17.67 MJ/kg). Their HHVs were higher than that of corn cob (16.90 MJ/kg), rice straw (16.78 MJ/kg), barley straw (15.70 MJ/kg) (Dhyani and Bhaskar, 2018), pine sawdust (16.81 MJ/ kg) (Hu et al., 2017), textile dyeing sludge (6.95 MJ/kg) (Sun et al., 2019), and lignite (16.16 MJ/kg) (Song et al., 2016), but lower than

that of liquor-industry wastes (19.53 MJ/kg) (Ye et al., 2018), subbituminous coal (25.31 MJ/kg), and bituminous coal (31.77 MJ/kg) (Mureddu et al., 2018). This finding indicates that the bamboo residues have a high combustion potential. The N contents of BSL (0.23%) and BB (0.62%) were lower than those of corn cob (0.70%), rice straw (0.63%) (Dhyani and Bhaskar, 2018), textile dyeing sludge (3.33%) (Sun et al., 2019), lignite (1.55%) (Song et al., 2016), sub-bituminous coal (1.59%), and bituminous coal (1.50%) (Mureddu et al., 2018). The higher N content of BL (2.26%) was similar to that of pine sawdust (2.26%) (Hu et al., 2017), and liquor industry wastes (1.94%) (Ye et al., 2018) which may result from their higher protein content (Ahmad et al., 2018). However, the low S contents of BL (0.21%), BSL (0.04%) and BB (0.08%) were below those of corn cob (1.30%), beech wood (0.70%) (Dhyani and Bhaskar, 2018), lignite (0.89%) (Song et al., 2016), subbituminous coal (7.14%), and bituminous coal (0.56%) (Mureddu et al., 2018). The N and S contents of the bamboo residues were lower than those of coal, sludge, and some biomass which indicated that the combustions of the bamboo residues had the possibility of releasing lower NOx and SOx emissions.

Table 2 Ultimate and proximate analyses of three bamboo residues on an air-dried basis. Sample

BL

BSL

BB

41.84 2.26 6.02 0.21 27.81

45.21 0.23 6.35 0.04 35.45

43.29 0.62 6.90 0.08 32.55

7.41 64.55 14.54 13.50 17.74

8.95 72.95 3.81 14.29 18.23

6.46 69.26 10.18 14.10 17.67

Photograph

Ultimate analysis (wt.%) C N H S O Proximate analysis (wt.%) M V A FC HHV(MJ/kg)

J. Hu et al. / Journal of Cleaner Production 235 (2019) 549e561

553

Fig. 1. (D)TG curves of (aed) BL, (bee) BSL and (cef) BB combustions at 10
C/min in the air atmosphere, and their stages in response to four heating rates.

3.2. (D)TG analyses The DTG curves of the three samples showed three similar peaks of mass loss with the first one being the largest (Fig. 1aec). Since the main components of the samples included (hemi)cellulose, lignin, and small amounts of lipids, and proteins (Yao and Ma, 2018), the three peaks related to their releases and decompositions. The combustions of the bamboo residues were divided into four stages (Fig. 1aec and Table 3). The first stage belonged to the water loss from the biomass cellular and surfaces (Ahmad et al., 2018). In the second stage (Table 3), the mass losses were at their highest by about 57e64% since the thermal decompositions of hemicellulose, cellulose, and lignin varied mainly between 220 and 315, 315 and 400, and 160 and 900
C, respectively (Xu and Chen, 2013). However, the higher maximum weight loss rate of BSL (10.35%/min) than BL (7.24%/min) and BB (7.86%/min) at this stage was due to its highest volatiles content. The total mass losses of BL, BSL and BB at the end of the

third stage were 75.29, 82.57 and 79.57%, respectively, which were parallel to their V þ FC contents according to the proximate analysis. Therefore, the third stage can be regarded as the thermal decomposition of most lignin and char. The fourth stage was the burnout and stabilization of inorganic substances, residual lignin, and char (Huang et al., 2018a). At this state, BSL and BB were completely burned at about 500
C, while BL was stable at about 550
C. BSL was burnt faster and more thoroughly with the highest overall weight loss. With the increased heating rate, the (D)TG curves moved to a higher temperature region as a whole, with 3.67e31.07, 4.70 to 64.77, and 3.65e35.61%/min as the maximum mass loss rates (Rp) of BL, BSL, and BB, respectively (Fig. 1def). They all converged at between 500 and 600
C and tended to be stable. The backward shift of the curve was due to the effect of the higher heating rate on the heat transfer between particles, resulting in a thermal lag effect on the combustion of volatiles and fixed carbon (Lu and Chen, 2015). The increase in the maximum combustion rate due to the

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Table 3 Temperature ranges and peaks of the bamboo residues at a heating rate of 10
C/min. Sample

Stage

Temperature range (oC)

Mass loss (%)

Maximum mass loss rate (%/min)

Total mass loss (%)

Residual mass (%)

BL

I II III IV I II III IV I II III IV

RT to 157.62 157.62 to 365.93 365.93 to 473.16 473.16 to 1000 RT to 166.06 166.06 to 345.48 345.48 to 422.81 422.81 to 1000 RT to 144.30 144.30 to 348.57 348.57 to 440.41 440.41 to 1000

3.22 57.05 18.42 3.88 3.20 64.20 18.37 7.26 3.19 58.57 21.00 4.65

e 7.24 2.24 0.90 e 10.35 2.76 2.21 e 7.86 2.70 1.43

82.57

17.43

93.03

6.97

87.41

12.59

BSL

BB

increased heating rate led to the enhancement of the thermal energy and the heat transfer between particles (Huang et al., 2018a). 3.3. TG-FTIR analysis The 3-D FTIR spectrogram of gaseous products from the combustions of the bamboo residues is detailed in the Supplementary Material (Fig. S1). From both 3-D FTIR spectrogram and the related literature (Ma et al., 2015), the functional groups of evolved gases can be inferred (Musellim et al., 2018). According to Lambert-Beer law, the gas concentration can be linearly reflected from the intensity of its absorption peak. The bamboo residues began to release gas at 200
C, with almost no gas emission at 600
C. The maximum absorption peaks of the three residues were at about 299.2
C according to their spectrograms. The three residues had similar absorption peaks. The absorption peaks of 4000 to 3400 cm1 (1900e1300 cm1) indicated a small amount of H2O production (Cai et al., 2018). The observed release of C-containing substances was abundant. The absorption peak of 2400 to 2260 cm1 was due to the asymmetric stretching vibration of C＝O which represented the production of CO2 accounting for most of the evolved gases. The minimal absorption peaks of 3000 to 2730 cm1 and 2240 to 2060 cm1 belonged to the symmetric stretching of CH4 due to the decomposition of methoxy (-OCH3) and methylene groups (-CH2-), and to the production of CO from the rupture of the C＝O bonds, respectively (Huang et al., 2018a). The absorption peak of asymmetric stretching of a small CeO bond between 1475 and 1000 cm1 pointed to the presence of alcohol, phenol, ether, and lipid. The absorption peak of 1690 to 1450 cm1 represented the benzene skeleton, while the C＝O stretching peak of 1900e1650 cm1 indicated the presence of aldehyde, ketone, or organic acid (Ma et al., 2015). These organic compounds were released during the devolatilization stage of 200e400
C due to the depolymerization of macromolecular polymers (hemicellulose, cellulose, and lignin) (Ma et al., 2015), and then, grew oxidatively cleavable to release various small molecule gases (CO2, and H2O) (Liang et al., 2018). Overall, the combustion oxidation reaction was carried out completely, with the production of almost no incomplete combustion gases. 3.4. TG-MS analysis The emission patterns of the small molecular gases were determined in detail using TG-MS analysis. The main gases of H2O, CO2, NH3, HCN, NO, NO2, and SO2 (m/z ¼ 18, 44, 17, 27, 30, 46, and 64) were selected. The patterns of gas releases from the combustions of the bamboo residues are shown in Fig. 2aeg. Overall, the gas release was mainly in the range of 200e600
C, while most of the organic compounds were oxidized and decomposed into CO2

and H2O. H2O was mainly produced between 200 and 500
C due to the dehydration reaction of hydroxyl (-OH) removal processes (Ozsin and Putun, 2019). The curve of the main gas product (CO2) had three peaks whose range and patterns matched with those of the DTG curve. In the volatilization stage, the bamboo residues began to produce CO2 whose release intensity was as follows: BSL > BL > BB. In the third stage of the DTG curve, the BL and BB combustions still produced considerable CO2 emission, while the CO2 intensity of BSL grew relatively low. However, in the final stage of the combustion stabilization of residual lignin and char, the CO2 intensity of BL and BB began to decrease. On the contrary, the CO2 intensity of BSL peaked due to the conversion of their lignin and other substances into char in the previous stage, and ultimately, to their complete oxidation and degradation. Finally, the CO2 intensities of the three residues were not detected or tended to be stable between 550 and 600
C. The total release intensity of gases can be described using the integral value of the gas release curves. Fig. 2h illustrates the comparison of the total intensity of the released gases whose detailed data can be found in the Supplementary Material (Table S1). The order of the total intensity was thus: CO2 > H2O > NH3 > NO > HCN > NO2 >SO2, while the order of the total emission intensity of the three residues was roughly the same. It was obvious that the released intensities of air pollutants were relatively low, 1/100-to-1/10 times CO2 (Fig. 2h), mainly in the range of 200e500
C (Fig. 2ceg). At the same time, almost no SO2 release (about 4/10000-to-8/10000 times CO2) was observed due to the trace S contents of the residues. For the N-related air pollutants, the maximum emission intensity was at about 300
C. The peak value was larger for BSL than BL and BB at this time, whereas the total amount was larger for BL than BSL and BB due to its higher N content. HCN and NH3 as the precursors of NOx and N2O (Sun et al., 2019) were mainly produced due to the thermal decomposition of N-containing volatiles (200e400
C) (Shah et al., 2018). NH3 produced the most intensity in gas pollutants due to the cracking of the amino group. Although its intensity was low, HCN emission is toxic to human respiratory system (Ahamad and Alshehri, 2012) and appeared to be produced by the degradation of intermediate heterocyclic-N compounds (such as pyrrole-N and pyridine-N) at below 350
C (Yang et al., 2015). With the increased temperature, HCN and NH3 continued to oxidize to NOx and also appeared to reduce a part of NOx to N2 (NO þ NH3 þ HCN / N2) (Fang et al., 2019). NOx had two peaks, while the intensity was lower for NO2 than NO. The three residues had the first peaks of NO2 and NO at about 300
C due to the primary degradation of their N-containing volatiles. The second peaks of NO and NO2 were due to the oxidative combustion of their N-containing char (Yang et al., 2015). BL had the second peak of NO2 at about 450
C, while BSL and BB had the second peak of NO at about 440 and 460
C, respectively.

J. Hu et al. / Journal of Cleaner Production 235 (2019) 549e561

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Fig. 2. (a to g) Emission patterns and (h) total amounts of main gases evolved from the combustions of the three bamboo residues.

However, the second peak of NO for BL was at about 550
C and had a wider range due to the higher and wider temperature range of its char combustion. Overall, the main gases evolved from the combustions of the

bamboo residues were CO2, H2O (accounted for about 90% of the total), and the small amounts of NH3, HCN, and NOx. SO2 was almost undetectable. Most of the air pollutants were produced at 200e500
C, with the maximum emission intensity at about

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Table 4 Combustion characteristic parameters of the three bamboo residues. Sample

BL

BSL

BB

b

5 10 20 40 5 10 20 40 5 10 20 40

Temperature (oC)

%/min

Time (min)

Ti

Tp

Tb

Rp

Rv

ti

tp

tb

244.88 246.90 252.27 255.04 238.21 248.39 259.42 268.64 240.01 248.95 261.35 261.96

275.79 287.27 298.97 301.09 269.52 278.85 289.40 302.01 276.54 291.57 303.10 303.57

511.51 512.81 532.63 542.76 444.23 453.17 464.80 473.90 454.55 469.03 486.72 500.41

3.59 7.24 15.50 30.92 4.70 10.30 24.73 64.10 3.65 7.90 17.53 35.61

0.42 0.85 1.68 3.47 0.49 0.96 1.92 3.83 0.45 0.90 1.78 3.70

44.10 22.47 12.49 7.28 42.79 22.57 12.76 7.52 43.14 22.64 12.84 7.41

50.23 26.25 14.42 8.15 48.96 25.40 13.96 8.11 50.38 26.63 14.57 8.18

97.86 49.11 25.84 13.52 84.38 43.05 22.40 11.88 86.44 44.69 23.52 12.51

S

Di

C

0.49 1.98 7.70 30.41 0.91 3.55 15.15 71.78 0.62 2.46 9.40 38.32

0.16 1.23 8.61 52.11 0.22 1.80 13.88 105.10 0.17 1.31 9.37 58.75

0.60 1.19 2.44 4.75 0.83 1.67 3.67 8.88 0.63 1.27 2.57 5.19

300
C. The total release intensity of the N-containing gases was of the following order: BSL < BB < BL, with BL having the wider range of gas emission. The control of N-containing gases should be more focused on the devolatilization stage (200e400
C). However, the combustion still needs to reach a higher temperature to eliminate potential organic pollutants (Wang et al., 2019), while the more controls over the gas pollutants are needed for the BL combustion. In short, the bamboo residues as an environmentally friendly and renewable feedstock released the relatively lower levels of air pollutants after the high temperature and a complete combustion. As for the large amount of NH3 in the released N-containing gas, their co-combustion with coal may reduce the NOx emission through the reduction action of NH3 (Zhao et al., 2016). 3.5. Combustion characteristic parameters S (107$%2
C3 min2), C (104$%$oC2 min1), and Di (102$%$ min3) were estimated to better characterize the combustion performance (Table 4). Ti, Tb, and Tp rose with the elevated b due to the thermal lag effect mentioned above. However, the ignition time (ti), and burnout time (tb) decreased with the higher b. The higher the S value was, the more intense the combustion was, and the faster the combustion rate was (Chen et al., 2017a). The higher Di value illustrated the better ignition performance, while the higher C value indicated the better combustion stability (Zhang et al., 2019b). The significant increases in S, Di and C occurred with the higher b. The Ti values of BL, BSL, and BB were similar, while their Tb values were of the following order: BL > BB > BSL (Table 4). The S, Di and C values of BSL were higher than those of BB. The combustion indices of BL were the smallest which was also the case with the increased heating rate. Overall, the combustion indices of the bamboo residues had the following order: BSL > BB > BL. BSL was easier to ignite with a faster combustion speed which appeared to relate to its low ash and high volatiles contents. 3.6. Joint optimization of multiple responses DSC illustrates the heat release with the changing temperature in the combustion process (Mureddu et al., 2018). The trends of the DSC and DTG curves (see the Supplementary Materials) were consistent. The three exothermic peaks of the combustions of the bamboo residues were attributed to the oxidative degradation reaction of the three stages in Section 3.2. However, the existence of thermal hysteresis resulted in the coincidence of the partial peaks of the DSC curves of BB and BSL at 40
C/min, thus indicating that BB and BSL were most likely to be more sensitive to the heating rates than was BL.

Fig. 3. Joint optimization of a, RM, DTG, and DSC in response to temperature, heating rate (HR), and bamboo residue type (BRT): the horizontal dashed blue lines refer to the best-fit values of the responses; the vertical solid red lines refer to the optimal points; and 1, 2, and 3 refer to BB, BL, and BSL, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

The four best-fit MNLR models (p < 0.001; n ¼ 109169) accounted for variations in the responses in the range of 43.8 for DTG to 97.1% for RM and a whose details are presented in the Supplemental Materials. The joint optimization for the combustions of the bamboo residues was based on the objective functions of the maximized DSC, DTG and a, and the minimized RM. The joint optimization results are shown in Fig. 3. As the combustion temperature increased, the four responses gradually approached the best-fit value at 653.2
C. The increase in the heating rate was negatively correlated with the D value. The DSC response was sensitive to both elevated heating rate and the bamboo residue type. The magnitude of change in the DSC curve in response to the residue type was of the following order: BB > BSL > BL. Overall, the best-fit values of the four responses indicated that 653.2
C and 5
C/min appeared to be the optimal operational settings to achieve the maximized heat release as well as the minimized remaining mass (D ¼ 0.988), while BB was most likely to be the bamboo residue type that performed the most efficient combustion.

J. Hu et al. / Journal of Cleaner Production 235 (2019) 549e561

557

Fig. 4. Fitted curves of Ea estimated by the FWO, KAS and FR methods for (a to c) BL combustion; and changes in Ea of (d) BL, (e) BSL and (f) BB combustions.

3.7. Kinetic analysis 3.7.1. Activation energy estimates by model-free methods The fitted curves of activation energy (Ea) estimated by the three iso-conversional methods in the range of 0.1 < a < 0.9 are shown in Fig. 4aec. The specific results with the coefficient of determination (R2) values as the goodness-of-fit of the models are shown in the Supplementary Materials (Table S2). The similar Ea estimates by the FWO and KAS methods were lower than those by the FR method. The average R2 values of >0.9 pointed to the accurate Ea estimates. Since the FR method is sensitive to noise and experimental errors, the R2 value of the curves fitted by this method was relatively low because its rate equation is a simple differential deformation without a mathematical approximation (Huidobro et al., 2016). The R2 values of the FWO-based Ea estimates were higher than those of the other methods. The Ea estimated by FWO was 121.28e201.01 kJ/ mol for BL, 170.83e263.09 kJ/mol for BSL and 157.27e227.05 kJ/mol for BB. The Ea of BSL was similar to that of swine manure (212.21e261.29 kJ/mol) (Fernandez-Lopez et al., 2016), while those

of BL and BB were similar to those of spent mushroom substrate (130.06e192.95 kJ/mol) (Huang et al., 2018a), and waste tea (145.46e231.81 kJ/mol) (Cai et al., 2018). The Ea value as the minimum energy required for the reaction depends on the mechanism of combustion reactions, with its higher value meaning that the reaction starts slowly (Huang et al., 2019a). Fig. 4def shows the trends of Ea for the three bamboo residues. The range of 0.1 < a < 0.7 between 200 and 350
C corresponded to the devolatilization stage during which the FWObased Ea estimates were initially of the following order: BSL > BL > BB. As the reaction progressed, the Ea values increased from 170.37 to 201.01 kJ/mol at the a of 0.3 and then decreased to 121.28 kJ/mol for BL; increased from 181.45 to 263.09 kJ/mol at the a of 0.6 and then decreased to 170.83 kJ/mol at about 0.75 for BSL; and increased from 157.27 to 177.00 kJ/mol at the a of 0.5 and then decreased to 174.26 kJ/mol for BB. In the subsequent reaction stage (0.7 < a < 0.9), the Ea values of the three residues showed an initially rising and then falling trend, indicating the subsequent degradations of residual lignin, char, and other substances. The

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J. Hu et al. / Journal of Cleaner Production 235 (2019) 549e561

Fig. 5. Curve-fitting of the CR method for the (a) second and (b) third stages of BL combustion.

average Ea values were thus: BSL > BB > BL meaning that BSL may need to break through greater energy barriers in the combustion process (Wang et al., 2019). However, the fluctuation of Ea in the entire range implied a complex combustion reaction in which hemicellulose and cellulose were degraded successively, while the lignin degradation spanned the entire main combustion stage. This involved a single or overlapping process of multiple mechanisms such as diffusion, interface, and nucleation (Huang et al., 2018b). 3.7.2. Estimation of kinetic triplets of Ea, A, and f(a) The kinetic triplets are critical to the optimization of industrial reactors, and the prediction of reactions (Chen et al., 2017b). In the range of 200e500
C, the mass losses of BL, BSL and BB in the second and third stages reached 75.47, 82.57 and 79.57% (Table 3), respectively, and corresponded to the overall conversion degree range of 0.05e0.95. Considering the effect of moisture in the foreend reaction, the mass transfer was dominant in the back-end reaction (Huang et al., 2019a), and in general, the heating rates were found to have no significant effect on the search for the reaction mechanism (Fernandez-Lopez et al., 2016). Hence, the reaction mechanism was determined according to the temperature ranges of the second and third stages when b ¼ 10
C/min (Table 3). The curve-fitting of the CR method is shown in Fig. 5, while the kinetic parameters estimated for various reaction models are detailed in the Supplementary Materials (Table S3). The reaction order models were found to be the most likely mechanism function to describe the combustion degradations of the bamboo residues. The second order reaction (F2) had the highest R2 value (greater than 0.99). The kinetic parameters estimated using the F2 model are listed in Table 5. The A changes are essential to the explanation of the reaction chemistry of the biomass combustion process. The A values represent a surface reaction or a compact complex if the reaction is not dependent on the surface when < 109 s1; a simpler complex when > 109 s1 (Maia and de Morais, 2016); and the limited rotation of the activated complex compared to the initial

reagent between 1010 and 1012 s1 (Xu and Chen, 2013). A was < 109 only in the second stage of BL and BB, while A was > 109 in the other stages which indicated a loose junctional complex. Table 5 shows that the CR-estimated Ea values were within the range of those estimated by the model-free methods for the three samples, thus pointing to more reliable results. Since the Ea value of the char combustion is higher than that of devolatilization (Fang et al., 2019), the Ea value was higher for the third stage than the second stage for the bamboo residues. It should be noted that CR yields the average Ea of a specific stage with a higher error than do the modelfree methods. The F2 model best described the main combustion stages (II to III) of the bamboo residues, this reflected that the decomposition of individual particle is achieved by random nucleation of two nuclei (Zhang et al., 2019a); however, A and Ea differed depending on the feedstocks. Fig. 6 shows the reaction predictions based on the F2 model. Our results showed that the estimated kinetic triplets well predicted the combustion reactions of the bamboo residues, with a R2 value of greater than 0.9970. The bamboo residues are the typical lignocellulosic material for which similar kinetic studies were reported. For example, the F2 model was reported as the best descriptor of the devolatilization stage of spent mushroom substrate (Huang et al., 2019b). The combustion of olive tree residues was most elucidated by the F1 model (Garcia-Maraver et al., 2015), while the most suitable mechanism for oil-plant residues was explained by the reaction order models (Chen et al., 2017b). Overall, the reaction order models are well suited to describe the combustion reaction mechanism of lignocellulosic biomass.

4. Conclusions The relatively high HHV and lower N/S contents of the three bamboo residues pointed to their great potential as a clean and renewable feedstock for the energy generation. The DTG curves

Table 5 Kinetic triplets estimated by F2 model. Sample BL BSL BB

Stage II III II III II III

A (s1)

Fitting equation y y y y y y

¼ ¼ ¼ ¼ ¼ ¼

13638.21x 23925.40x 16715.31x 25473.53x 15699.68x 23019.36x

þ þ þ þ þ þ

11.64 22.02 17.52 25.66 15.40 21.47

1.54 8.75 6.78 3.54 7.65 4.87

     

f(a) 7

10 1011 109 1013 108 1011

(1a) (1a)2 (1a)2 (1a)2 (1a)2 (1a)2 2

Ea (kJ/mol)

R2

113.39 198.92 138.97 211.79 130.53 191.38

0.9992 0.9951 0.9993 0.9935 0.9936 0.9959

J. Hu et al. / Journal of Cleaner Production 235 (2019) 549e561

559

Fig. 6. Comparisons of predicted versus measured conversion degrees (a) based on F2 model.

showed that the three major peaks of mass loss for all the three bamboo residues between 200 and 600
C. TG-FTIR and TG-MS analyses showed that CO2 and H2O were released as the main gases from their combustions between 200 and 600
C. Air pollutants were produced mostly in the range of 200e500
C, while BL released more gas pollutants than did BB and BSL. Sufficient air volume, and a high combustion temperature should be maintained for the combustions of the three bamboo residues. The controls over the gas pollutants should focus on the range of 200e400
C as well as on NOx rather than SO2 emission. The combustion properties of the bamboo residues exhibited the following pattern of BSL > BB > BL, with BSL burning more rapidly. Based on the joint optimization, the optimum feedstock utilization and efficiency were achieved at 653.2
C and 5
C/min with BB. The fluctuation of

the Ea estimates according to the model-free methods pointed to the complex combustion reactions of the bamboo residues. According to the CR method, the second order reaction model appeared to best elucidate their reaction mechanisms, thus indicating the random nucleation as the dominant process. Overall, the three bamboo residues appear to be of great utility value in terms of the recovery of the clean and renewable energy for the industrial applications. This study, for the first time, paved the way for the controls over air pollution, and clean energy generation from the three bamboo residues. Given the low levels of the polluting gas releases from the combustions of the bamboo residues, their co-combustion and co-pyrolysis with coal, or sludge remain to be explored to reduce NOx and CO2 emissions.

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