Air- and oxygen-blown characterization of coal and biomass by thermogravimetric analysis

Fuel 212 (2018) 626–637

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Full Length Article

Air- and oxygen-blown characterization of coal and biomass by thermogravimetric analysis

MARK

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Mauro Mureddu , Federica Dessì, Alessandro Orsini, Francesca Ferrara, Alberto Pettinau Sotacarbo S.p.A. – Grande Miniera di Serbariu, 09013 Carbonia, Italy

G RA P H I C A L AB S T R A C T

A R T I C L E I N F O

A B S T R A C T

Keywords: Thermogravimetric analysis (TGA) Combustion Oxy-combustion Coal Biomass

This paper reports on the results of air-blown combustion and oxy-combustion kinetic characterization (comparing two different isoconversional methods: Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose) of different kinds of coal (from Italy, South Africa and Hungary) and biomass (pine and eucalyptus chips) by thermogravimetric analysis (TGA) and differential scanning calorimeter (DSC) together with the assessment of different characteristic combustion parameters. It can be observed that the burning rate of fuels can be improved by the oxy-combustion process, shortening the burning time (a mean reduction of the burnout time of 14% and 22% can be observed for coal and biomass samples, respectively). Moreover, biomass shows better ignition performance than coal and enhances combustibility indexes (S and Hf), especially in oxy-combustion conditions. For example, the S index, which reflects combustion properties, increases by an order of magnitude for biomass combustion and oxy-combustion with respect to coal values, thus indicating a higher combustion activity for biomass; an opposite trend can be observed for the Hf index, which describes the rate and intensity of the process and is lower for biomass than for coal, thus indicating better performance for wood chips combustion. Kinetic analysis shows that the activation energy Ea varies with conversion values, reflecting the kinetic complexity in both the processes. Moreover, with the same range of heating rates (10 ≤ β ≤ 50 °C/min) and for the overall range of conversion (0.1 ≤ α ≤ 0.9), both of the models used fit the experimental data in combustion regime, whereas the increase of the oxygen concentration makes the results reliable for coal samples and more sensitive to weight loss for biomass samples.

⁎

Corresponding author. E-mail address: [email protected] (M. Mureddu).

http://dx.doi.org/10.1016/j.fuel.2017.10.005 Received 3 April 2017; Received in revised form 14 September 2017; Accepted 3 October 2017 Available online 15 October 2017 0016-2361/ © 2017 Elsevier Ltd. All rights reserved.

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Nomenclature mf mi mα R R2 S T Tf tf Ti ti Tp tp α β Δt1/2 t f(α) g(α)

Acronyms CCUS DSC DTG FWO HHV KAS TG TGA

carbon capture, utilization and storage differential scanning calorimetry derivative thermogravimetry Flynn-Wall-Ozawa model higher heating value Kissinger-Akahira-Sunose model thermogravimetry thermogravimetric analysis

Symbols Df Di DTGmax DTGmean Ea Hf k(T) A

Burnout index (wt.%/min4) ignition index (wt.%/min3) maximum combustion rate (wt.%/min) mean combustion rate (wt.%/min) activation energy (kJ mol−1) combustion index (°C) reaction rate (function of the kinetic model expression) pre-exponential factor (function of the kinetic model

1. Introduction

expression) residual mass of samples (mg) initial mass of samples (mg) actual mass of samples (mg) gas constant (J/(mol ∗K)) correlation coefficient (non-dimensional) combustion index (wt.%/(min2 ∗°C3)) temperature (K) burnout temperature (°C) burnout time (min) ignition temperature (°C) ignition time (min) maximum peak temperature (°C) maximum peak time (min) conversion degree (non-dimensional) heating rate (°C/min) time range of DTG/DTGmax = 0.5 (min) time (s) kinetic model (function of the kinetic model expression) integral form of kinetic model (function of the kinetic model expression)

devolatilization, pyrolysis, gasification and combustion reactions). In particular, it is well known that the knowledge of thermal decomposition of coal and biomass is essential to assess the performance of carbonization, combustion and gasification processes [11]. Non-isothermal thermogravimetric analysis (TGA) is the most simple, the least expensive and the most effective technique to observe both the pyrolysis and combustion profiles of a fuel [12]. Fuel samples are typically heated up to 800–1000 °C in a predefined atmosphere and sample weight losses are measured continuously. Pyrolysis behaviour is typically assessed by performing the analysis in an inert (nitrogen or argon) atmosphere, whereas combustion profiles are determined by feeding the thermogravimetric analyser with an oxidant gas (air or oxygen). Detailed combustion features of the fuels, in terms of characteristics combustion parameters, can also be evaluated, including the ignition, peak and burnout temperatures and combustion rate [13]. This information is of great importance to enhance the knowledge of this process and to estimate its efficiency; it can be successfully used to establish the optimum process conditions. Several studies have been recently published on combustion performance assessment of different kinds of fuel (coal, biomass, waste, etc.) by thermogravimetric analysis. Table 1 shows a brief summary of the most interesting ones. As shown in Table 1, many investigations have been conducted and published on combustion (and sometimes co-combustion) of biomass and coal under air atmosphere. Several studies consider oxy-combustion by operating TGA in a O2/CO2 atmosphere [14,17], where CO2 shifts the equilibrium of several reactions, with a significant impact on the process. But it can be noticed that there is a real shortage of published data on oxy-combustion assessment [19,33]. So far, to the best of the present authors’ knowledge, only a few papers dealing with the use of oxygen at high concentration in oxycombustion have been published. The study of oxy-combustion of coal and microalgae in O2/N2 or O2/CO2 atmosphere at an oxygen concentration of 70–80% by volume is reported by Chen et al. (2013 and 2015) [25,34], respectively. Only for one of these mentioned works has the kinetic study with different models been performed. Only Liu et al. (2016) [19] assess the combustion performance indexes up to an oxygen concentration of 100%. In this light, a detailed investigation on the oxygen-enriched behaviour of coal and biomass, including both the evaluation of the

The utilization of fossil fuels (coal, oil and natural gas) has led an outstanding era of prosperity and advancement for human development. Nevertheless, carbon dioxide concentration in the atmosphere has consequently risen from about 280 ppm (by volume) before the industrial revolution to about 400 ppm in 2016 [1]. A further increase up to about 570 ppm could be expected by the end of this century [2]. The increase in CO2 emissions can contribute to global warming and climate changes due to the enhanced greenhouse effect. The increasing attention towards climate changes and the recent strategic policies to stabilize and reduce CO2 emissions has spurred the development of technologies for the use of renewable energy sources. In particular, the European Commission has agreed to reduce their carbon emission by 20% by 2020, by 40% by 2030 and by 80–95% by 2050, with reference to 1990 levels [3]. This is also promoting research in the field of carbon capture, utilization and storage (CCUS) technologies, whereby CO2 is captured from industrial flue gas and reused (for example for the production of liquid fuels) or permanently stored in geological formations, such as depleted oil and gas fields or saline aquifers [4]. After fossil fuels, biomass is the most important source of energy, which can supply about 14% of the world’s energy consumption [5,6]. Among renewable sources, biomass can be considered almost carbonneutral and presents the lowest risk and capital required to be used in energy generation [7]. Moreover, whereas power generation from sun and wind cannot be programmed, biomass can be used instead of fossil fuels for base-load power generation. In this context, Sotacarbo is engaged in several theoretical and experimental studies on the potential use of coal and/or biomass for distributed power generation. The aim is the development of CO2-free power generation technologies for small-scale commercial applications, including the feeding of smart grids in integration with other renewable energy sources. Several experimental campaigns have been carried out in a pilot fixed-bed up-draft gasifier [8,9] and a new bubbling fluidizedbed gasifier is currently under construction in the Sotacarbo Research Centre in Carbonia (Sardinia, Italy) [10]. The fine optimization of both the previously mentioned gasification technologies requires, among other issues, an extensive knowledge of the thermochemical processes occurring in the gasifier (i.e. 627

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Table 1 Brief review of several previous studies on combustion characterization by TGA. Source

Fuel

Max. Temp. (°C)

Heating rate (°C/min)

Atmosphere (% by volume)

Shan et al. 2017 [14] L.-González et al. 2017 [15] Zhou et al. 2016 [16] Niu et al. 2016 [17] Wang et al. 2016 [18] Liu et al. 2016 [19] Liu et al. 2015 [20] F.-Lopez et al. 2015 [21] Lin et al. 2015 [22] Magdziarz, Werle 2014 [23] Parshetti et al. 2014 [24] Chen et al. 2013 [25] Cheng et al. 2012 [26] Idris et al. 2012 [27] Lai et al. 2011 [28] Skreiberg et al. 2011 [29] Selçuk and Yuzbasi 2011 [30] Yuzbasi and Selçuk 2011 [7] Marinov et al. 2010 [31] Biswas et al. 2006 [32]

Pine and rice husk Solid animal waste Chinese bit. coal, corn, sawdust Dried sewage sludge Waste biomass and coal Corncob, hardwood, bitum. coal Herbaceous biomass, bit. coal Canadian solid animal waste Paper sludge and oil palm waste Polish coal and sewage sludge Indonesian coal and palm residue Chlorella vulgaris microalgae Cellulose, hemicellulose, lignin Malaysian oil palm Chinese municipal solid waste Wood and waste wood Turkish lignite Turkish lignite and olive residue Biodesulphurized coals Two Indian coals

800 1000 1000 1000 627 800 800 900 1000 800 900 800 600–800 1100 1000 900 950 950 800 750

Variable 10, 20, 30 15, 60 20, 40, 60, 80 2.5, 5, 10, 20 40 10–90 10 20 10 20 10, 20, 40 5 10, 20, 40, 60 10, 20, 40 5, 10, 100 40 40 10 10

21–100% (O2/CO2) Var. (O2/CO2-Ar) Air 21–40% O2 (+ CO2) Air 21–100% O2 (+N2) Air 21% O2, 79% Ar 21% O2, 79% N2 20% O2, 80% N2 30% O2, 90% N2 Var. (O2/N2-CO2) Air Air Variable (O2/N2) 21% O2, 79% Ar Air Air Air Air

As shown in Table 2, coal typically has higher heating value than biomass (except for the Hungarian coal, characterized by a very high ash content). This is mainly due to its higher carbon content. Amongst biomass, stone pine has a higher heating value than eucalyptus. It is interesting to underline the higher volatile content of Sulcis and Hungarian coal with respect to South African coal; this parameter has a strong impact on the combustion and oxy-combustion performance and influences coal reactivity. In particular, it is difficult to ignite fuel due to the absence of inherent oxygen and low volatile matter content. Finally, the very high sulphur content in both Hungarian and Sulcis coal can be observed.

combustion performance indexes and the kinetic study, seems worthy of being undertaken. This work presents the main results of a non-isothermal TGA characterization of different kinds of coal and biomass, carried out to define the kinetic process during combustion (with both air and oxygen) to support the pilot-scale gasification tests. The analysis integrates the results of a preliminary assessment on the pyrolysis process [35,36]. The experimental scheme adopted for this study can be summarized as following: (i) characterization of fuels (proximate, ultimate and calorimetric analyses), (ii) preparation of samples, (iii) non-isothermal TGA for the assessment of the key process parameters, (iv) kinetic analysis (carried out with different methods to compare the results).

2.2. Methods

2. Experimental

Simultaneous thermogravimetry (TG) and differential scanning calorimetry (DSC) analyses have been carried out under dynamic conditions, as a function of time and temperature, in a Mettler Toledo TGA/ DSC 3+ thermogravimetric analyser, by heating samples from ambient temperature to 1000 °C. In order to avoid heat and mass transfer limitations, a small amount of the sample (8–10 mg) has been loaded into a 70 µl aluminium oxide crucible. All the experiments have been performed under non-isothermal conditions at atmospheric pressure, under air or pure oxygen atmosphere (both provided in bottles with controlled

2.1. Materials and sample preparation Different kinds of fuel, selected as the most representative among those tested in the Sotacarbo experimental units [9,35–37], have been characterized: (i) a high ash South African bituminous coal; (ii) a high sulphur sub-bituminous coal from the Sulcis coal mine (south-west Sardinia, Italy); (iii) a high sulphur brown coal from Miskolc basin (northern Hungary); (iv) stone pine (Pinus pinea) wood chips and (v) eucalyptus (Eucalyptus regnans) wood chips, both from Sardinian forests. A sample (about 200 g) of each fuel is crashed in a cross beater mill (Retsch SK100) and sieved in order to obtain a particle size lower than 125 μm. It is dried in an oven with a constant temperature of 105 ± 2 °C for at least 24 h and then stored in a desiccator to prevent moisture absorption from the atmosphere [38,39]. The choice to operate the experimental tests with dry samples comes from an experimental evaluation described in previous works [35,36]. Table 2 shows proximate, ultimate and calorimetric analyses (the latter to determine higher heating value, HHV) of the considered fuels; the analyses have been carried out in the Sotacarbo laboratories according to the international standards. In particular, proximate analysis has been performed by a LECO TGA-701 thermogravimetric analyser (based on the ASTM D 5142-04 “Moisture Volatile Ash” standard); ultimate analysis has been carried out on a LECO Truspec CHN/S analyzer (based on ASTM D 5373-02 for carbon, hydrogen and nitrogen, and ASTM D 4239-05 for sulphur); finally, higher heating values have been measured by a LECO AC-500 calorimeter according to the ISO 1928:1995 standard.

Table 2 Primary fuel characterization (dry basis). (i) S. African

(ii) Sulcis

(iii) Hungarian

(iv) Stone pine

(v) Eucalyptus

Proximate analysis (% by weight) Fixed carbon 77.51 Volatiles 14.38 Ash 8.11

42.55 43.27 14.18

24.00 32.57 43.43

23.23 73.74 3.03

20.38 72.41 7.21

Ultimate analysis (% by weight) Total carbon 85.88 Hydrogen 2.84 Nitrogen 1.50 Sulphur 0.56 1 Oxygen 1.11 Ash 8.11

63.22 4.43 1.59 7.14 9.44 14.18

35.80 2.86 0.70 4.88 12.33 43.43

50.88 6.71 0.50 0.08 38.80 3.03

48.30 5.90 0.12 0.00 38.47 7.21

Calorimetric analysis (MJ/kg) Higher heating value 31.77

25.31

13.69

19.64

17.57

1

628

Note: By difference.

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concentrations) with a flow rate of 100 ml/min and using 70 ml/min nitrogen as purge gas. All the samples have been heated from 25 °C to 105 °C at which they have been held for 10 min to ensure a complete removal of moisture, and then further heated up to 1000 °C at different heating rates of 10, 20, 30, 40 and 50 °C/min and held at this temperature for 10 min to complete the process. Blank experiments have been carried out to obtain the baselines, used to subtract the buoyancy effect, calibrating the experiments with samples [22,28]. All the samples have been analysed under exactly the same conditions [23] and the experiments have been replicated three times to decrease the error of the experimental results and to determine their reproducibility [40].

curve tangentially before the thermal effect and re-joins the curve in the same way after the effect. A spline baseline type obtained using a flexible ruler to manually interpolate between two points (known as Bezier curve) has been used for all the samples; this bow-shaped or Sshaped baseline is based on the left and right tangents. DSC profiles for all the fuels have been recorded under the same operating conditions and simultaneously to TG curves. 2.4. Kinetic models: FWO and KAS The results of thermogravimetric analysis at different heating rates can be used to calculate the kinetic parameters. The general kinetic description of solid-state reaction, constituted by a single process, is based on the following assumptions.

2.3. TG and DSC: key parameters The obtained TG and DTG (derivative thermogravimetric) curves have been used to evaluate the combustion and oxy-combustion parameters that reflect the thermal behaviour [41] such as ignition temperature (Ti), burnout temperature (Tf) and maximum peak temperature (Tp). In particular, Ti represents the temperature at which the sample starts to burn and is determined by the TG-DTG tangent method [13,17,42]; it indicates how easily a specific fuel ignites. Tf represents the point at which fuel oxidation is completed; it can be defined as the temperature at which the combustion rate decreases to 1 wt.%/min at the end of the combustion process [43]. Tp represents the temperature corresponding to the peak of the DTG profile [44]. The characteristic temperatures have been correlated with different combustibility indexes such as the ignition index (Di), the burnout index (Df), and the socalled “combustion indexes” (S and Hf). In particular, Di is defined as:

DTGmax t p ti

Di =

i) The reaction rate, defined as the change in conversion per unit of time, is a function of the conversion degree (α):

dα = k (T ) f (α ) dt

where t is the time, k(T) the constant rate and f(α) is the kinetic model. The conversion α is defined as follows:

α=

−Ea

k (T ) = A exp RT

(1)

DTGmax Δt1/2 tp t f

iii) Substituting k(T) defined in Eq. (7) into Eq. (5) results that: −Ea dα = A exp RT f (α ) dt

⎜

(8)

iv) For the experiments performed under non-isothermal conditions, it is possible to introduce the heating rate β = dT/dt obtaining the follow equation: −Ea dα A = ⎜⎛ ⎟⎞ exp RT f (α ) dT β ⎝ ⎠

(3)

where DTGmean represents the mean combustion rate, Ti the ignition temperature and Tf the burnout temperature. The larger the value of S index, the higher combustion activity is. Hf describes the rate and the intensity of the combustion process:

Δt1/2 ⎞ Hf = Tpln ⎛ DTG mean ⎠ ⎝

(7)

where Ea is the activation energy, T the absolute temperature, R the gas constant and A the pre-exponential factor.

(2)

DTGmax DTGmean Ti2 Tf

(6)

ii) The temperature depends on the constant reaction rate, k(T), described by the Arrhenius equation:

where Δt1/2 is the time range of DTG/DTGmax = 0.5 and tf is the burnout time. The S index reflects the ignition, combustion and burnout properties of a fuel [45]:

S=

(mi−mα ) (mi−mf )

where mi is the initial mass of sample, mα the actual sample mass and mf the residual mass after the process.

where DTGmax is the maximum combustion rates, tp is the corresponding time of DTGmax, ti is the ignition time. The larger Di value, the more volatile compounds are separated from fuel, so combustion occurs easily in the early stage. Df is defined as:

Df =

(5)

(9)

The non-isothermal kinetic study of the combustion process is very complex due to the presence of several components and consecutive or parallel reactions. It is possible to classify the kinetic methods in modelfitting or model-free mode, and the latter allows to determine the activation energies independently on the particular mechanism that govern the transformation. Most of the model-free methods are isoconversional, allowing the assessment of a given parameter as a function of conversion. In this work, both isoconversional Flynn-WallOzawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods have been used to calculate the activation energy for combustion and oxy-combustion of coal and biomass samples. The FWO model is a linear integral method based on Doyle’s approximation [49] which allows activation energy to be obtained from the plot of natural logarithm of heating rates (lnβ) versus (T)−1. The latter represents the linear relation of a given value of conversion at different heating rates [50,51]:

⎟

(4)

where Tp is the corresponding temperature of DTGmax and ΔT1/2 is the temperature range of DTG/DTGmax = 0.5 [46]. The smaller the value of Hf index, the better combustion properties are [47,48]. DSC analysis has been used in order to measure the heat flow during combustion and oxy-combustion processes, allowing to detect endothermic and exothermic reactions in the fuels and determining temperatures that characterize the peaks or other effects. The integrated area, calculated using the Mettler Toledo STARe software, is that between the DSC curve and a baseline. The choice of the right baseline is crucial for the evaluation of the reaction. The interpolated baseline for the determination of reaction enthalpy leaves the DSC

lnβ = −1.052

629

Ea AEa ⎞ −5.331 + ln ⎜⎛ ⎟ RT ⎝ Rg (α ) ⎠

(10)

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g (α ) =

∫ 0

1 dα f (α )

(11)

where the integral form of kinetic model g(α) is constant at a given value of conversion and Ea is the activation energy as a function of α. The activation energy Ea can be calculated from the slope of the straight line (−1.052 Ea/R). The KAS model is a linear integral method based on Coats-Redfern approximation [52] and described by the following equation [53,54]:

β AR ⎞ Ea ln⎛ 2 ⎞ = ln⎛⎜ ⎟− ⎝T ⎠ ⎝ Ea g (α ) ⎠ RT

(12)

where the activation energy can be calculated from the slope of the straight line (−Ea/R) obtained from the plot of ln(β/T2) versus (T)−1. 3. Results and discussion The thermogravimetric experimental results for the considered fuels are represented and compared by using thermogravimetric (TG) profiles, differential thermogravimetric (derivative of the TG profile, DTG) curves and differential scanning calorimeter (DSC) profiles. DTG peaks correspond to the inflection points of the TG profiles and represent the maximum weight loss rates. 3.1. Thermal and calorimetric analyses The burning features obtained from thermal analysis technique have been used to effectively compare the reactivity and combustibility of solid fuels. Combustion and oxy-combustion profiles for the considered fuels are shown in Figs. 1–3, which show the weight loss curve, derivative thermogravimetric evolution profiles and differential scanning calorimeter profiles, respectively, all obtained at a heating rate of 20 °C/

Fig. 2. DTG profiles in combustion (a) and oxy-combustion (b) modes at β = 20 °C/min.

Fig. 1. TG profiles in combustion (a) and oxy-combustion (b) modes at β = 20 °C/min.

Fig. 3. DSC profiles in combustion (a) and oxy-combustion (b) modes at β = 20 °C/min.

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of fixed carbon. The exothermic peaks shift to lower temperatures and narrow with the increasing of the oxygen content. The limits of the two peaks for coal samples become more obvious in oxy-combustion regime than that in a combustion one, because the higher oxygen concentration enhances the burning rate of volatile compound; on the contrary, biomass samples show only one clear peak in an oxy-combustion regime due to the high oxygen amount. Figs. 4 and 5 show DTG and DSC profiles obtained at different heating rates (β = 10, 20, 30, 40 and 50 °C/min) in both combustion and oxy-combustion modes for one coal (Sulcis) and one biomass (pine), selected as the most representative ones. For both the samples, during air-blown combustion (Fig. 4a and c) the main peak shifts slightly towards a higher temperature with the increase of β due to heat transfer limitation [57], and the combustion temperature range becomes wider, showing that an increase of β tends to postpone the thermal decomposition process. As shown in Fig. 4, with an increasing heating rate, DTG profiles shift to higher temperatures, and the rate of weight loss increases since the heat transfer is not as effective as it was for lower heating rates. This is indicative that the decomposition process is slower with the increasing of β. The maximum combustion rate of samples increases when the heating rate is increased, as heightening of mass transfer results from the enhancement of heat transfer. This can be reasonably attributed to the fact that the heating of solid particles occurs more gradually at lower heating rates, thus leading to an improved and more effective heat transfer to the inner portions and among the particles. It has also been found that burnout temperature increases when the heating rate is increased; this is confirmed by the greater temperature gradient through the inside and outside the particle, which does not promote the release of volatile matter. The DSC curves in a combustion atmosphere (Fig. 5) follow the same behaviour of the DTG profiles, showing a band that shifts toward higher temperatures and becomes wider with the increase of the heating rate, especially for Sulcis coal (Fig. 5a). In addition, with the increase of the heating rate, temperature rises very quickly involving an overlapping of the decomposition peaks, since the individual reaction does not have enough time to be completed; this phenomenon is mainly visible on a biomass sample (Fig. 5c). On the other hand, an oxy-combustion atmosphere results in a narrower temperature range and the increasing of the heating rate has the same effect as for the combustion regime. Nevertheless, with the increase of the oxygen concentration, the exothermic peak results at lower temperatures (between 200 °C and 400 °C for biomass and coal samples, respectively, for the highest heating rates) showing that an increase of oxygen concentration tends to hasten the thermal decomposition process (Fig. 5b and d). From the DTG curve (Fig. 4b and d) it is possible to observe that both samples do not present relevant differences in terms of maximum temperature of the main decomposition peak with the increase of the heating rate. The same curves for all the other considered fuels have been reported as Supplementary Material. Table 3 reports the heat released during the combustion and oxycombustion processes of all the considered fuels, based on the area under the peaks of the DSC curve. Enthalpy values are in the range 8.3–22.5 kJ/g for the combustion process, and 8.4–23.2 kJ/g for the oxy-combustion process, pointing out a poor variation of the enthalpy of reaction. These findings confirm that coal samples are more energetic compared to biomass samples and that the oxygen concentration has only a slight effect on the enthalpy of reaction. The combustion and oxy-combustion characteristic parameters for all the fuels at heating rate of 30 °C/min are reported in Tables 4 and 5, respectively. As shown in Table 4, the peak temperatures (Tp) of coal samples are in the range of 527–668 °C, higher than those of biomass (337–345 °C); this indicates that the maximum reaction rate of biomass occurs at lower temperatures than for coal. The ignition temperatures (Ti) follow the same trend, showing the higher value for South African coal, indicating the low reactivity of this fuel.

min (chosen as reference temperature by several authors, as can be seen in Table 1). In general, in the DTG curves, the peak’s height can be considered directly proportional to the reactivity, whereas the temperature corresponding to the peak is inversely proportional to it [55]. According to the general TGA results, it can be observed that it is possible to divide the combustion process into two stages (which can be classified on the basis of weight and heat changes): (i) combustion stage, with the oxidation and volatile matter release, which is an exothermic process, and oxidation of the carbon residue; (ii) burnout stage. Both TG and DTG profiles mainly display different trends. During the combustion stage, all the curves show a main peak (Fig.2a) that indicates the temperature corresponding to the maximum reaction rate. The whole of the above TG results indicates that the overall weight loss is significantly higher for biomass (weight loss of pine > weight loss of eucalyptus) with respect to coal (weight loss of Sulcis > weight loss of South African > weight loss of Hungarian). Furthermore, it seems that oxygen concentration (air or pure oxygen) does not significantly influence the overall mass loss of each fuel, thus indicating that, in all the considered cases, the combustion is almost complete. On the contrary, it is worth noting that the high oxygen concentration shifts TG and DTG curves to the low temperature area (see Fig. 1b and Fig. 2b), indicating the advance in combustion and complete combustion at lower temperatures. From the DTG point of view (Fig. 2), the differences between coal and biomass are clear. Due to a high content of cellulose, hemicellulose and lignin with a low degree of order, the temperature at which weight loss begins is lower for biomass than for coal. The peaks in coal combustion and oxy-combustion profiles are wide (there is not a clear peak temperature), in contrast to biomass profiles; in particular, under higher oxygen concentration (Fig. 2b), the maximum value of DTG curves increases and a shorter time is required to reach that value. This is due to the devolatilization of coal, which progresses more slowly compared to biomass and partially overlaps with char oxidation. A different trend can be noticed for South African coal, especially in the combustion process (Fig. 2a): due to its low reactivity, the corresponding DTG curve does not present a marked peak and only at a high heating rate (40 and 50 °C/min) is it possible to observe a shoulder at a high temperature, superimposed to the main peak. Biomass samples (pine and eucalyptus) present marked peaks, proportional to fuel reactivity, even if a shoulder, or a nearly separated peak (located at higher temperatures), can be observed, overlapping to the main peak [56]. The first significant peak at 330–340 °C for the two biomass samples may be associated with the degradation of hemicellulose and cellulose, while the second peak (or shoulder, depending on the heating rate) may be attributed to the degradation of lignin [29]. Although lignin degradation occurs in the wide temperature range, dechotomizing it from hemicellulose and cellulose decomposition is difficult (as can be seen in Fig. 2). The peak complexity at a higher heating rate can be observed due to a relatively high content of hemicellulose degradation at lower temperatures than cellulose. The decomposition energy of the volatile matter from biomass is relatively weaker than the char reaction, and this phenomenon accounts for the high reactivity. Fig.3 shows the experimental thermograms for all the samples. The combustion and oxy-combustion behaviour of coal and biomass seems to be slightly different. As in the TG and DTG profiles, the combustion DSC curves (Fig.3a) present two distinct phenomena in all the fuels, whereas in oxy-combustion DSC profiles (Fig.3b) it is possible to reveal only one clear peak in both biomass samples and partially overlapped peaks in coal samples. Ideally, DSC curves obtained from a chemical reaction show just one single smooth peak. In practice, the shape of the peak is often distorted by overlapping reactions or decomposition reactions occurring at different temperatures (depending on the solid fuels, generally included between 350 and 750 °C). As expected, all the main peaks are exothermic; the first region is due to the combustion of light volatile matters and the second region represents the combustion 631

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Fig. 4. DTG profiles in combustion for Sulcis coal (a) and stone pine biomass (c), and oxy-combustion for Sulcis coal (b) and stone pine biomass (d) at different heating rates.

be concerned, related to the ignition temperature. The possible reasons are the effect of ash content on ignition performance because high ash content can affect oxygen diffusion and heat transfer. As reported in Table 2, ash content for the Hungarian coal is much higher than that of Sulcis coal, resulting in the greater resistance of oxygen diffusion and

The ignition behaviour can be assessed by the ignition index (Di); as reported in Table 4, Sulcis and South African coal present an expected trend: the higher ignition index corresponds to the lower ignition temperature. A different behaviour has been observed for the Hungarian coal, implying that the ignition index may not be only factor to

Fig. 5. DSC profiles in combustion for Sulcis coal (a) and stone pine biomass (c), and oxy-combustion for Sulcis coal (b) and stone pine biomass (d) at different heating rates.

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if compared with coal, biomass presents a higher oxygen content and a lower amount of carbon. In the literature, it is reported that the hydrogen and oxygen indexes (H/C and O/C ratios, respectively) in fuels result in different bonding energies, as described by a van Krevelentype diagram, because the bond energy of carbon–oxygen is lower than that of carbon–carbon bonds [58]. These component characteristics can induce faster ignition, as shown in Table 4. Furthermore, the enhanced reactivity of the volatile matter with the enriched oxygen of biomass results in higher mass weight loss with respect to temperature, if compared with coals. These results are consistent with previously described findings [59]. A possible reason could be the effect of ash content on ignition performance since high ash amounts can affect oxygen diffusion and heat transfer [60]. The connection with the ignition and burnout temperatures may be highlighted; it can be explained by the burnout process, which is affected by the ignition process. Regarding the burnout temperature, the low values for biomass samples are a clear indication of a reduced presence of unburnt compounds; on the contrary, high burnout temperature values are an indication of the difficulty in burning of coal samples, thus requiring longer residence times and higher temperatures for completing conversion. As reported in Table 4, the values of S index for biomass are greater than that for coal, indicating a better combustion performance of the former. This behaviour follows the results of ignition index. Finally, the trend of Hf index is in accordance to the ignition and burnout indexes (Di, Df), showing smaller values for biomass samples, thus confirming their better combustion properties. In contrast, the high Hf value for South African coal is, again, a clear indication of the poor reactivity of the fuel. Table 5 summarizes the same parameters referred to the oxy-

Table 3 Enthalpy of reaction values during the combustion and oxy-combustion processes. Coal

Biomass

Sulcis

S. African

Hungarian

Stone pine

Eucalyptus

Combustion Entalphy of reaction (kJ/g) 10 °C/min 20 °C/min 30 °C/min 40 °C/min 50 °C/min

13.4 12.9 13.5 14.5 16.4

18.6 19.2 21.4 22.5 22.5

15.8 15.7 13.6 14.9 15.3

8.3 11.7 11.8 15.9 16.3

14.1 13.7 11.3 10.8 10.5

Oxy-combustion Entalphy of reaction (kJ/g) 10 °C/min 20 °C/min 30 °C/min 40 °C/min 50 °C/min

23.2 17.3 17.1 16.8 17.3

19.4 19.9 19.2 18.6 17.6

18.0 18.9 12.4 15.4 16.8

8.7 8.4 11.2 10.1 10.9

12.6 12.3 13.4 13.1 9.1

the higher temperature gradient between the inside and outside of the particle. This implies a lower temperature than that of the Sulcis coal even if the ignition index of the Hungarian sample is lower (Di = 7.4) than that of the Sulcis one (Di = 8.5). Same behaviour has been observed for biomass samples, although the ignition temperatures are lower than those of the coal samples. Also in this case, eucalyptus samples present a higher ash content compared to that of the stone pine samples which is reflected in the lower ignition temperature. Moreover,

Table 4 Main combustion parameters. Coal

Ignition temperature Ti (°C) Peak temperature Tp (°C) Burnout temperature Tf (°C) Ignition time ti (min) Peak time tp (min) Burnout time tf (min) DTGmax (%/min) DTGmean (%/min) ΔT1/2 (°C) Di (wt.% /min3 *10−3) Df (wt.% /min−4 *10−5) S (wt.% /(min−2 *°C−3)*10−8) Hf (°C *103)

Biomass

Sulcis

S. African

Hungarian

Stone pine

Eucalyptus

371.2 527.3 768.5 27.0 32.2 40.4 7.4 1.4 351.0 8.5 1.6 9.7 2.0

560.1 668.3 904.7 33.3 36.9 44.8 8.6 1.3 301.5 7.0 1.7 4.1 2.4

328.4 529.3 635.7 25.6 32.3 35.9 6.1 1.0 263.5 7.4 2.0 9.1 2.0

299.2 345.0 526.5 24.6 26.1 32.2 25.6 1.6 57.1 40.0 53.5 84.9 0.3

295.5 337.5 527.7 24.5 25.9 32.3 22.9 1.5 63.2 36.1 43.4 73.7 0.3

Table 5 Main oxy-combustion parameters. Coal

Ignition temperature Ti (°C) Peak temperature Tp (°C) Burnout temperature Tf (°C) Ignition time ti (min) Peak time tp (min) Burnout time tf (min) DTGmax (%/min) DTGmean (%/min) ΔT1/2 (°C) Di (wt.% /min3 *10−3) Df (wt.% /min−4 *10−5) S (wt.% /(min−2 *°C−3)*10−8) Hf (°C *103)

Biomass

Sulcis

S. African

Hungarian

Stone pine

Eucalyptus

395.4 434.7 622.0 27.7 28.8 35.5 36.1 1.4 214.2 45.2 16.5 53.2 0.77

489.0 574.5 648.6 30.8 33.3 36.3 20.3 1.3 110.0 19.8 15.2 17.5 1.0

347.3 364.6 514.4 26.1 26.5 31.8 33.8 1.1 13.3 44.5 273.9 52.7 0.31

297.0 308.2 350.5 24.4 24.6 25.3 154.1 1.6 21.8 256.5 1131.9 782.5 0.60

290.2 302.1 348.8 24.2 24.4 25.3 127.4 1.5 22.9 215.6 903.4 634.1 0.52

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combustion process. It can be observed that the values of Ti slightly change for the two biomass samples, thus indicating that the initial volatile evolution is not (or weakly) affected by the oxygen concentration. On the other hand, there is a fluctuating behaviour for coal samples; Sulcis and Hungarian coals show the same change pattern, which are different from the South African one. This can be related to the volatile content of the samples, which affects the amount of oxygen functional groups [61]. The effect of oxygen concentration is more remarkable on peak and burnout temperatures, showing an outstanding decrease passing from a combustion to an oxy-combustion process. Comparing the results shown in Tables 4 and 5, it can be observed that the combustibility indexes for the oxy-combustion process are higher than those referred to the combustion one; this indicates that a higher oxygen concentration can improve the combustion performance of both coal and biomass samples. This evolution is more significant on biomass compared to coal. To summarize, oxy-combustion atmospheres exhibit the best ignition performance and best combustion activity with a higher ignition index and higher combustion index, in comparison with a combustion regime. As can be seen from the comparison between Tables 4 and 5, the values of the combustibility indexes (Di, Df, S) increase with the increase of the amount of oxygen, which indicates that enriched-oxygen atmospheres can improve the combustion performance, irrespective of the considered fuels (coal or biomass). The result of the comprehensive index (Hf) is also in agreement with the results of the ignition index and the burnout index. 3.2. Kinetic analysis As mentioned, kinetic analysis has been performed by using both the FWO and KAS methods. For example, Figs. 6 and 7 show, for Sulcis coal, the plots for activation energy calculation at various conversions by both the methods, respectively.

Fig. 7. Activation energy calculation in combustion (a) and oxy-combustion (b) modes by KAS method for Sulcis coal.

According to different studies [15,25], all the conversion ranges (0.1 ≤ α ≤ 0.9) and heating rates (β = 10, 20, 30, 40 and 50 °C/min) have been used to investigate better the effect of the different oxygen concentrations on the kinetic parameters for all the considered fuels, determining the variation of activation energy with the conversion. According to these models, when α is constant, the values of lnβ and ln (β/T2) versus T−1, obtained at different β values, have been correlated by a straight line, whose slope is associated with the activation energy. As shown in Tables 6–10, it can be observed that the correlation coefficient (R2) of most curves are within the range of 0.9–0.99, and most of the activation energy values calculated with the two methods are in good accordance. The analysis shows that the activation energy varies with conversion values, reflecting the kinetic complexity in combustion and oxycombustion regimes. The activation energy values in combustion regime, as a function of conversion degree (α) are reported in Fig. 8a. It is possible to observe a different behaviour between coal and biomass samples in both processes. Coal samples show a clear asymptotic trend with a reduction of the Ea with the increasing of the conversion degree, whereas the trends for biomass samples show a maximum at α = 0.4, followed by a progressive reduction of Ea down to α = 0.9. The unexpected result about the higher activation energy in both of the biomass samples can be reasonably attributed to the energy required for the beginning of hemicellulose and cellulose degradation [27]. These findings further support the idea that no simple correlation exists between the activation energy and volatile matter of biomass. For all coal and biomass samples, the significant decreasing of the activation energy could be associated with an intense burning of volatile matter. The values of activation energy in oxy-combustion regime, as a function of conversion, are presented in Fig. 8b. As far as the coal is

Fig. 6. Activation energy calculation in combustion (a) and oxy-combustion (b) modes by FWO method for Sulcis coal.

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Table 6 Activation energy (Ea) and correlation coefficient (R2) at different conversion degree (α) for Sulcis coal. Sulcis Coal

α

FWO method

Table 9 Activation energy (Eα) and correlation coefficient (R2) at different conversion degree (α) for stone pine chips.

KAS method

Stone pine chips

Ea (kJ mol−1)

R2

Ea (kJ mol−1)

R2

α

FWO method

KAS method

Ea (kJ mol−1)

R2

Ea (kJ mol−1)

R2

Combustion

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

253.7 173.1 129.6 103.5 81.4 65.6 54.5 48.2 42.7

0.993 0.997 0.999 0.998 0.993 0.989 0.986 0.982 0.976

256.9 170.4 124.7 96.4 72.3 52.2 41.1 22.2 27.7

0.992 0.997 0.998 0.998 0.989 0.982 0.971 0.958 0.937

Combustion

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

160.4 180.2 188.1 192.0 179.4 168.3 128.0 102.0 62.4

0.999 0.997 0.996 0.992 0.995 0.995 0.982 0.953 0.902

159.6 179.6 187.9 192.1 178.0 158.1 117.8 90.9 50.4

0.998 0.997 0.996 0.991 0.994 0.994 0.978 0.941 0.855

Oxy-combustion

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

– 209.8 194.2 207.9 190.9 145.8 120.1 122.9 152.9

– 0.897 0.927 0.899 0.868 0.898 0.877 0.915 0.934

– 209.9 192.8 206.9 188.7 140.5 113.7 116.2 147.3

– 0.887 0.919 0.889 0.853 0.881 0.852 0.896 0.922

Oxy-combustion

0.1 0.2 0.3

161.2 205.5 373.8

0.999 0.992 0.907

160.5 207.0 383.3

0.999 0.991 0.903

concerned, a different trend in the Ea versus α plot is clearly visible. A significant variation of the activation energy with conversion indicates a kinetically complex process. In the literature, the commonly reported feature for this behaviour [15,62] is the dependence of Ea with the conversion rate in dynamic experiments due to the change in the oxidation mechanism. In this light, the dependence of Ea on α can be separated into two distinct regions with similar Ea values. For Sulcis coal, the first region, in which iso-conversional Ea can be considered to be stable, corresponds to 0.2 < α < 0.5; the second region, in which a different variation of Ea has been observed, is defined by 0.5 < α < 0.9. These two regions fit well with the two main stages of the oxidation process discussed above; the low conversion range is associated with the devolatilization stage, whereas the high conversion range corresponds to the oxidation of the sample. South African coal shows a similar behaviour with a higher value of Ea in the first region 0.1 < α < 0.3, followed by a second region, wider than Sulcis coal, between 0.4 < α < 0.9, where the activation energy is constant. Based on the findings of the obtained results, the higher activation energy characterizes the low conversion area, corresponding to the devolatilization stage; this can be ascribed to the change in the oxycombustion mechanism from chemical kinetic control (rate limiting) to diffusion-chemical kinetic control (fast step) [15,62]. Hungarian coal shows a fluctuating behaviour, probably associated with different oxidation mechanisms. The oxy-combustion behaviour of biomass has only been assessed with conversion values up to 0.3; as later explained, this is due to the loss of linearity of the straight line of the activation energy calculation, so that the alpha values up to 0.9 would not have represented a clear indication on the behaviour of the biomass sample in

Table 7 Activation energy (Ea) and correlation coefficient (R2) at different conversion degree (α) for South African coal. α

South African Coal

FWO method

KAS method

Ea (kJ mol−1)

R2

Ea (kJ mol−1)

R2

Combustion

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

124.8 97.2 81.4 67.2 55.3 47.4 41.9 39.5 40.3

0.996 0.994 0.989 0.979 0.968 0.962 0.961 0.971 0.980

177.2 88.1 69.8 54.9 42.4 31.6 25.3 23.0 22.9

0.995 0.992 0.984 0.962 0.934 0.907 0.887 0.901 0.933

Oxy-combustion

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

170.7 185.7 146.2 129.1 115.4 105.9 99.6 98.8 97.2

0.980 0.995 0.997 0.993 0.995 0.992 0.990 0.987 0.984

166.3 182.9 140.5 122.2 108.1 97.3 90.6 85.6 87.3

0.977 0.994 0.997 0.927 0.994 0.990 0.986 0.982 0.978

Table 8 Activation energy (Ea) and correlation coefficient (R2) at different conversion degree (α) for Hungarian coal. Hungarian Coal

α

FWO method Ea (kJ mol

−1

)

Table 10 Activation energy (Ea) and correlation coefficient (R2) at different conversion degree (α) for eucalyptus chips.

KAS method R

2

Ea (kJ mol

−1

Eycalyptus chips )

α

2

FWO method Ea (kJ mol

R

−1

)

KAS method R

Ea (kJ mol−1)

R2

2

Combustion

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

137.5 150.2 177.8 97.2 83.8 76.7 70.3 62.4 56.9

0.867 0.987 0.994 0.992 0.985 0.974 0.966 0.952 0.939

135.5 148.0 112.2 89.8 75.7 64.8 57.7 49.0 42.68

0.852 0.984 0.992 0.989 0.979 0.963 0.950 0.923 0.896

Combustion

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

160.4 176.2 199.9 201.5 178.6 153.3 120.9 90.9 72.3

0.999 0.999 0.998 0.996 0.996 0.973 0.951 0.929 0.903

159.8 175.4 200.4 201.2 177.9 143.1 109.9 79.0 60.0

0.999 0.999 0.997 0.995 0.995 0.972 0.941 0.907 0.864

Oxy-combustion

0.1 0.2 0.3

118.6 165.2 173.1

0.954 0.988 0.985

116.4 163.8 171.3

0.947 0.987 0.983

Oxy-combustion

0.1 0.2 0.3

158.1 173.1 429.1

0.997 0.997 0.988

163.8 213.7 442.3

0.996 0.987 0.714

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thermogravimetric analysis on two different kinds of solid fuels (coal and biomass) in combustion and oxy-combustion conditions. TG results indicate that the overall weight loss is significantly higher for biomass (in particular for eucalyptus chips) than for coal (mainly for Sulcis type), but it is not significantly influenced by oxygen concentration. The peaks in coal combustion and oxy-combustion DTG profiles are wide (except for South African coal), whereas marked peaks can be noticed for biomass. Oxy-combustion process can improve the burning rate of the considered fuels, shortening the burning time (25–36 min) with respect to air-blown combustion (32–49 min). Most of the transformations occur in a relatively narrow range of temperatures (around 300–670 °C, with respect to 300–900 °C for air-blown combustion). The S index (which reflects ignition, combustion and burnout properties of a fuel) is significantly higher for biomass (mean values of 79.3 and 708.3 wt.%/(min2 ∗°C3) for combustion and oxy-combustion, respectively) than for coal (7.6 and 41.1 wt.%/(min2 ∗°C3)), indicating that wood chips present better combustion performance and enhanced combustion properties, especially in oxy-combustion conditions. The application of FWO and KAS methods to perform the kinetic study to the combustion and oxy-combustion processes shows that, under the same range of heating rate (10 ≤ β ≤ 50 °C/min) and for the overall range of conversion (0.1 ≤ α ≤ 0.9), both the models comply with the experimental data in combustion regime for all the considered fuels; on the other hand, with the increase of the oxygen concentration (oxy-combustion regime), both the models result reliable for the coal samples and more sensitive to the weight loss for the biomass samples. Acknowledgements This work has been carried out within the “Centre of Excellence on Clean Energy” research project (D82I13000250001) led by Sotacarbo and funded by the Regional Government of Sardinia.

Fig. 8. Activation energy behaviour in combustion (a) and oxy-combustion (b) modes by KAS method.

an oxy-combustion atmosphere. The oxy-combustion trend seems not to be subject to significant differences if compared to the combustion process, except for the steeper trend between 0.1 and 0.3. Both the biomass samples show a maximum in terms of activation energy around α = 0.35. It is evident that the maximum values of activation energy for both pine and eucalyptus samples are strongly dependent on oxygen concentration and Ea values have doubled passing from combustion to oxy-combustion conditions. This can be reasonably clarified with the increase of oxygen concentration. According to Chen et al. (2011) [63] and Fang et al. (2006) [64], the increase in oxygen concentration involves an increase of heat released by semi-coke during the oxidation process and, as a consequence, an increase of the surface temperature of semi-coke itself. It can be observed that the correlation coefficient (R2) of the straight lines in combustion regime presents high values (0.90 < R2 < 0.99) for both the FWO and KAS models; this means that all the points are well fitted and the methods provide reliable values of activation energy. However, for the oxy-combustion process, activation energy presents a linear correlation with good R2 values only in a narrow range of conversion (between α = 0.1 and 0.4). The assumption for this finding can be ascribed to the violent exothermic reaction due to high oxygen concentration, where, for α > 0.4, it is not possible to appreciate the linear variation of the weight loss as a function of the heating rate. Based on the findings of the kinetic study, it should be concluded that both methods (FWO and KAS) match the experimental data under all the conversion ranges in combustion regime, but they are not so effective, especially for biomass samples, when a high oxygen concentration is taken into consideration.

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