Catalytic effects of CaO, Al2O3, Fe2O3, and red mud on Pteris vittata combustion: Emission, kinetic and ash conversion patterns

Journal of Cleaner Production 252 (2020) 119646

Contents lists available at ScienceDirect

Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Catalytic effects of CaO, Al2O3, Fe2O3, and red mud on Pteris vittata combustion: Emission, kinetic and ash conversion patterns Yueyao Song a, Jinwen Hu a, Jingyong Liu a, *, Fatih Evrendilek b, c, Musa Buyukada d a

School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou, 510006, China 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 b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 August 2019 Received in revised form 10 November 2019 Accepted 9 December 2019 Available online 13 December 2019

Catalytic effects of red mud (RM), calcium oxide (CaO), aluminum trioxide (Al2O3), and ferric oxide (Fe2O3) were quantified on the combustion, emission and ash characteristics of aboveground (PA) and belowground (PB) biomass of Pteris vittata using thermogravimetric, Fourier transform infrared, X-ray fluorescence and FactSage analyses. CaO affected the specific formation pathways of tar species and inhibited the CO2, HCN and SO2 emissions. Fe2O3 shortened the initial release time of the emissions. Al2O3 inhibited the final NO emission but did not control the N-containing products. RM catalyzed the combustion by suppressing the emissions. The enthalpy of PA was catalytically enhanced in the following order: CaO > RM > Fe2O3 > Al2O3. Only Fe2O3 increased the enthalpy of PB. The stationary index value of
PB declined with the catalysts. The comprehensive combustion index of PA was high at 20 C/min. Al2O3 reduced the risks of slagging, and fouling for PA and PB, while RM exerted a more pronounced effect on PA than PB. The fusion of low-melting point minerals accelerated the mass and heat transfers, and the ash melting. Activation energy was reduced by 275.99% with RM and by 119.82 and 115.81% with Al2O3, and Fe2O3 for PA, respectively. Our results pave the way for cleaner and sustainable production strategies with the catalytic biomass combustion. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Richard Wood Keywords: Red mud Pteris vittata Combustion Catalytic effect TG-FTIR FactSage

1. Introduction Environmentally compatible energy production and use have become increasingly crucial to the welfare, health, and security on the local-to-global scales due to the rapid population and economic growth (Ghaderi et al., 2016). Globally, bioenergy generation, on average, contributes about 14% to the renewable energy utilization and is on the rise owing to its cleanness, renewability, carbon neutrality and feedstock abundance (Yang et al., 2018). For example, China aims to reduce its CO2 emission by raising the share of bioenergy in its energy consumption to 20% by 2030 (Yang et al., 2018). The search has been intensified for combining abundant biofeedstocks with a high calorific value (Duan et al., 2017), and environmentally friendly and economically efficient technologies in generating bioenergy (Zhang et al., 2019b). One such promising feedstock is Pteris vittata that has been explored for incineration

* Corresponding author. E-mail address: [email protected] (J. Liu). https://doi.org/10.1016/j.jclepro.2019.119646 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

(Song et al., 2019), phytoremediation (Yan et al., 2012), ethanol extraction (da Silva et al., 2019), and bio-oil production from hydrothermal liquefaction (Chen, 2018). For both economic and environmental costs of the biomass (co-) combustions to be reduced, one of the best management practices is to add a catalyst (Long et al., 2019). However, mineral catalysts may exert either a positive or negative effect on the thermal degradation. For example, magnesium oxide (MgO) was found to positively affect the residual amount more than did zinc oxide (ZnO), and aluminum trioxide (Al2O3) (Fang et al., 2016). Calcium oxide (CaO) was shown to fix gas pollutants, unlike sludge ash, and Al2O3 (Sun et al., 2019). Ca-containing compounds were reported to enhance the lignin decomposition, and the char combustion as well as to reduce the activation energy (Yuan et al., 2019). Unlike silicon dioxide (SiO2), ferric oxide (Fe2O3) significantly reduced NO2 emission (Minh Loy et al., 2018). Red mud (RM) is a polluted waste discharged from aluminum industry. Globally, total bauxite reserve as its primary mineral is estimated at 29 billion tons while the possible commercial applications of bauxite is on the order of 150 million tons per year

2

Y. Song et al. / Journal of Cleaner Production 252 (2020) 119646

(bauxite.world). RM mainly consists of Al2O3, SiO2, CaO, Fe2O3, sodium oxide (Na2O), and titanium dioxide (TiO2) and poses a severe environmental threat (Vigneshwaran et al., 2019). Its use as a catalyst in the (co-)combustion or pyrolysis (Resende et al., 2013) is most likely to alleviate the environmental pressures associated with its waste stream (Jollet et al., 2014), and conventional disposals such as long-term storage in lagoon reservoirs (Hu et al., 2018). RM was found to fix N compounds in char, to inhibit its conversion to tar-N and to promote N2 emission from the sludge pyrolysis (Xiao et al., 2019). Thus far, neither RM as a natural catalyst rich in multiple metal oxides, nor P. vittata has been explored in terms of combustion performance. Related literature also contains a large knowledge gap as to the combustion performance of P. vittata with the catalytic addition of Al2O3, CaO, and Fe2O3. Therefore, the objectives of this experimental study were, for the first time, to (1) quantify the combustion performances of both belowground (PB) and aboveground (PA) biomass of P. vittata with(out) the four catalysts; and (2) detect their emission, kinetic and ash conversion patterns in order to provide insights into new strategies to render the catalytic biomass combustion cleaner.

using a TG system (TG209 F1, NETZSCH, Germany) coupled with a Fourier transform infrared (FTIR) spectroscopy (iS50 FTIR, Thermo, America). FTIR spectra were recorded in the wavenumber range of 4000 to 600 cm1, with 16 scans per run at a spectral resolution of 8 cm1. Their major elements and mineral compositions were detected using an Axios-Minerals wavelength dispersive X-ray fluorescence (XRF) spectrometer. Ashes were sampled from the fuels combusted at 575 ± 25
C in an electric furnace of energysaving box type (model SX-G12123, Tianjin, China). The multiphase equilibria and phase transitions of ashes in the air atmosphere were simulated using FactSage version 7.1. The elements of K, Ca, Si, Al, Mg, Cl, Fe, P, Ti, and Na were elected as input data in the equilibrium module. Phase formation data for these elements and their combinations were chosen from the databases of FactPS, FToxid and FTsalt. The simulations were carried out between 200 and 1400
C at an interval of 50
C under the pressure of 1 atm (1.013  105 Pa), and the excess air ratio (l) of 1.5.

2. Materials and methods

Kinetic analysis was used to describe the thermal degradations of P. vittata during which their solid-state reaction properties were non-isothermal and heterogeneous. According to the Arrhenius’s law, the reaction rate is shown thus (Wang et al., 2019):

2.1. Preparation of feedstocks and catalysts Aboveground (fresh branches and leaves) and belowground (stalks and roots) biomass of P. vittata was obtained from the Guangdong province (China). After being washed with deionized water and sun-dried for 36e48 h, both sample types were ground using a crusher, sieved through less than 74 mm and further dried in an oven at 105
C for 24 h. Owing to their inexpensive and abundant supplies, RM and its main components CaO, Al2O3 and Fe2O3 (all purity  99.7%) were chosen as the catalysts. RM was acquired from Zhengzhou of the Henan province (China), while Al2O3, CaO, and Fe2O3 were obtained from commercial stores and directly used as received. RM, CaO, Al2O3, and Fe2O3 were blended with the dry biomass samples at a ratio of 5% via grinding at least for 30 min which were in turn kept in an oven at 105
C for 24 h. Ten (non-) catalyzed samples were placed in the sealed plastic bags and stored in a desiccator at room temperature (RT). 2.2. Physicochemical properties and combustion analysis Ultimate, proximate, fiber and higher heating value (HHV) analyses of PA, PB, and RM are presented in Table 2. The contents of hemicellulose, cellulose, and lignin were analyzed using the Van Soest’s method of cellulose assay. Ultimate, proximate and HHV analysis were the same as with our previous study (Song et al., 2019). The combustion characteristics were analyzed using a TG analyzer (NETZSCH STA 409 PC) at a gas flow rate of 50 mL/min
under the heating rates of 5, 10, 20 and 40 C/min in the air atmosphere. The catalyzed-samples (PAþ4C and PBþ4C), and the non-catalyzed samples (PA and PB) were heated from RT to 1000
C. In each run, around 6.0 ± 0.5 mg of the samples were loaded into an Al2O3 crucible to be sent into the TG furnace. The experiments were replicated at least twice. In our experiments, no change was observed in the surface of the aluminum crucible, and the aluminum crucible was found not to have participated in the catalytic reaction. 2.3. Gas evolutions, mineral ash compositions and FactSage simulations Gas evolutions from the (non-)catalyzed samples were analyzed

2.4. Kinetic analysis

b¼

dT dt

(1)

Eq. (1) can be re-arranged thus:

da A ðEa Þ ¼ e RT f ðaÞ dT b

(2)

Conversion rate (a2½0; 1), also known as a normalized quality, can be stated as follows:

a¼1  x ¼

ðm0  mt Þ ðm0  mf Þ

(3)

where m0 , mt and mf refer to initial, instantaneous and final masses of the sample, respectively. The integral of Eq. (2) yields the following (Chen et al., 2017):

gðaÞ ¼

ða 0

da A ¼ f ðaÞ b

ðT

Ea

eðRT Þ dT ¼

T0

AEa bR

ðu 0

eðuÞ AEa pðuÞ du ¼ bR u2

(4)

where f ðaÞ is an uncharted kinetic model.A, R, Ea, T0, and T refer to the pre-factor, general gas constant, apparent activation energy, and initial and absolute temperatures, respectively. gðaÞ represents Ea the integrated form of u ¼ RT and PðuÞ is the integrated form of temperature. The kinetic triplets of A, Ea, and f (a) were estimated from the Coats-Redfern (CR) method as a function of temperature. Since 2RT ≪1, and f (a) ¼ (1a) n was the nth reaction order, Eq. (2) can be Ea rewritten thus (Hu et al., 2019):

ln½

GðaÞ Ea AR þ lnð ¼ Þ RT bEa T2

(5)

The better the linear fit is, the more appropriate the reaction mechanism function is selected. Ea and A were estimated from the best-fit with the highest coefficient of determination (R2). Several common functions of reaction mechanism are shown in Table 1.

Y. Song et al. / Journal of Cleaner Production 252 (2020) 119646

3

Table 1 Common reaction mechanism functions (Cao et al., 2019). f(x)

g(a)

Nucleation

A1.5 A2 A3 A4 An

Avarami-Erofeev Avarami-Erofeev Avarami-Erofeev Avarami-Erofeev Avarami-Erofeev

1.5(1-a)[-ln(1-a)]1/3 2(1-a)[-ln(1-a)]1/2 3(1-a)[-ln(1-a)]2/3 4(1-a)[-ln(1-a)]3/4 n(1-a)[-ln(1-a)](n1)/n

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

Reaction order

F1 F2 F3 Fn

First Order Reaction Second Order Reaction Third Order Reaction n 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-a)(1n)-1]/(n-1)

Diffusion

D1 D2 D3 D4

One-dimension diffusion Valensi Jander 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

Power law

P2 P3 P4

2-Power law 3-Power law 4-Power law

2a1/2 3a1/3 4a1/4

a1/2 a1/3 a1/4

Geometrical Contraction

R2 R3

Contracting cylinder Contracting sphere

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

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

Reaction Models

Table 2 Results of proximate, ultimate, fiber and HHV analyses for PA, PB, and RM on an airdried basis. Sample Ultimate analysis (wt. %) Carbon Hydrogen Nitrogen Sulfur Fiber analysis (wt. %) Hemicelluloses Cellulose Lignin Proximate analysis (wt. %) Moisture Volatiles Ash Fixed Carbon HHV (MJ/Kg)

PA

PB

Red Mud

41.70 5.21 1.54 0.15

42.42 5.01 0.79 0.07

2.37 1.39 e 0.22

12.03 40.78 28.28

0.22 40.23 40.92

/ / /

9.35 58.51 14.21 17.93 16.60

10.43 49.52 13.76 26.29 16.34

4.01 16.29 79.69 0.01 4.284

below the detection line; Fixed carbon ¼ 100%  moisture e volatiles e ash (Hu et al., 2019).

3. Results and discussion 3.1. Comparative physicochemical characteristics On a dry weight basis, the total (hemi)cellulose and lignin contents of PA (81.09%) and PB (81.37%) were similar. Hemicellulose was much lower in PB (0.22%) than PA (12.03%), while lignin was much lower in PA (28.28%) than PB (40.92%). Ultimate, proximate and HHV analysis of PA and PB were studied in our previous study (Song et al., 2019). When compared to RM, P. vittata is advantageous owing to its high volatility, low ash content and high combustion calorific value (Table 2). The very low HHV value (4.284) of RM reached 14.239 for PA þ RM and 14.027 MJ/kg for PB þ RM. Therefore, the RM-catalyzed combustion of P. vittata may pose a higher combustion performance. 3.2. Effects of catalysts on emissions TG-FTIR analysis is an advanced technique not only to record fuel mass losses, and emission temperatures of major volatiles but

also to characterize their functional groups and species including both the non-condensable gases of CO2, CO, and CH4 and the condensed volatiles of H2O, methanol, acids, and phenols. The emission patterns of organic functional groups were similar in the catalyzed samples, except for the wavenumber of about 2935 cm1 which should be aliphatic (Yan et al., 2013). The cleavage of the compound or an aliphatic hydrocarbon other than some aromatic hydrocarbons (such as CeH bond broken in CH4) indicated that the catalysts promoted CH4 emission from the PA combustion. Typical functional groups, reaction types, and corresponding wavenumber segments are shown in the Supplementary Materials (Table S1). The emission patterns of the five typical gases (CO2, CO, HCN, NO, and SO2) were compared for the PA and PB combustions with(out) the addition of the catalysts at specific wavenumbers (Fig. 1).

3.2.1. Carboniferous emissions During the biomass combustion, the adsorption of oxygen is considered a control step for the carbon-oxygen reaction. In the wavenumbers of 2400 to 2250 cm1 and 800 to 575 cm1, the significant release peaks for both samples belonged to the C]O stretching vibration characteristic zone of CO2 emission from the decomposition of some carbonates into inorganic substances. The CO2 emission was mainly attributed to the cracking and reforming of the carbonyl and carboxyl groups of organic matters (Zhang et al., 2019a). The cellulose decomposition released less CO2, whereas the lignin decomposition released more CO2 between 351 and 481
C (Wang et al., 2017a) (Fig. 1a and b). The CO2 emission from the catalyzed samples significantly fell in the devolatilization stage between 200 and 600
C (Fig. 1a), with PA þ CaO decreasing the most. Al2O3 exacerbated the decrease in the CO2 emission, while PA þ Fe2O3 appeared to move to the low temperature zone. The CO2 emission from the PB combustion occurred between 250 and 550
C. The peak width decreased when a shoulder peak appeared at around 500
C. This may be due to the higher lignin content of PB than PA. Fe2O3 and Al2O3 tended to move towards a low temperature region and exacerbated the CO2 emission. CaO and RM reduced the CO2 emission which indicated that CaO accelerated the CO2 absorption at higher temperatures. The effect of PB þ RM was the most pronounced one, with the peak value being about the half of PB. This pointed to substances other

4

Y. Song et al. / Journal of Cleaner Production 252 (2020) 119646

than CaO with a strong CO2 absorption function in RM such as NaOH, and other complex highly basic sodium aluminates. The shoulders at about 700
C were mainly related to the following reaction: CaCO3 (solid) / CaO (solid) þ CO2 (gas). In the ranges of 2250 to 2000 and 1420 to 1300 cm1, the expansion and contraction of CeO band of CO occurred. The catalysts exerted significant effects on the samples between 200 and 400
C, decreasing all the peaks, and increasing the peak width (Fig. 1c). This suggested that the combustion was no longer intense with the low CO emission. The four catalysts did not significantly affect the decomposition of lignin þ char, but CO emission from the hemicellulose decomposition. The hemicellulose decomposition is known to release CO over the entire temperature range, while the cellulose and lignin decompositions produce low and high CO emissions, respectively, between 350 and 480
C (Wang et al., 2017a). Fe2O3, and CaO enhanced the overall CO emission, with Fe2O3 being the most significant one at lower temperatures. CaO appeared to enhance some

insignificant pathways of CO emission. For example, CaO may have restrained the cracking of ether bridges as the main source of CO emission at lower temperatures (Liu et al., 2008). CO2 emission from the decomposition of CaCO3 reacted with char carbon, also known as Boudouard reaction C þ CO2 ¼ 2CO, through which CO emission may also be generated. Unlike CaO, RM significantly reduced the CO emission between 400 and 600
C probably due to the following reaction: 2Fe2O3 þ 2CO ¼ 4FeO þ 2CO2. 3.2.2. HCN and NOx emissions HCN is an important precursor for the generation of N2O and NO, a characteristic band of HCN bending vibration at 714 cm1. HCN emission between 200 and 400
C significantly fell with CaO, Fe2O3, and RM but rose with Al2O3 (Fig. 1e). The main products between 200 and 400
C were volatiles-N (mainly composed of tar N and gas phase N), and char-N (part of N bound in the fuel-N that remained in the char matrix during the initial combustion). The

Fig. 1. FTIR analysis of PAþ4C and PBþ4C combustions in terms of (a, b) CO2, (c, d) CO, (e, f) HCN, (g, h) NO and (i, j) SO2 emissions at 20
C/min: 4C refers to CaO, Al2O3, Fe2O3, and RM (PA and PB data adapted from (Song et al., 2019)).

Y. Song et al. / Journal of Cleaner Production 252 (2020) 119646

5

Fig. 1. (continued).

related (tar) gases were mainly NH3, HCN, and a small amount of HNCO. The HCN emission was enhanced with Al2O3, and Fe2O3 but reduced with CaO, while RM absorbed more HCN between 400 and 600
C (Fig. 1f). These findings may be related to the reduction of the HCNeN yields with the synergistic effects of Fe and Ca compounds on the protein-N conversion (Liu et al., 2017) such as the generation of Ca2Fe2O5, discussed in Section 3.4.2. The inhibition effects (Xiao et al., 2019) on the secondary cracking of amine-N compounds by RM, and less heterocyclic- and nitrile-N appeared to reduce the yields of NH3 and HCN. Therefore, it is reasonable to infer that CaO in RM rather than Al2O3 may be the key metal oxide responsible for fixing the char-N of the samples. HCN seemed to continue to oxidize to form N2O or NO, with newly formed NO resulting in N2 under the reduction of hydrocarbons, and char. A small quantity of NO was detected during the combustion. The NO emission can be attributed to the reaction between N-containing compounds and OH radicals (Ren and Zhao, 2013). A bimodal shape was observed for the NO emission in Fig. 1i and j. The peak NO emission from the PA combustion dropped with Al2O3, Fe2O3, and RM but did not significantly change with CaO. The NO emission from the PB combustion declined with Al2O3, and CaO, but significantly rose with Fe2O3 and did not change with RM. 3.2.3. SOx emission S mainly entered the plant through the absorption of sulfates in the soil by PB, and the reduction and assimilation reaction by PA to form the essential amino acids. During the biomass combustion, S was present mainly in the forms of SO2 at 1374 cm1, a characteristic band of the tensile vibration of SO2, alkali metal and alkaline earth metal sulfate deposited as fly ash or bottom slag (Yan et al., 2013). Primarily, the former was from the oxidation of organic sulfides, while the latter was from the decomposition of inorganic sulfates.

CaO, and RM inhibited the SO2 emission from the PA and PB combustions (Fig. 1geh). The SO2 emission was prevented from PB but promoted from PA with Al2O3. On the contrary, the SO2 emission was suppressed from PA but enhanced from PB with Fe2O3. Overall, CaO affected specific formation pathways of tar species and inhibited CO2, HCN and SO2 emissions. Fe2O3 shortened the initial release time of the emissions. Al2O3 suppressed the final NO emission but did not control the main intermediate of N-containing products. Due to its rich mineral composition, RM catalyzed the combustion and suppressed the emissions. This shows that the byproducts of the alumina manufacturing industry have a great catalytic potential in the biomass (co-)combustions. 3.3. Catalytic combustion characteristics The four catalytic effects on the PA and PB combustions were
analyzed with the four heating rates (5, 10, 20 and 40 C/min)
(Fig. S1). 20 C/min was chosen to further study their (D)TG and differential scanning calorimetry (DSC) curves (Fig. 2). The following four stages were determined: the evaporation and combustion of low-boiling substances (stage 1); the combustion and decomposition of original components (stage 2); the combustion of subordinately organics and inorganics (stage 3); and the combustion and decomposition of residual materials (stage 4). For a more detailed comparison, the TG, DSC and DTG curves and combustion parameters of P. vittata in our previous study (Song et al., 2019) were also added to Fig. 2 and Table 3. The first stage was the exothermic volatilization of the low-boiling components. The second stage reaction was complicated. The light component was first decomposed and then burnt rapidly when the ignition temperature was reached. The entire process was exothermic but different in its strength. In the third stage, the component with a

6

Y. Song et al. / Journal of Cleaner Production 252 (2020) 119646




Fig. 2. (a) TG, DSC and (b) DTG curves of PAþ4C and PBþ4C combustions at 20 C/min (Blue, green, pink and gray backgrounds refer to stages 1, 2, 3 and 4, respectively.). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Y. Song et al. / Journal of Cleaner Production 252 (2020) 119646

7

Table 3 Combustion parameters of the (non-)catalyzed samples in the air atmosphere at four heating rates. Parameters

PA

PA þ CaO

Heating rate:5
C/min Temperature (oC) Tp1 59 56 Tp2 286 283 Tp3 433 434 251 247 Ti Tf 622 666 Combustion index  Rp (%/min) 3.36 2.86  Rv (%/min) 0.45 0.42 Mf (%) 12.99 19.37 C 0.52 0.47 0.38 0.30 CCI (108%2
C 3,min2) o
Heating rate:10 C/min Temperature ( C) Tp1 72 71 Tp2 297 294 Tp3 436 434 Ti 259 257 Tf 624 678 Combustion index  Rp (%/min) 14.10 9.63  Rv (%/min) 0.91 0.86 Mf (%) 15.17 18.77 C 2.02 1.46 CCI (108%2
C 3,min2) 2.95 1.85 Heating rate:20
C/min Temperature (oC) Tp1 79 65 Tp2 304 306 Tp3 431 429 Ti 272 265 Tf 653 683 Combustion index  Rp (%/min) 15.36 15.35  Rv (%/min) 1.89 1.80 Mf (%) 13.83 13.82 C 2.07 2.19 CCI (108%2
C 3,min2) 6.00 5.76 Heating rate:40
C/min Temperature (oC) Tp1 92 93 Tp2 311 313 Tp3 437 432 Ti 276 270 Tf 670 709 Combustion index  Rp (%/min) 35.43 23.85  Rv (%/min) 4.01 3.62 Mf (%) 14.19 21.32 C 4.65 3.27 CCI (108%2
C 3,min2) 27.90 16.72

PA þ Al2O3

PA þ Fe2O3

PA þ RM

PB

PB þ CaO

PB þ Al2O3

PB þ Fe2O3

PB þ RM

51 286 433 251 623

58 285 435 252 619

58 284 434 243 602

47 307 402 272 489

48 308 400 264 596

62 309 401 268 484

61 307 401 265 489

67 308 400 268 495

2.96 0.42 18.20 0.47 0.32

3.03 0.43 17.82 0.48 0.33

3.18 0.42 18.42 0.54 0.38

3.63 0.46 11.04 0.50 0.47

3.12 0.42 17.93 0.45 0.32

3.23 0.42 17.64 0.45 0.39

3.17 0.41 21.41 0.45 0.37

3.16 0.40 22.20 0.44 0.36

71 298 435 262 607

67 296 439 262 621

67 295 431 249 632

63 316 413 260 517

69 316 410 263 619

64 318 415 268 495

64 317 412 264 501

52 316 414 260 526

8.34 0.85 19.28 1.22 1.69

6.75 0.83 20.07 0.98 1.31

14.58 0.88 16.85 2.35 3.27

6.80 0.91 12.33 0.91 1.60

6.11 0.82 21.17 0.82 1.09

6.03 0.83 19.09 0.78 1.31

6.09 0.82 20.55 0.81 1.34

6.19 0.87 16.30 0.84 1.40

75 309 442 268 666

82 307 578 273 630

75 301 428 256 636

84 323 411 290 510

67 328 419 284 647

87 330 423 292 522

67 326 419 282 514

91 328 425 282 522

13.33 1.74 19.38 1.86 4.85

14.34 1.75 20.07 1.92 5.35

17.90 1.78 18.69 2.72 7.62

13.32 1.76 18.13 1.66 5.75

12.27 1.73 18.94 1.52 4.07

12.63 1.71 20.00 1.48 4.85

13.08 1.74 18.76 1.65 5.57

12.42 1.69 21.48 1.56 5.05

95 314 447 271 694

89 308 436 273 646

69 317 437 273 660

84 330 411 289 524

83 335 414 293 681

76 339 430 294 547

71 332 413 287 530

87 332 418 286 532

23.31 3.60 20.61 3.17 16.46

24.48 3.63 21.40 3.28 18.46

26.61 3.85 16.25 3.57 20.82

27.16 3.69 18.75 3.32 23.38

24.04 3.64 19.27 2.80 14.97

21.63 3.58 19.54 2.50 16.37

24.02 3.71 18.08 2.92 20.45

24.49 3.55 22.00 2.99 19.99

Tp: peak temperature; Ti: ignition temperature (intersection between tangent and tangent at the starting point of decomposition); - Rp: maximum weight loss rate; Tf: final temperature (total mass loss ¼ 98%); and - Rv: average weight loss rate (Huang et al., 2019). (PA and PB data adapted from (Song et al., 2019)).

high molecular weight was decomposed into volatiles and char while being burnt. The residual heavy molecular weight tar and char were burnt slowly in the fourth stage showing a distinct exothermic peak on the DSC curve. The enthalpy (DH) magnitude was estimated from integrating the peak area (Kok and Gundogar, 2013). The catalysts increased the DH value of the PA combustion in the following order: CaO > RM > Fe2O3 > Al2O3. The DH value of the PB combustion rose with only Fe2O3 and decreased with CaO, Al2O3, and RM, with Al2O3 exerting the strongest effect. Many oxides were produced during the decomposition of the metal oxides or other materials that possessed a strong absorption capacity for oxygen (Cheng et al., 2016). During the combustion, these oxides acted as the active oxygen carrier by transporting oxygen to the surface of the carbon. These oxides in turn lowered the resistance during the gas-phase oxygen transport to the surface of biomass (Deng et al., 2018). This mechanism increases the burning rate and reduces the ignition temperature of biomass. However, the catalysts may as well cover the sample surface which

in turn increases the oxygen diffusion resistance. In other words, the catalysts can either promote or inhibit the biomass combustion. For example, the decreased peaks of TP1 and TP2 with the catalysts for PA indicated that the catalysts affected the second stage. TP3 basically shifted back with the catalysts, while only the PA þ RM peak temperature advanced (Table 3). This may be because some components of RM penetrated the chemical bond ruptures inside of PA, thus increasing the combustion of volatiles. Almost all the four catalysts delayed the Tp2 and Tp3 values of PB, which was more pronounced at the high heating rates. The Ti values of PA and PB tended to decrease with RM. The minerals of the catalysts increased the ash deposition on the surface of the combusted samples at the burnout temperature (Tf) and were returned to the surface of the incinerator tube. With CaO, CaCO3 was decomposed at a high temperature with a peak of weight loss, while the burnout temperature of the sample grew high. The Tf value of PA tended to decrease with Fe2O3, while that of PB had the opposite tendency R and was unstable. The stationary (C ¼ 2p ), and comprehensive Ti

8

Y. Song et al. / Journal of Cleaner Production 252 (2020) 119646

combustion (CCI ¼

ðRp ÞðRv Þ ) T 2i Tf

indices were also used to evaluate

the combustion performance. The higher the C and CCI values are, the better the combustion performance is (Song et al., 2019). The C value of PB generally decreased with the catalysts. This indicates that the stability of the PB combustion was affected by the addition of the catalysts. The CCI value of PA pointed to a good combustion
performance at 20 C/min. The four catalysts changed the combustion parameters of PA and PB and affected their combustion performance in a complex way under the four heating rates.

3.4. Ash analysis 3.4.1. Chemical compositions and fusion characteristics XRF data can be used as a leverage to estimate some depositionpredictive indices. The major chemical constituents were of the following order: K2O > SiO2 > CaO > Al2O3 > MgO for PA and SiO2 > Al2O3 > CaO > K2O > Fe2O3 for PB (Fig. 3). The SiO2 content of PB was twice that of PA, while the K2O content of PA was triple that of PB. The alkali metals released during the combustion play an important role in the ash fusion and deposition through vaporization and condensation. These components form submicron ash particles when flue gas temperature decreases, ultimately condense on the heated surface and lead to a sticky initial layer of slagging (Wei et al., 2005). These ash behavior and deposition tendencies were predicted using the empirical indices for P. vittata and its catalyzed blends. The values (Table 4) of the blends were estimated as the arithmeticweighted average of the catalyzed samples. The ash fusion temperatures increased with the acid oxides and decreased with the basic oxides. The RB/A(þP) estimate is the simplest index allowing to predict the tendency of ash slagging and sintering (Liu et al., 2018). The additions of CaO and Fe2O3 significantly increased the issues of ash slagging, and sintering, RB/A(þP). The sintering risk declined for PA but rose for PB with the addition of RM. Al2O3 exerted the best control over sintering. PA had a high fouling index which was still lower than what oat straw (50.94), and wood sawdust (79.24) had (Magdziarz et al., 2018). The additions of Al2O3 and RM greatly reduced its fouling risk. PB had a less serious fouling issue than did PA, and sewage sludge (5.42) (Magdziarz et al., 2016). The high slag viscosities (SR) of PA and PB were not significantly improved with the catalysts. The additions of CaO and Fe2O3 made their slag viscosity stronger, but their SR values were lower than that of wood pine (59), and peanut shell (65) (Rizvi et al., 2015). This was also the reason why the sintering issue was more pronounced for PAþ4C than PBþ4C. The I/C ratio and melting temperature index (F) of PB were higher than those of PA. The ratio of Fe2O3 to CaO confirmed the possibility of containing or enhancing the slag-induced

Fig. 3. XRF analysis of mineral compositions of PA, PB, and RM.

formation of eutectics (Magdziarz et al., 2016). The eutectic formation was induced by Fe2O3 and RM for PA and by Al2O3 and RM for PB. Unlike Al2O3 and Fe2O3, CaO and RM effectively reduced the melting temperature of slag for PA and PB.

3.4.2. Thermodynamic equilibrium simulations Knowledge about the formation and transformation of the mineral parts of the biomass ash is essential to devising controls over the issues of fouling and slagging in boilers. Based on the minimization of the Gibbs energy of a given system, the thermodynamic model was used to analyze the main mineral evolutions (Fig. 4). For transition metals and rare earth metals, due to the large number of lattice defects and holes in the molecular structure, oxygen formed a balance between oxygen and lattice oxygen on the surface of the solids. The transportation of the external environment to the internal tunnel reduced the burning point of the combustion, and thus, accelerated the combustion (Cheng et al., 2016). With CaO, CaCO3 was the main crystalline phase of PA and decomposed completely at below 700
C. For PB, it existed mainly as anorthite (CaAl2Si2O8). With the temperature rise, KeAleSi were formed and melted. Ca dissolved into the melted K-silicate and promoted the formation of KeCaeSi. The decomposition of anhydrate (CaSO4) in PA þ CaO and PB þ CaO was completed at about 200 and 900
C, respectively. Following the decomposition of CaSO4, Ca in the PA ash was mainly converted to CaO, and Ca2SiO4, while Ca3Fe2Si3O12 in the PB ash turned mainly into CaFe2O4. With Al2O3, acidic oxides with high ionic potential were easy to combine with oxygen to form silicate and aluminosilicate-stable networks which increased the ash fusion temperatures (AFT) (Li et al., 2018a). The crystalline phases of KAlSi2O6, kaliophilite (KAlSiO4), and KAlO2 in PA were mainly in the form of AleKeSi salts. These crystalline phases suggested the reaction of K compounds in the PA ash with Al2O3 in the following possible way: KAlSi3O8 (solid) / SiO2 (solid) þ KAlSi2O6 (solid) (600
C) and KAlSiO6 / KAlSiO4 þ SiO2 (Li et al., 2019). KAlSiO4, and KAl2Si3AlO10(OH)2 were identified as the crystalline phases formed during the combustion of agricultural residues with Al2O3 (Llorente et al., 2008). Alumina was shown to be effective in reducing the sintering tendency of some biomass materials (Llorente et al., 2008). The following reactions may have occurred in PB: CaSiO3 (solid) / CaO (slag) þ SiO2 (slag) (>1100
C) and CaSO4 (solid) þ MgSiO3 (solid) CaMgSiO6 (solid) þ SO2 (gas) (900
C) (Xing et al., 2019) The amounts of eutectic formed by Fe2O3, and other compounds were so small as to be neglected. In fact, Fe2þ was usually used as a network modifier to reduce AFT, but Fe3þ can be used as an amphoteric substance to change AFT. Andradite (Ca3Fe2Si3O12) and CaFe2O4 crystalline phases were formed in the mid-stage of the PA combustion. Andradite is a low-melting point eutectic formed by the condensation reaction of CaO, Fe2O3, and SiO2 (Li et al., 2018b). The fusion of low-melting point minerals accelerated the mass and heat transfers, and thus, the ash melting. At high temperatures, the stabilized crystal was Ca2Fe2O5. RM as an alkali metal ensemble evaporated more water when combusted with PA and PB. With the temperature rise, some K salts turned into the vapor phase which dropped the K content. Ca-based carbonates and silicates were the main components of PA. The eutectic crystals such as CaMg (CO3)2, KAlSi2O6, KAlSiO4, and K2Ca2(CO3)3 had low-melting points and were produced at 800
C. The following reactions may have occurred in PB þ RM: 2KCl þ Al2O3 þ 6 SiO2 þ H2O / 2KAlSi3O8 þ 2HCl (gas) and CaSO4 þ 2SiO2 þ Al2O3 / CaAl2Si2O8 þ SO3 (gas) and 2NaCl þ Al2O3 þ 6SiO2 þ H2O / 2NaAlSi3O8 þ 2HCl (gas) (Liu et al., 2018).

Y. Song et al. / Journal of Cleaner Production 252 (2020) 119646

9

Table 4 Deposition-predictive indices and criteria for mineral compositions of (non-)catalyzed samples. Parameter (%) RM

RA RB RB/A RS Fu RB/A(þP) SR S/A I/C F

57.03 41.50 0.73 0.16 8.19 0.72 43.00 0.75 0.83 1.43

PA

27.49 49.00 1.78 0.27 40.45 1.53 41.00 2.04 0.25 2.01

PA catalyzed with

PB

CaO

Al2O3 Fe2O3 RM

20.06 62.79 3.13 0.47 51.83 3.29 22.00 2.04 0.07 0.76

47.09 35.76 0.76 0.11 12.57 0.83 41.00 0.40 0.25 2.01

20.06 62.79 3.13 0.47 51.83 3.29 22.00 2.04 2.63 2.01

34.22 47.29 1.38 0.21 27.75 1.48 41.00 1.40 0.38 1.90

59.57 28.20 0.47 0.03 3.39 0.46 65.00 2.04 0.57 3.36

PB catalyzed with

Deposition criteria

CaO

Al2O3 Fe2O3 RM

43.09 48.07 1.12 0.08 5.78 1.15 40.00 2.04 0.13 0.92

70.76 20.39 0.29 0.02 1.49 0.31 65.00 0.68 0.57 3.36

43.09 48.07 1.12 0.08 5.78 1.15 40.00 2.04 3.85 3.36

58.98 31.31 0.53 0.04 4.31 0.56 61.00 1.62 0.65 2.85

low (<0.5), medium (0.5e1), high (>1) low (<0.6), medium (0.6e2.0), high (2.0e2.6), extremely high (>2.6) low (0.6), high (0.6e40), extremely high (40) low (<0.5), medium (0.5e1), high (>1) low (>78), medium (66.1e78), high (<66.1) low (<0.31 or >3); high (0.31e3) low (<0.31 or >3), high (0.31e3) higher with higher F value

Acid, RA ¼ (SiO2þTiO2þAl2O3); Base, RB ¼ (Fe2O3þCaO þ MgO þ K2O þ Na2O) (Liu et al., 2018); Base/acid ratio, RB/A ¼ Basic/Acid; Slagging index, RS ¼ RB/ASd, Sd ¼ S% in dry fuel; Fouling index, Fu ¼ RB/A(Na2OþK2O) (Tortosa Masi a et al., 2007); Tendency of ash slagging & sintering, RB/A(þP) ¼ (Fe2O3þCaO þ MgO þ K2O þ Na2O þ P2O5)/ (SiO2þTiO2þAl2O3) (Liu et al., 2018); Slag viscosity index, SR ¼ 100SiO2/SiO2þFe2O3þCaO þ MgO (Wang et al., 2017b); Silicon/alumina ratio, S/A ¼ SiO2/Al2O3; Iron/calcium ratio, I/C ¼ Fe2O3/CaO (Garcia et al., 2015); Fusion temperature index, F ¼ (SiO2þK2O þ P2O5)/(CaO þ MgO).

Fig. 4. Thermodynamic equilibrium distributions of Ca, Al, and Fe with the addition of (a, e) CaO, (b, f) Al2O3, (c, g) Fe2O3, and (d, h) RM to the PA and PB combustions, respectively, at 20
C/min.

3.5. Kinetic triplet indices The conversion rate range of 0.05e0.95 for the entire temperature range was selected for the kinetic analysis (Song et al., 2019). However, the combustion cannot be best described using a single mechanism function f(x) but using the multiple functions according to the second and third stages. Out of more than 20 common models fitted, the D- and F-types were identified, and the F-type with a step size of 0.1 was further used to obtain an accurate solution. Finally, the best-fit reaction curve equation with the highest R2 was selected to estimate Ea and A. Since the heating rate was reported not to significantly affect Ea (Hu et al., 2019), a fixed heating rate of 20
C/min was adopted in this study. The values of pre-exponential factor (A) < 109 s1 denote a surface reaction, or a compact complex if the reaction is not dependent on the surface. The values of A > 109 s1 represent a simpler complex. Whether be (non-)catalyzed samples, those that belonged to a surface reaction or a compact complex reaction in the second stage belonged to a simpler complex reaction in the third stage. The values of A decreased with the catalysts. The Ea values of PA and PB significantly differed in the two stages (Table 5). With the catalysts, the reaction

order of PB and PA decreased markedly and slightly, respectively. The third stages of PA and PB belonged to the three-dimensional diffusion and reaction order models, respectively. F2.3 represented a random nucleation of 2.3 nuclei on an individual particle. The Ea estimates changed with the four catalysts. For PA, the catalytic reduction effect on Ea was of the following order; RM > CaO > Fe2O3 > Al2O3 in the second stage and RM > Al2O3 > Fe2O3 > CaO in the third stage. The Ea value declined with RM by 275.99% and with Al2O3 and Fe2O3 by 119.82 and 115.81%, respectively, for PA. These results showed that RM exerted a significant catalytic effect on the main combustion stage of PA. The PA combustion with RM appeared to achieve the best energy efficiency. Al2O3, and Fe2O3 led to a better catalytic effect on the combustion of secondary organic matter, and char and to no significant positive effect on the PA combustion in the second stage. For PB, CaO changed the reaction model of the second stage to a first-order diffusion model and increased Ea by 8.48%. Unlike PA, Fe2O3 exhibited a good catalytic impact on both stages for PB. RM did not change the Ea value of the third stage of PB as much as it changed the Ea value of the third stage of PA. Therefore, RM appeared to be more suitable for the PA than PB combustion.

10

Y. Song et al. / Journal of Cleaner Production 252 (2020) 119646

Table 5
Kinetic parameters of the thermal decompositions of PA, PA þ CaO, PA þ Al2O3, PA þ Fe2O3, PA þ RM, PB, PB þ CaO, PB þ Al2O3, PB þ Fe2O3, and PB þ RM at 20 C/min (Y: decrease; [: increase). Ea

f(x)

Modal

R2

Change in Ea (%)

107 107 103 106 106

84.03 77.50 84.19 83.82 77.08

(1-a)13/10 (1-a)6/5 (1-a)13/10 (1-a)13/10 (1-a)6/5

F1.3 F1.2 F1.3 F1.3 F1.2

0.9934 0.9989 0.9980 0.9955 0.9978

Y7.77 0.19[ Y0.25 Y8.27 (best)

    

106 105 103 107 106

81.53 88.66 78.85 72.70 78.92

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

F1 D1 F1 F1 F1

0.9972 0.9788 0.9917 0.9936 0.9962

8.48[ Y3.20 Y10.51 (best) Y3.12

3.06 2.15 2.19 2.89 3.08

    

1032 1016 1022 1031 1026

460.53 436.60 359.85 363.22 228.62

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

D3 D3 D2 D2 F1

0.9861 0.9916 0.9929 0.9909 0.9908

Y28.48 Y119.82 Y115.81 Y275.99 (best)

5.16 4.09 3.12 5.52 4.30

    

1019 1018 1015 1012 1016

257.33 242.47 146.91 167.55 218.16

(1-a)23/10 (1-a)2 (1-a)2 (1-a)8/5 (1-a)9/5

F2.3 F2 F2 F1.6 F1.8

0.9983 0.9982 0.9897 0.9904 0.9968

Y17.69 Y12.40 Y106.85 (best) Y46.61

Stage

Sample

A

Stage 2

PA PA PA PA PA

þ þ þ þ

CaO Al2O3 Fe2O3 Red Mud

2.26 2.06 9.73 3.15 4.92

    

PB PB PB PB PB

þ þ þ þ

CaO Al2O3 Fe2O3 Red Mud

6.17 2.39 1.64 9.13 2.28

PA PA PA PA PA

þ þ þ þ

CaO Al2O3 Fe2O3 Red Mud

PB PB PB PB PB

þ þ þ þ

CaO Al2O3 Fe2O3 Red Mud

Stage 3

Pre-exponential factor: A (s1); Activation energy: Ea (kJ/mol); Kinetic model: f(x); Coefficient of determination: R2.

4. Conclusions

Appendix A. Supplementary data

The DH values increased with all the catalysts for PA and with only Fe2O3 for PB but decreased with CaO, Al2O3, and RM, with Al2O3 exerting the stronger impact. The C value of PB decreased with the catalysts. The CCI value of PA pointed to the best perfor
mance at 20 C/min, but the catalysts did not contribute positively to PB. CaO affected the specific formation pathways of tar species and inhibited the emissions. Fe2O3 shortened the initial release time of the emissions. Al2O3 inhibited the final NO emission but did not control the main intermediate of N-containing gases. RM increased the combustion efficiency of P. vittata and suppressed the emissions. Al2O3 reduced the risks of slagging and fouling for PA and PB. RM exerted a stronger effect on PA than PB in terms of slagging, and fouling. The fusion of low-melting point minerals accelerated the mass and heat transfers, and the ash melting. RM, and Fe2O3 deceased Ea at the main combustion stage of PA, and PB, respectively. Not only did RM improve the combustion efficiency of P. vittata, but also its use in the catalytic biomass combustion decreased its waste stream.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.119646.

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments This work was financially supported by the National Natural Science Foundation of China (No. 51978175), the Scientific and Technological Planning Project of the Guangzhou (No. 201704030109) and Guangdong (No. 2018A050506046 and 2019B020208017) provinces, the Research Fund Program of Guangdong Key Laboratory of Radioactive and Rare Resource Utilization (No.2019-LRRRU04).

References bauxite.world. http://bauxite.world-aluminium.org/refining/bauxite-residuemanagement/. Cao, H., Jiang, L., Duan, Q., Zhang, D., Chen, H., Sun, J., 2019. An experimental and theoretical study of optimized selection and model reconstruction for ammonium nitrate pyrolysis. J. Hazard. Mater. 364, 539e547. Chen, J., 2018. Bio-oil production from hydrothermal liquefaction of Pteris vittata L.: effects of operating temperatures and energy recovery. Bioresour. Technol. 265, 320e327. Chen, J., Wang, Y., Lang, X., Ren, X., Fan, S., 2017. Evaluation of agricultural residues pyrolysis under non-isothermal conditions: thermal behaviors, kinetics, and thermodynamics. Bioresour. Technol. 241, 340e348. Cheng, J., Zhou, F., Xuan, X., Liu, J., Zhou, J., Cen, K., 2016. Cascade chain catalysis of coal combustion by NaeFeeCa composite promoters from industrial wastes. Fuel 181, 820e826. da Silva, E.B., Mussoline, W.A., Wilkie, A.C., Ma, L.Q., 2019. Arsenic removal and biomass reduction of As-hyperaccumulator Pteris vittata: coupling ethanol extraction with anaerobic digestion. Sci. Total Environ. 666, 205e211. Deng, S., Wang, X., Zhang, J., Liu, Z., Mikulcic, H., Vujanovic, M., Tan, H., Duic, N., 2018. A kinetic study on the catalysis of KCl, K2SO4, and K2CO3 during oxybiomass combustion. J. Environ. Manag. 218, 50e58. Duan, L., Li, X., Jiang, Y., Lei, M., Dong, Z., Longhurst, P., 2017. Arsenic transformation behaviour during thermal decomposition of P. vittata, an arsenic hyperaccumulator. J. Anal. Appl. Pyrolysis 124, 584e591. Fang, S., Yu, Z., Lin, Y., Lin, Y., Fan, Y., Liao, Y., Ma, X., 2016. Effects of additives on the co-pyrolysis of municipal solid waste and paper sludge by using thermogravimetric analysis. Bioresour. Technol. 209, 265e272. Garcia, R., Pizarro, C., Alvarez, A., Lavin, A.G., Bueno, J.L., 2015. Study of biomass combustion wastes. Fuel 148, 152e159. Ghaderi, H., Pishvaee, M.S., Moini, A., 2016. Biomass supply chain network design: an optimization-oriented review and analysis. Ind. Crops Prod. 94, 972e1000. Hu, J., Yan, Y., Evrendilek, F., Buyukada, M., Liu, J., 2019. Combustion behaviors of three bamboo residues: gas emission, kinetic, reaction mechanism and optimization patterns. J. Clean. Prod. 235, 549e561. Hu, W., Nie, Q.K., Huang, B.S., Shu, X., He, Q., 2018. Mechanical and microstructural characterization of geopolymers derived from red mud and fly ashes. J. Clean. Prod. 186, 799e806. Huang, J., Liu, J., Kuo, J., Xie, W., Zhang, X., Chang, K., Buyukada, M., Evrendilek, F., 2019. Kinetics, thermodynamics, gas evolution and empirical optimization of (co-)combustion performances of spent mushroom substrate and textile dyeing sludge. Bioresour. Technol. 280, 313e324. Jollet, V., Gissane, C., Schlaf, M., 2014. Optimization of the neutralization of Red Mud

Y. Song et al. / Journal of Cleaner Production 252 (2020) 119646 by pyrolysis bio-oil using a design of experiments approach. Energy Environ. Sci. 7 (3), 1125e1133. Kok, M.V., Gundogar, A.S., 2013. DSC study on combustion and pyrolysis behaviors of Turkish crude oils. Fuel Process. Technol. 116, 110e115. Li, F.H., Fan, H.L., Guo, M.X., Guo, Q.Q., Fang, Y.T., 2018a. Influencing mechanism of additives on ash fusion behaviors of straw. Energy Fuels 32 (3), 3272e3280. Li, F.H., Yu, B., Wang, G.X., Fan, H.L., Wang, T., Guo, M.X., Fang, Y.T., 2019. Investigation on improve ash fusion temperature (AFT) of low-AFT coal by biomass addition. Fuel Process. Technol. 191, 11e19. Li, J.Z., Xiong, Q., Shan, J., Zhao, J.T., Fang, Y.T., 2018b. Investigating a high vanadium petroleum coke ash fusibility and its modification. Fuel 211, 767e774. Liu, H., Yi, L., Hu, H., Xu, K., Zhang, Q., Lu, G., Yao, H., 2017. Emission control of NO x precursors during sewage sludge pyrolysis using an integrated pretreatment of Fenton peroxidation and CaO conditioning. Fuel 195, 208e216. Liu, Q., Wang, S., Zheng, Y., Luo, Z., Cen, K., 2008. Mechanism study of wood lignin pyrolysis by using TGeFTIR analysis. J. Anal. Appl. Pyrolysis 82 (1), 170e177. Liu, Z.J., Zhang, T., Zhang, J., Xiang, H.Z., Yang, X.M., Hu, W.H., Liang, F., Mi, B.B., 2018. Ash fusion characteristics of bamboo, wood and coal. Energy 161, 517e522. Llorente, M.J.F., Arocas, P.D., Nebot, L.G., Garcia, J.E.C., 2008. The effect of the addition of chemical materials on the sintering of biomass ash. Fuel 87 (12), 2651e2658. Long, F., Li, F., Zhai, Q., Wang, F., Xu, J., 2019. Thermochemical conversion of waste acidic oil into hydrocarbon products over basic composite catalysts. J. Clean. Prod. 234, 105e112. Magdziarz, A., Gajek, M., Nowak-Wo zny, D., Wilk, M., 2018. Mineral phase transformation of biomass ashes e experimental and thermochemical calculations. Renew. Energy 128, 446e459. Magdziarz, A., Wilk, M., Gajek, M., Nowak-Wozny, D., Kopia, A., Kalemba-Rec, I., Kozinski, J.A., 2016. Properties of ash generated during sewage sludge combustion: a multifaceted analysis. Energy 113, 85e94. Minh Loy, A.C., Yusup, S., Fui Chin, B.L., Wai Gan, D.K., Shahbaz, M., Acda, M.N., Unrean, P., Rianawati, E., 2018. Comparative study of in-situ catalytic pyrolysis of rice husk for syngas production: kinetics modelling and product gas analysis. J. Clean. Prod. 197, 1231e1243. Ren, Q., Zhao, C., 2013. NOx and N2O precursors from biomass pyrolysis: role of cellulose, hemicellulose and lignin. Environ. Sci. Technol. 47 (15), 8955e8961. Resende, E.C.D., Gissane, C., Nicol, R., Heck, R.J., Guerreiro, M.C., Coelho, J.V., Oliveira, L.C.A.d., Palmisano, P., Berruti, F., Briens, C., Schlaf, M., 2013. Synergistic co-processing of Red Mud waste from the Bayer process and a crude untreated waste stream from bio-diesel production. Green Chem. 15 (2). Rizvi, T., Xing, P., Pourkashanian, M., Darvell, L.I., Jones, J.M., Nimmo, W., 2015. Prediction of biomass ash fusion behaviour by the use of detailed characterisation methods coupled with thermodynamic analysis. Fuel 141, 275e284. Song, Y., Liu, J., Evrendilek, F., Kuo, J., Buyukada, M., 2019. Combustion behaviors of Pteris vittata using thermogravimetric, kinetic, emission and optimization analyses. J. Clean. Prod. 237.

11

Sun, Y.Q., Chen, J.J., Zhang, Z.T., 2019. General roles of sludge ash, CaO and Al2O3 on the sludge pyrolysis toward clean utilizations. Appl. Energy 233, 412e423. , A.A., Buhre, B.J.P., Gupta, R.P., Wall, T.F., 2007. Characterising ash of Tortosa Masia biomass and waste. Fuel Process. Technol. 88 (11e12), 1071e1081. Vigneshwaran, S., Uthayakumar, M., Arumugaprabu, V., 2019. Development and sustainability of industrial waste-based red mud hybrid composites. J. Clean. Prod. 230, 862e868. Wang, S.R., Dai, G.X., Yang, H.P., Luo, Z.Y., 2017a. Lignocellulosic biomass pyrolysis mechanism: a state-of-the-art review. Prog. Energy Combust. Sci. 62, 33e86. Wang, T., Hou, H.B., Ye, Y., Rong, H., Li, J.P., Xue, Y.J., 2019. Combustion behavior of refuse-derived fuel produced from sewage sludge and rice husk/wood sawdust using thermogravimetric and mass spectrometric analyses. J. Clean. Prod. 222, 1e11. Wang, Y.B., Tan, H.Z., Wang, X.B., Du, W.Z., Mikulcic, H., Duic, N., 2017b. Study on extracting available salt from straw/woody biomass ashes and predicting its slagging/fouling tendency. J. Clean. Prod. 155, 164e171. Wei, X.L., Schnell, U., Hein, K.R.G., 2005. Behaviour of gaseous chlorine and alkali metals during biomass thermal utilisation. Fuel 84 (7e8), 841e848. Xiao, K., Guan, R., Yang, J., Li, H., Yu, Z., Liang, S., Yu, W., Hu, J., Hou, H., Liu, B., 2019. Effects of red mud on emission control of NOx precursors during sludge pyrolysis: a protein model compound study. Waste Manag. 85, 452e463. Xing, P., Darvell, L.I., Jones, J.M., Ma, L., Pourkashanian, M., Szuhanszki, J., Williams, A., 2019. The use of equilibrium thermodynamic models for the prediction of inorganic phase changes in the co-firing of wheat straw with El Cerrejon coal. J. Energy Inst. 92 (3), 813e823. Yan, J., Jiang, X., Han, X., Liu, J., 2013. A TGeFTIR investigation to the catalytic effect of mineral matrix in oil shale on the pyrolysis and combustion of kerogen. Fuel 104, 307e317. Yan, X., Zhang, M., Liao, X., Tu, S., 2012. Influence of amendments on soil arsenic fractionation and phytoavailability by Pteris vittata L. Chemosphere 88 (2), 240e244. Yang, Q., Zhou, H.W., Zhang, X.Y., Nielsen, C.P., Li, J.S., Lu, X., Yanga, H.P., Chen, H.P., 2018. Hybrid life-cycle assessment for energy consumption and greenhouse gas emissions of a typical biomass gasification power plant in China. J. Clean. Prod. 205, 661e671. Yuan, R., Yu, S., Shen, Y., 2019. Pyrolysis and combustion kinetics of lignocellulosic biomass pellets with calcium-rich wastes from agro-forestry residues. Waste Manag. 87, 86e96. Zhang, J.H., Liu, J.Y., Evrendilek, F., Xie, W.M., Kuo, J.H., Zhang, X.C., Buyukada, M., 2019a. Kinetics, thermodynamics, gas evolution and empirical optimization of cattle manure combustion in air and oxy-fuel atmospheres. Appl. Therm. Eng. 149, 119e131. Zhang, Y., Shen, L., Shuai, C., Bian, J., Zhu, M., Tan, Y., Ye, G., 2019b. How is the environmental efficiency in the process of dramatic economic development in the Chinese cities? Ecol. Indicat. 98, 349e362.