Model-free and model-based kinetics of the combustion process of low rank coals with high ash contents using TGA-DTG-DTA-MS and FTIR techniques

Thermochimica Acta 679 (2019) 178337

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Model-free and model-based kinetics of the combustion process of low rank coals with high ash contents using TGA-DTG-DTA-MS and FTIR techniques

T

Bojan Jankovića, , Nebojša Manićb, Ivana Radovićc, Marija Jankovićd, Milica Rajačićd ⁎

a

University of Belgrade, Institute of Nuclear Sciences “Vinča”, Department of Physical Chemistry, Mike Petrovića Alasa 12-14, P.O. Box 522, 11001 Belgrade, Serbia University of Belgrade, Faculty of Mechanical Engineering, Fuel and Combustion Laboratory, Kraljice Marije 16, P.O. Box 35, 11120 Belgrade, Serbia c University of Belgrade, Institute of Nuclear Sciences “Vinča”, Department of Materials, Mike Petrovića Alasa 12-14, P.O. Box 522, 11001 Belgrade, Serbia d University of Belgrade, Institute of Nuclear Sciences “Vinča”, Radiation and Environmental Protection Department, Mike Petrovića Alasa 12-14, P.O. Box 522, 11001 Belgrade, Serbia b

ARTICLE INFO

ABSTRACT

Keywords: Low-ranking coals Air combustion CO2 emission coefficient Mineral matter Model-based analysis

Thermal and kinetics behaviors of the low-rank coals from different annual periods (Kolubara (2015)/(2018) and TENT A (2015)/(2018)) during combustion process in air atmosphere, using simultaneous TGA-DTG-DTAMS measurements were investigated. The FTIR spectroscopy was used to gain additional information on coals structures. Kolubara and TENT A coals from (2015)/(2018) annual periods show differences in reactivity, where the reason for this demeanor lies in differences in decomposition kinetics of these coals. The conclusions made on the basis of model-based analysis clearly indicate that differences in combustion reaction pathways (especially in transitions from primary to secondary combustion stages) arise from continual changes in physical structure of the coals. It was found that the mineral matter significantly influences on the reactivity of coal during combustion, where this is particularly pronounced for TENT A coal particles.

1. Introduction Coal, as an energy source, marked the economic and political development of Europe in the 19th and 20th centuries. At the beginning of the 21st century, coal energy accounts for 36 % of the total energy produced in Europe and are individually the most important energy source [1]. The Republic of Serbia is one of the European countries where coal is the predominant energy source. The largest coal consumers in the Republic of Serbia are coal-fired power plants in which 96 % of the total produced coal is burned annually, while the remaining part is sold, such as dried and classified coal, in industry and consumer goods [2]. Nearly 70% of available coal reserves can be dug off by superficial high-productivity machinery with continuous operation. All these data indicate that coal is the basic energy raw material in the Republic of Serbia. Direct combustion of coal takes place in coal-fired power plants in order to produce electricity. This type of coal-fired process has a large environmental impact. Impurities in the coal, such as sulphur and nitrogen are released into the atmosphere, causing problems such as acid rain and smog. However, the biggest concern is the emission of carbon dioxide (CO2). Because of great concerns over the levels of CO2 in the atmosphere, technologies have been developed for its capture and storage [3,4]. The CO2 emissions can be reduced by

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increasing efficiency of coal conversion processes. Equipment needs to be designed so that coal conversion reactions can occur at optimum conditions. It is therefore important to understand how coal behaves in these processes. This can be achieved by developing and testing kinetic models for coal conversion reactions. Accurate models for devolatilisation and combustion are needed for assessing improving design of coal gasification and combustion equipments. During conversion processes of coal, such as devolatilisation and combustion, coal decomposes to give off organic vapors as it is heated [5]. An important tool used for understanding of these conversion reactions is thermogravimetric analysis (TGA), since these reactions involve mass change of the sample. The mass change versus temperature profiles of the coal as it is subjected to different heating regimes can be used to determine kinetic parameters of coal conversion reactions. These parameters are important because provide idea of optimum conditions needed for reactions [6] and are necessary in design of any type of coal decomposition equipment [7]. When coal is heated in oxidizing atmosphere, it loses moisture and volatiles. Homogeneous combustion of volatile matter occurs, heterogeneous combustion of char occurs and mineral matter is oxidized to ash [8]. Devolitilisation is usually complete before bulk of char is combusted, but two processes are not completely separated. Previously,

Corresponding author. E-mail address: [email protected] (B. Janković).

https://doi.org/10.1016/j.tca.2019.178337 Received 8 June 2019; Received in revised form 13 July 2019; Accepted 15 July 2019 Available online 20 July 2019 0040-6031/ © 2019 Elsevier B.V. All rights reserved.

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it was announced [9] that the coal rank plays a crucial role in occurrence of char and volatile reactions and that coal rank, ash properties, water content, and behavior of nitrogen during oxidation of coals exert a significant influence of both, NOx emission and combustion in atmospheric conditions. However, beside TGA, another significant thermal analysis technique represents derivative thermogravimetry (DTG) which allows determination of relative reactivity of coals. Chemical differences amongst coals affect their combustion mainly through their behavior during devolatilisation and extent of devolatilisation affects char reactivity further. The maximum rate of devolatilisation reaction can be used as an indicator of the reactivity. Coal rank represents most important factor influencing the reactivity of coal [10]. Since structure of coal varies greatly, it is clear that a kinetic model based on structure of coal would be limited in its applicability. Modelfree methods for calculations of kinetic parameters may be useful for design purposes. These methods are usually based on thermal analysis data at a constant heating rate and rely on a stage at which all reactions are considered equal. This stage is usually defined as stage at which a fixed fraction of initial mass of solid sample has reacted [11]. Methods are therefore referred to as isoconversional methods [12,13]. Isoconversional methods are undoubtedly quickest way to derive kinetic parameters for complex reaction profiles involving multiple processes. These methods can not determine exact kinetic model of individual reactions in complex stages of combustion process. So, model-based analysis represents powerful cutting-edge mathematical calculations to determine best kinetic model of particular reaction. One of advantages over isoconversional methods represents determination of reaction rate of each reaction step within process stage, as well as all kinetic parameters (including activation energy Ea, pre-exponential factor A as well as order of reaction n) and reaction type during process progression. Reaction models can be flexible in that sense which allow designs by adding new reactions as independent, consecutive or competitive steps to any place in model. The next advantage of this approach represents ability that simulated reaction step can be visually moved to corresponding step on experimental thermo-analytical (TA) curve and then parameters of this step can be optimized. Main goal of this study is kinetic investigation of combustion process (in an air environment) of coals supplied from “Kolubara” coal basin in the Republic of Serbia. Two different coal-fired power plants “NIKOLA TESLA A” (designated by TENT A) and “KOLUBARA” (designated by Kolubara) uses coals from this basin. This study contains two assaying approaches. The first represents solid fuel characterization by thermal analysis tests, such as simultaneous thermal analysis (STA), including thermogravimteric analysis (TGA) – derivative thermogravimetry (DTG) – differential thermal analysis (DTA) system. This system is coupled with mass spectrometry (MS) analyzer, for gas evolving analysis monitored during combustion process. Tested coals originate from two different aging periods (coals sampled in 2015. and 2018 year, in these coal-fired power plants). Coals classification was performed by ultimate and proximate analyses, as well as by spectroscopy analysis using FTIR (Fourier transform infrared spectroscopy) for evolution of coal structures. Based on analysis methods described in first part, the coal net efficiency was estimated, using high operational temperature and oxidation onset temperature (OOT) as experimental parameters. Values of CO2 emission coefficient from given coal samples were also determined. The second approach represents detailed kinetic analysis of combustion process in an air atmosphere. For estimation of kinetic parameters as well as reaction mechanisms, model-free and model-based analyses were applied, using state of art computational software (Kinetics NEO Version 2.1.2). Performed analyses provided a much clearer picture of mechanism and characteristics of combustion process of coals in their utilization, especially in coal-fired power plants, as well as in future designing of industrial coal-fired boiler furnaces. Obtained results were compared and analyzed in light of usability for combustion process optimization, as well as real-time fuel performances. According

to our knowledge, such inquiries were not conducted for purpose of versatile description of combustion process of studied coal samples. A special emphasis in the work is related to selected coal samples in the view of the fact that motive for this research is to check the combustion performances of coals originated from Kolubara basins, which suffered the great flood in 2014, within “2014 Southeast Europe floods”. Therefore, having this event in mind, the coals have probably made significant changes in their composition and are characterized by high ash content. Hence, the important goal was to check the characteristics of the coals combustion process and to explain the possible differences in their kinetics, bearing in mind their geological origin and the area of exploitation after the catastrophic floods in a given region. The obtained results were correlated to chemical composition of tested coals, regarding to elevated high ash contents influencing on their quality. 2. Experimental 2.1. Sampling and fuel characterization procedure Research presented in this paper was based on experimental tests performed on four coal samples with high ash contents [14]. All tested coals were originated from Kolubara basins sampled in a different year (labelled 2015 and 2018) and from different sample points (directly from coal mine - labelled as ‘Kolubara’ and from coal-fired power plant storage yard – labelled as ‘TENT A’). Coals (Kolubara (2015), (2018), and TENT A (2015), (2018)) were ground in a mill, air-dried at room temperature, and then sieved with standard 200 Mesh to the particle sizes of rp ˜ 75 μm. Before experiments, coal samples were dried in oven over night at T =105 °C. Collection and sample preparation procedures for tested coal samples, as well as ultimate and proximate analysis, were done according to ASTM standards [15–17]. In proceeding analysis, the rank classification of coals was also included [18]. 2.2. FTIR spectroscopy FTIR spectra of coal samples were collected using a PerkinElmer Spectrum two FTIR spectrometer (PerkinElmer, Inc., Waltham, Massachusetts, USA) in transmission mode (TM). Measurements were perfomed on dry samples where samples being prepared using the pressed KBr pellets (1:100) technique. A spectrum of KBr in compartment was used for background correction to remove interfering peaks due to atmospheric water and carbon dioxide. Spectra were recorded in the range from 4000 to 400 cm-1 with the resolution mode of 4 cm-1, in order to obtain the quality spectrum lines. 2.3. Simultaneous thermal analysis (STA) and mass spectrometry (MS) analysis Simultaneous thermal analysis (STA) (TGA-DTG-DTA) and the mass spectrometry (MS) analysis of considered samples were done by the Simultaneous Thermal Analyzer Netzsch STA 449 Jupiter F5 system coupled with Netzsch QMS 403D Aeolos quadrupole mass spectrometer. The STA tests were performed under the following conditions:

• sample mass: 10 ± 0.5 mg (sample mass was kept at 10 mg to avoid • • • •

the resistance of probable effect of mass and heat transfer during process of coal decomposition). temperature range was from room temperature up to 950 °C. the used heating rates: β = 10, 20 and 30 °C min-1. the carrier gas was synthetic air (21 % vol O2 + 79 % vol N2). the total carrier gas flow rate was ψ =70 mL min-1.

STA scanning was repeated twice, where consistent results with excellent reproducibility were obtained. 2

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The mass spectrometry analysis was done in order to identify evolved gasses during thermal decomposition of coal samples in air atmosphere, namely gaseous combustion products with amu 18 and 44. According to the analysis of the MS results in correlation with basic fragment analysis, the considered amu which corresponds to H2O and CO2 evolutions respectively, could gives the comprehensive data about combustion behavior of studied coals through optimum coupling between STA and MS.

methods, the measurements are analyzed for multiple conversion levels. Besides conventional isoconversional approach, the modified Friedman method was also applied, as numerical optimization which correlates estimated activation energy and pre-exponential factor values, for best fit at every considered heating rate to get a minimum of objective function Ψ, by following expression:

=

(

calc i

exp 2 ) , i

(1)

curves points

3. Theoretical background

αicalc

αiexp

where and represent calculated and experimental conversion values, considered for every used heating rate. In all model-free methods, the activation energy is determined using points at the same conversion (α = 0.05, 0.06,…,0.95, for estimation of Ea values, with conversion step of Δα = 0.01) from measurements at different heating rates. The FR and KAS were expressed as Ea against conversion, and logarithm of pre-exponential factor (A) against conversion (log A – α dependency). Related to optimization procedure, results were expressed as conversion fits (α – T curves). Some difference which may arises between kinetic parameters among FR and KAS methods lies in calculation formula and in results generally.

3.1. Model-free (isoconversional) analysis Model-free methods are based on evaluating the Arrhenius (kinetic) parameters without relying upon reaction mechanism function [19]. Model-free method is also called isoconversional, as reaction rate is a function of temperature at constant conversion [20]. Isoconversional methods are valid to analyze both, isothermal and non-isothermal thermo-analytical (TA) measurements. 3.1.1. ASTM E2890 The ASTM E2890 procedure [21] shared a different perspective which lies between the model-based and model-free methods. Unlike other isoconversional models, there is no need to estimate activation energy (Ea) for each conversion value. It takes a model-free approach for estimation of activation energy which is evaluated from Kissinger’s plot of log(β/T m2) versus (1/T m), where T m is temperature point of the maximum combustion rate. Pre-exponential factor can be calculated on assumption of first-order reaction as follows A = (β·Ea)/(R·T m2)·exp(Ea/ RT m) [22]. Determination of Ea using peak maxima points of rate curves can be performed without assumption of specific reaction type. From obtained straight line log(β/T m2) = f[(1/T m)], the Ea can be calculated from the slope. Method can be applied only for one-step reactions, while for complex reactions, the points are not placed strictly on the line. Only one pair of kinetic parameters may be obtained, while all other information is lost. From DTG curves, the two characteristic parameters can be obtained: the temperature of maximum rate of mass loss (T m) and the oxidation onset temperature (Ti), which represents the parameters that depend on sample properties.

3.2. Model-based analysis Model-based kinetic analysis is based on three govern assumptions: 1) The process consists of several elementary reaction steps, and reaction rate of each step can be described by a kinetic equation of its own, depending on concentration of initial reactant ej, the concentration of the product pj, pre-exponential factor Aj and activation energy Eaj, specific only for this step with number j, as follows [29]:

Re action Ratej = Aj f j (ej , pj ) exp

Eaj RT

(2)

so, the each considered step has its own reaction type described by the function fj(ej,pj). Number of kinetic equations is equal to number of reaction steps; concentration for each reactant increases for reaction steps where this reactant is a product, and decreases for reaction steps where this reactant is a starting substance. All used functions are incorporated into Kinetics NEO software with listing of all reaction types and their kinetics codes. Many of them can be seen in literature [30,31]. Each reaction step is mathematically included through function fj(ej,pj); 2) All kinetic parameters including activation energy, pre-exponential factor, order of reaction, and reaction type are assumed to be constant during reaction progress for every individual reaction step, and 3) Total thermo-analytical (TA) signal is the sum of the signals of individual reaction steps.

3.1.2. Dynamic Arrhenius method Dynamic Arrhenius method or integral Ozawa method is used only for failure temperature values. For each dynamic measurement, the failure temperature value is important. Namely, this is the temperature at which investigated fuel has defined changes during its heating. Typical example of these data is oxidation onset temperature (Ti), and this quantity represents the temperature for fuel heated in an air atmosphere. For each dynamic measurement, the pair of parameter values is taken for analysis: the heating rate and the failure temperature. Analysis shows the graph of log(β) = f(1/Ti) as the straight line, where from the slope and intercept of this line, the Ea and A can be calculated, respectively. The important advantage of this method represents the fact that reaction must not be measured completely. Disadvantage represents that actual method can be applied only for one-step reactions while for complex reactions the points are not placed strictly on indicated line, similar to ASTM E2890. Calculation was done in accordance with ASTM E1641 [23]. Results of this analysis are used as failure temperature for ASTM E1877 [24].

4. Results and discussion 4.1. Proximate and ultimate analysis Table 1 shows the proximate and ultimate analysis of all tested coals (TENT A (2015)/(2018) and Kolubara (2015)/(2018)). Proximate and ultimate analysis reflects properties of coal from different aspects and both can serve as reference on the features of coal quality. From results presented in Table 1, it can be observed that studied coals show some differences in their characteristics. Namely, TENT A (2018) and Kolubara (2018) coals show higher moisture content (nearly 10 %) than other two coals. The higher moisture in coals may causes spontaneous combustion [32], during transportation and storage of the coals. In addition, the moisture also plays an important role in combustion process. Therefore, higher moisture present in coals decreases the furnace temperature and eventually can reduce the boiler efficiency. So, the low moisture coals (such as coals from both coal-fired power plants in 2015 year (Table 1)) can be considered as a much more suitable for combustion process. Coals from 2018 are characterized by

3.1.3. Differential Friedman (FR) and integral Kissinger-Akahira-Sunose (KAS) isoconversional methods For model-free approach, the change of kinetic mechanism is described by the continuous changing of activation energy and pre-exponential factor with the progress of reaction. Friedman (FR) analysis [25] is differential isoconversional method, whereas Kissinger-AkahiraSunose (KAS) [26–28] analysis is integral isoconversional method. In all 3

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applied heating modes. At low heating rates the volatiles are released at a slow rate escaping through existing pores or cracks in coals, avoiding abrupt fragmentation and ruptures that occur at high heating rates what can be found in blast furnaces. All coals are characterized by elevated content of the ash, but the ash content is higher for coals in 2015, than those in 2018 (Table 1). This is followed by the correct percentage distribution of mineral matters (Table 1). Mineral matter can results in appearances of oxides of silicon and metals, such as, aluminium, iron and calcium. These oxides are essential part of the ash. In addition, by losing in weight, amount of ash of the coal remains even smaller than its mineral matter content (Table 1). Tested coals can be selected in increasing content of the ash (from smallest to the largest content) as follows: Kolubara (2018), TENT A (2018), Kolubara (2015), and TENT A (2015), respectively. Ash content may have an important role in catalyzing the oxygen exchange reactions, particularly for coal char gasification by CO2 [33,34]. Not the all fuels contribute to the same carbon dioxide (CO2) emissions. Due to their different chemical compositions, the emissions are also different, resulting from the combustion of different fuels, for same thermal effect. To differentiate between the various fuels, the carbon dioxide (CO2) emission coefficient is necessary to introduce, and its represents the mass of the emitted CO2 in an atmosphere reduced to an energy units. The CO2 emission coefficient (KCO2) can be expressed in a form such as KCO2 = 3.67·[(FC)/HHV], where 3.67 is the stoichiometric coefficient (from C to CO2), while the HHV (MJ (kg)-1) is gross calorific value [35]. The CO2 emissions from the fuels primarily depend on their carbon (C) content (Table 1) and their hydrogen-carbon ratio. Over the years, the trend of the fossil fuel usage tends towards the higher hydrogen by carbon (H/C) ratio. The higher the H/C ratio (Table 1), the higher is the energy efficiency of the fuel and is lower the CO2 emissions from its combustion. Fig. 1 shows graphical bar presentation of CO2 emission coefficient (KCO2) values (in kg per MJ) for all investigated coals. TENT A (2015) has the lowest value of KCO2. The amount of CO2 produced when a fuel is burned is a function of the carbon content (C) of the fuel with a reduced HHV value (Table 1). However, the oxygen content may influence on the heating value, and its contribution can be important depending on how the oxygen in the coal is bound to the carbon and has, therefore, already partially oxidized the carbon, decreasing its ability to generate heat. The more oxygen a coal contains, the easier it is to start to burn it, or to achieve its ignition. This depends on the structural composition of a given coal and its ranking. Consequently, variations in the ratios of carbon to heat content of the coal are due primarily to variations in the hydrogen content (Table 1). By the long-recognized finding is that the anthracite emits

Table 1 Proximate and ultimate analysis of investigated coals. TENT A (2015) coal Proximate analysis (wt%) Moisture Volatile matter (VM) Fixed carbon (FC) Ash Fuel ratioc HHV (MJ (kg)-1) LHV (MJ (kg)-1) Mineral matter (MM)d

6.46 14.05 6.87 72.62 0.49 4.823 4.283 78.64

Ultimate analysisa (wt%) C 13.54 H 1.68 Ob 11.54 N 0.24 S 0.38 H/C 1.48 O/C 0.64

TENT A (2018) coal Ultimate analysisa (wt%)

Proximate analysis (wt%) Moisture Volatile matter (VM) Fixed carbon (FC) Ash Fuel ratioc HHV (MJ (kg)-1) LHV (MJ (kg)-1) Mineral matter (MM)d

9.33 22.97 10.53 57.17 0.46 7.146 6.321 62.07

C H Ob N S H/C O/C

20.44 2.63 18.79 0.37 0.60 1.53 0.69

Kolubara (2015) coal Ultimate analysisa (wt%)

Proximate analysis (wt%) Moisture Volatile matter (VM) Fixed carbon (FC) Ash Fuel ratioc HHV (MJ (kg)-1) LHV (MJ (kg)-1) Mineral matter (MM)d

6.45 18.69 9.65 65.21 0.52 5.372 4.730 70.70

C H Ob N S H/C O/C

16.29 2.14 15.57 0.30 0.49 1.57 0.72

Kolubara (2018) coal Ultimate analysisa (wt%)

Proximate analysis (wt%) Moisture Volatile matter (VM) Fixed carbon (FC) Ash Fuel ratioc HHV (MJ (kg)-1) LHV (MJ (kg)-1) Mineral matter (MM)d

9.58 24.46 9.93 56.03 0.41 6.565 5.718 60.85

C H Ob N S H/C O/C

19.17 2.70 21.11 0.38 0.61 1.68 0.83

a

On a dry basis. By the difference (O(%) = 100 % - (C + H + S + N + Ash)). c The ratio of FC to VM (FR = FC/VM). d The MM content was calculated by the Parr’s formula: MM = (1.08·Ash) + (0.55·S). b

higher volatile matter (VM) in comparison with ones from 2015 (Table 1). Volatile matter (VM) of coal has an important influence on ignition. The coal with high VM tends to burn more quickly with a larger flame, and coal self-ignites is much quicker. On the other hand, the coal with low VM tends to burn more slowly with the lower flame, but at higher temperatures. So, for TENT A (2018) and Kolubara (2018) coals, which have higher VM contents, we may expect that are selfcombust faster. Also, it can be noted, since the volatiles can contain hydrocarbons, then for coals which have higher VM content, the HHV value may declines as time goes by. This issue is specially a risk for lower calorific coals, such as sub-bituminous coals or even younger coals. Coals with higher VM (as TENT A (2018) and Kolubara (2018) (Table 1)) tend to have lower ash fusion temperatures, and therefore require lower furnace temperatures. Conditions under which VM is driven off affect the nature of fixed carbon (FC) or char that remains. Also these properties depend on volatiles releasing during low and high

Fig. 1. The CO2 emission coefficient (KCO2) values of tested coals. 4

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Fig. 2. H/C versus O/C atomic ratios for tested coals with designation of coalification paths. Fig. 3. The FTIR spectra of investigated coal samples.

the largest amount of CO2, followed by lignite, sub-bituminous coal, and bituminous coal [36]. Fig. 2 shows H/C versus O/C atomic ratios for tested coals, where full arrow colored lines represent coalification paths for liptinite (and cutinite), huminite-vitrinite and inertinite in accordance with Bustin et al. [37]. It can be observed that all coals belong to huminite coal type. Kolubara coals (2015/2018) have the highest H/C and O/C ratios (Fig. 2) and these coals are positioned at graph location which belongs to xylite (aluminosilicate of Ca, Mg and Fe) (Kolubara (2018)) and xylite-rich ((Kolubara (2015)) ortho-lignites (low-rank C) [38]. Lowest O/C ratio has TENT A (2015) coal which was followed by TENT A (2018) coal, and in considered cases the metamorphic degree increases, so, these coals are more inclined towards sub-bituminous coals (lowrank A) [39]. In that case, the total specific surface area of the coal decreases (with coal metamorphic degree). The difference in pore diameter distributions between Kolubara and TENT A coals is obviously reflected in the values of KCO2, which were obtained (Fig. 1). Probably going from Kolubara to TENT A coals, the pore volume is decreasing, and thereby affecting the magnitude of the CO2 emission. In addition, the FC and VM provide an insight into the reactivity of coal. The ratio of fixed carbon to volatile matter (fuel ratio) indicates the ease of ignition and burnout, but the heat content of the volatile matter is a more reliable guide to ignition. The volatile matter content influences NOx formation [40]. Careful control of air/fuel ratio and staging of air combustion results in conversion of most of N volatile to N2 rather than NO. The VM content or FR is likely to be a reasonable indicator of volatile N release. It follows that coals with high VM or low FR (Kolubara coals, Table 1) are likely to respond favorably to NOx controls. FR is another indicator of volatile release, and can be used for coal reject limit. For example, in Japan, typical Japanese electric power company rejects coal with FR > 2.50 [41].

bonded –OH groups from phenol, alcohol, carboxylic acids and moisture is centered about 3360 cm-1 in all coal samples. Absence or low content of aliphatic –CH2 groups is a good indicator for the maturation of coals. Minor peaks were present in all samples, with sharp bands at 2923 (asymmetric stretch) and 2847 cm-1 (symmetric stretch) [44]. In every coal sample formation of aromatic structures on the account of aliphatic was evident. b) 2000-1200 cm-1 — presence of complex aromatic structures: All spectra showed –C = O conjugated with in-plane bending of aromatic –C–H and the aromatic ring –C = C stretch around 1600 cm-1, with slight shift to 1619 cm-1 for peak from TENT A (2018) [45–47]. The shift is in accordance with xylite free coals. Carbonyl stretch was observed at 1508 cm-1, but with much lower intensity for TENT A (2018), which further indicates maturation of this coal sample [45]. Low intensity aryl ether –C–O band appeared at 1265 cm-1 in all the spectra, which is in accordance with the expectations for lignite and more mature coals [45]. c) 1200-400 cm-1 — presence of different minerals: Absence of strong bands around 1430 and 875 cm-1 confirmed that coals did not contain calcite, but mostly silicate minerals [48]. Strong bands in the region from 1100 to 900 cm-1 in all spectra could be associated with –C–O stretching coupled with –Si–O from accompanying minerals [44]. However, the difference lie in the shape and the peak position between the TENT A (2018) coal and the rest of the coals. TENT A (2018) showed weak broad band at 1023 cm-1, followed by a strong absorbance at 998 cm-1, while all the other coals had two strong bands with equal intensity, shifted to 1030 and 1007 cm-1. Decrease in band at 1023 cm-1 and shift towards lower frequency in the spectra of TENT A (2018) coal indicated the stronger presence of kaolinite in the sample. This assumption was further encouraged with appearance of the highest intensity peak at 912 cm-1, coming from Al2OH bending in kaolinite [49]. The two neighboring –C–H bonds in aromatics were observed at 830 cm-1 in TENT A (2018) coal, while bands around 795 and 779 cm-1 belong to three and four adjacent –C–H groups, coupled with silica [50–52]. Mono-substituted aromatics and Si–O–Si appeared at 690 cm1 , with highest intensity in the spectrum of TENT A (2018) coal. The Si–O–Al from kaolinite was identified at 525 cm-1 on all spectra’s as well as Si–O–Si at 465 cm-1 [49]. From 500 to 400 cm-1 there are several bands, mostly originating from kaolinite and other silicate minerals, except for a band at 420 cm-1, which identified presence of pyrite in TENT A (2018) and Kolubara (2018) coals [53,54]. All of the coal samples contained high amount of impurities, mostly from silicate minerals, which hindered their true heating value.

4.2. FTIR results As coal matures through metamorphosis, it loses –H and –OH groups, while content of un-saturated carbon bonds and aromatic structures grows [42]. FTIR spectroscopy can help track these changes through disappearance and formation of bands relating to these bonds; therefore, we have split analysis into three regions (Fig. 3). a) 3800-2700 cm-1 — presence of –OH and aliphatic –CH2 groups: All IR spectra of our coal samples showed sharp minor peaks at 3696 and 3620 cm-1 that are associated with free –OH groups, which indicate the presence of clay minerals [43]. TENT A (2018) had the highest amount of inorganic crystals with inherent water molecules. Hydrogen 5

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Fig. 4. Graphical illustration of combustion process in an air of tested coals with higher ash content, by thermal analysis in laboratory conditions (coal particles burner region – PART A), together with char burn-out region occurring in the furnace chamber (PART B).

4.3. TGA-DTG-DTA study

decomposition reactions by weakening and breaking of the chemical links in coal samples, are subsequently burn, releasing a significant amount of heat through highly pronounced exothermic effects (DTA curves in Figs. 5 and 6). Since the vast majority of the diluent in an air is nitrogen, so, in our case, for every mole of the oxygen required for combustion, approximately about 3.78 mol of the nitrogen must be introduced. However, the nitrogen may not significantly alter the oxygen balance it does have a major impact on chemical kinetics and formation of pollutants in coal combustion systems. Among Kolubara coals (Fig. 5), based on the positions of Tm and DTGmax values, Kolubara (2018) coal has lower Tm values at all heating rates in comparison with Kolubara (2015) coal, and its DTGmax values are higher (considered at every heating rate) in relation to those in the case of Kolubara (2015) coal (visually even can be noticed in Fig. 5 (DTG)). So, Kolubara (2018) coal reveals better combustion reactivity in comparison with Kolubara (2015) coal, which is evident from lower peak temperature, Tm, as well as Ti values. However, considered TENT A coals, we have a slightly different situation. If we consider the position of Tm temperature, at the lowest heating rate (10 °C min-1), TENT A (2018) coal has lower Tm value, than the same for TENT A (2015) coal, but at higher heating rates, the Tm values exceed those in the case of TENT A (2015). However, the mass loss rates still increase with an increasing of the heating rate, but the shape of DTG curves are more disrupted than ones in Kolubara (2015) coal (compare DTG curves in Fig. 6). The reason for this may be in the existence of some differences in decomposition kinetics of these two coals, especially in relation to Kolubara coals. Factors that can affect the kinetic behavior of tested coals (Kolubara and TENT A) represent dissimilar contents of volatile matter (VM), the content of mineral matter (MM) as well as the carbon content. Also, higher the heating rate to coal particles, the higher the temperature of the maximum mass loss rate. The use of higher heating rates results in a more extensive thermal fragmentation of coals molecule structure and suppresses secondary reactions and the loss of fixed carbon (FC). Table 2 lists the values of the oxidation onset temperature (Ti) and temperature of maximum rate of mass loss (Tm), for the combustion processes of all tested coals. In the later stage of studied process, which take places above 500/ 550 °C, the char combustion reaction (with a smaller exothermic effect) occurs, where over 700 °C (Figs. 5 and 6), it shows the trend of deceleration. Considering Kolubara coals, the exothermic effects for this stage are almost the same, where DTG (third peak) features have unchanged shapes at all heating rates. For TENT A coals, we can see that for TENT A (2015), DTG peak related to char oxidation is more pronounced and “grows” with an increasing the heating rate, where exothermic peaks (at all heating rates) are expressed and clearly visible (Fig. 6 b and c).

In this part of paper we presented the results and discussion of thermal analysis experiments that are capable of describing and identifying transformations related to burner region of coal particles heated up to temperature of 950 °C, where devolatilisation reactions are dominant. In Fig. 4, this is presented in the area marked with “PART A”, where the char formation is being realized. Knowledge of coal devolatilisation was of great importance because it exerts a marked effect on overall combustion process behavior. Coals are devolatilised using thermal analysis methods at various heating rates and products of this process were detected by mass spectrometer. Recent advances in understanding of coal structure have led to more fundamental approaches for devolatilisation modeling, and new step in modeling was proposed here. From presented outfits, the heat content of volatile matter (VM) is also dependent on the rank, type and mineral matter (MM) content of the tested coals. Fig. 5 shows TG-DTG-DTA curves of combustion process in an air atmosphere at different heating rates (β = 10, 20 and 30 °C min-1) for Kolubara (2015) (a-c) and (2018) (d-f) coals. Fig. 6 shows TG-DTG-DTA curves of combustion process in an air atmosphere at different heating rates (β = 10, 20 and 30 °C min-1) for TENT A (2015) (a-c) and (2018) (d-f) coals. Considering coals from both annual periods (2015 and 2018; Figs. 5 and 6), the first stage represents heating and removal of moisture (25 – 200 °C), and which was characterized by the sharp acceleration of the mass loss in TGA curves, marked with a clear DTG peak, and followed by endothermic heat effects (the minor endotherm which precedes the main combustion exotherm). Through heating procedure, this stage was characterized by the absence of chemical reactions, where the process of adsorbed and chemically bonded moisture evaporation from the particles surface occurs. The intensity and broadness of DTG peaks for coals in 2018 (Figs. 5 and 6), are much more pronounced, since that both Kolubara (2018) and TENT A (2018) coals contain higher moisture. DTG peak profiles for 2018 coals are varied more strongly with increasing of heating rate expressed asymmetrical behavior, which indicate that difference of water vapor concentration may exists, as a driving force of process. This is much more uniformed for coals from 2015. The second stage which represents pyrolysis — devolatilisation, appeared at higher temperatures ΔT ˜ 200 – 550 °C (Figs. 5 and 6). The most striking and most prominent DTG peak for all observed cases belongs to devolatilisation reactions, where volatile matter (VM) escapes from the coal samples (Figs. 5 and 6). However, in this zone, liberated gases and light chemical compounds as products of 6

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Fig. 5. TG (mass loss: 100 % – TG signal) (%), DTG (% min-1) and DTA (μV mg-1) curves of air-combustion process at different heating rates (β = 10, 20 and 30 °C min-1) for Kolubara (2015) (a, b, c) and Kolubara (2018) (d, e, f) coals.

However, in the case of TENT A (2018), the intensity of char oxidation strongly depends on the used heating rate, and this is detected through the appearance of “shoulder” in exothermic features presented by DTA curves (Fig. 6 e and f). The reason for this behavior may be in chemical kinetics limiting factors in considered temperature regions. This can lie in some of differences that exist in Arrhenius parameters creating differences in reactivity between oxidizer and solid char. The two heterogeneous reactions and one homogeneous reaction govern the

products of the char combustion, where CO2 and CO gases are released, and these are the following reactions: 1) C + O2 → CO2 (exothermic), 2) C + ½ O2 → CO (exothermic), and 3) CO + ½ O2 → CO2 (exothermic) (this reaction takes place in the boundary layer). 4.4. The MS spectra analysis Fig. 7 a – b shows the simultaneous TG-MS gases evolved analysis 7

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Fig. 6. TG (mass loss: 100 % – TG signal) (%), DTG (% min-1) and DTA (μV mg-1) curves of air-combustion process at different heating rates (β = 10, 20 and 30 °C min-1) for TENT A (2015) (a, b, c) and TENT A (2018) (d, e, f) coals.

during combustion process in an air atmosphere for Kolubara (2015) and Kolubara (2018) coals at the heating rate of β =10 °C min-1. As we can see from Fig. 7, for both coals, the main evolution profile belongs to CO2 which represents the major contributor in air combustion process. The intensity of water evaporation (in the temperature range of 25 – 200 °C) is much higher for Kolubara (2018) coal than for Kolubara (2015) coal, given the higher moisture content (Table 1). The

H2O and CO2 evolutions are observed to initiate at around 200 °C, and continuous up to the end of combustion, including devolatilisation and volatile combustion zones in much wider temperature range (ΔT ˜ 200 – 650 °C). The lower oxygen concentration in combustion environment results in the shift in CO2 formation to higher temperature zone, since the reduced oxygen level leads to slower burning and later release of CO2. It 8

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was found that for Kolubara (2015) coal, the CO2 releases accomplished the higher absorbance intensity, in comparison with ones presented in the case of Kolubara (2018) coal (Fig. 7). Considering both coals, this is also confirmed by overlapping behaviors of their amu values, related to gas compound and its fragment. If we consider H2O releasing profiles, in the first temperature zone (up to 200 °C), H2O formation is detected due to the moisture release in all cases (which was previously indicated). In the second part, further H2O release is observed as a consequence of coals oxidation reactions. Fig. 8 a – b shows the simultaneous TG-MS gases evolved analysis during combustion process in an air atmosphere for TENT A (2015) and TENT A (2018) coals at the heating rate of β =10 °C min-1. Comparing TG-MS profiles for actual coals, with ones shown in Fig. 7, the certain differences can be noticed. Excluding the moisture release below 200 °C, we have one main region which encompasses temperatures between 200 – 650 °C, which reveals the “shoulders” at H2O signal profiles (Fig. 8). This can be attributed to cracking decomposition reactions of the oxygen-containing functional groups, where H2O signal is much more pronounced for TENT A (2015) coal, followed by the appearance of two “shoulders” at around 400 and 490 °C, respectively (Fig. 8 a). The H2O is produced over a wide range of temperatures, where its release is observed probably from the condensation of phenols. However, the MS signal for CO2 may arises from cracking of carboxyl functional groups, breaking of aliphatic bonds, some weak

Table 2 The Ti and Tm values for air-combustion process of the investigated coals. β ( oC min-1)

Kolubara (2015)

Kolubara (2018) o

Oxidation onset temperature, Ti (oC)

Tm ( C)

10 20 30

305.70 315.50 325.20

375.00 400.00 425.00

β ( oC min-1)

TENT A (2015)

10 20 30

a,b

Oxidation onset temperature, Ti (oC)

Tm (oC)a,b

297.50 308.60 320.10

360.00 390.00 420.00

TENT A (2018)

Oxidation onset temperature, Ti (oC)

Tm (oC)a,b

Oxidation onset temperature, Ti (oC)

Tm (oC)a,b

306.00 312.00 318.20

385.00 410.00 430.00

301.70 305.30 309.00

370.00 420.00 460.00

a

From DTG curve. The maximum of thermo-oxidative decomposition of coals are shifted towards higher temperatures as the heating rate increased. This is caused by differences in heat transfer and kinetic rates, thereby delaying sample decomposition (because it takes the period of time to transmit heat quantity from surface to sample interior). High heating rate obviously shifts the reactions to a much higher temperature range. b

Fig. 7. Simultaneous TG-MS analysis of combustion process in an air atmosphere for Kolubara (2015) a) and Kolubara (2018) b) coals at 10 °C min-1; The MS graphs show major contributors of gaseous products in combustion process, involving H2O and CO2 and the fragments expressed in amu.

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Fig. 8. Simultaneous TG-MS analysis of combustion process in an air atmosphere for TENT A (2015) a) and TENT A (2018) b) coals at 10 °C min-1; The MS graphs show major contributors of gaseous products in combustion process, involving H2O and CO2 and the fragments expressed in amu.

bonds of aromatic, oxygen-bearing carboxyl functional groups, ether, as well as stable oxygen hetero-cycles. The MS signal related for CO2 at around 600 °C (Figs. 7 and 8) may appears from the decomposition of carbonate with iron ion which takes place at that temperature [55]. Based on these observations, the first objective is fulfilled since that hydrocarbon fuels are successfully converted into CO2 and H2O plus additional heats. The second objective leans on reducing a large volume of solid into a small amount of the solid ashes, plus, again, CO2 and H2O from combustion of the organic compounds. Unfortunately, coals which are combusted contain chemicals that are vaporized or converted chemically into gases that mix with the harmless gases such as CO2 or SO2 and NO. So, considering gas evolving data, and estimated values of carbon dioxide emission coefficients based on proximate analysis of studied coals (see above results), unfortunately, it is not possible to establish a qualitative correlation formula between the amounts of emitted CO2 identified by mass spectrometry with KCO2 magnitude.

kinetic parameters values exists. Considering results from dynamic Arrhenius method for all tested coals (Fig. S2.), Kolubara coals are characterized by lower values of logA and Ea at the beginning of oxidation (ignition) process stage, in comparison with kinetic parameters calculated for TENT A coals. If we consider all coals from different annual periods, Kolubara (2018) coal has the lowest values of kinetic parameters (Supplementary material; Fig. S2.). It can be assumed that higher Ea may implies higher sensitivity to temperature change, while lower Ea means that the rate constant will increase less as the temperature is increased. However, this should be taken with caution because of the appearance of “fallacy of activation energy concept”, since that a higher Ea can very well be (and often is) compensated by a higher pre-exponential factor. Taking into account the same annual periods, the ASTM E2890 approaches (Fig. S1.), shows that the 2015 coals have higher values of kinetic parameters than those for 2018 coals. However, considered reaction combustion mechanisms around temperature Tm, this method shows some variations of logA and Ea values for Kolubara and TENT A coals. Kolubara (2015) and TENT A (2015) coals have very similar logA and Ea values (Fig. S1.), compared with 2018 coals. However, the some differences (but not large) between the kinetic parameters of Kolubara (2018) and TENT A (2018) coals obviously exist. It can be concluded that the Kolubara and TENT A coals are different in their chemical structures, as the consequence of coal type distinctions. These suggest on the probable existence of concurrent

4.5. Kinetic analysis and air-combustion mechanisms ASTM E2890 and dynamic Arrhenius methods were applied for estimation the single pair of kinetic parameters for investigated combustion processes of the studied coals. The corresponding plots of log(β/ T m2) versus 1/T m and log(β) versus 1/Ti are given in the Supplementary material (see Fig. S1., and Fig. S2.). From the obtained results, we can observe that difference between 10

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Fig. 9. FR and KAS methods for evaluating the Ea – α (conversion) dependency for air-combustion processes of studied coals (Kolubara (2015)/(2018) and TENT A (2015)/(2018) coals). Graphs represent isoconversional dependencies in absence of the moisture evaporation stage (due to the fact that we focus on the main combustion process, the Ea values are not calculated for this stage).

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reaction combustion mechanisms between Kolubara and TENT A coals. Fig. 9 shows model-free (isoconversional) methods/models using Friedman’s (FR) and Kissinger-Akahira-Sunose (KAS) approaches for air-combustion processes of tested coals. All calculations were performed with exclusion of the moisture evaporation stage. From the observed dependencies, the greatest noticeable difference can be seen between Kolubara and TENT A coals, where this is the most pronounced in the case of TENT A (2015) coal air-combustion process. However, in all cases studied, the complicated, multi-component and the multi-step reaction kinetics during combustion in an air atmosphere were presented. For tested coals, no constant Ea regions is observed, where pronounced “death valley” (approximately at the medium conversion values) (Fig. 9) is identified for Kolubara (2015)/(2018) and TENT A (2018) coals, which gives challenges in the interpretation of the combustion mechanisms. Using the same isoconversional methods, the corresponding logA – α (conversion) dependencies were also estimated and the results are presented in Supplementary material (Fig. S3.). Considering both isoconversional approaches, the co-existence of the results is obvious. Both considered methods show the same type of dependency of the kinetic parameters from the conversion, if we analyzed the Kolubara and TENT A coals from the different annual periods (2015 and 2018) (see also Fig. S3.). The three reaction regions can be identified, such as reactions at lower conversions (approximately from α ˜ 0.05 – 0.25), at medium conversions (higher than α = 0.25 and lower than α = 0.65, within “death valley”), and at higher conversions (α > 0.65, where the Ea maintains an increases trend to the very end of the process). Decreasing trend of Ea value at lower α values (up to ˜ 25 % of α’s) corresponds to releasing the volatiles from coal samples, where after this region, it follows an entrance into “death valley” whose conversion segment and “depth” varies, depending on the type of coal as well as applied model-free method/model (Fig. 9). This may be corresponds to oxidation of carbonaceous parts, which its duration is not the same and depends on the structure of the tested coal. The final region (at higher conversions above ˜ 65 % of α’s) can be attributed to the char combustion. In general, from obtained isoconversional results, it is evident that kinetic parameters are dynamically changed, and combustion process of coals is complicated by several factors such as the multicomponent character of the coals (various light and heavy hydrocarbons components), the complex decomposition reaction paths, as well as multi-phase behavior of the coals. Namely, the last one is characterized by the fact that the reaction kinetic performance of a certain species may be different in different phases, such as gas phase and liquid phase. So, when accounting for phase behavior across a wide temperature range, from ambient to the peak combustion temperature, the thermodynamic equilibrium may changes intensively. Therefore, the effect of phase behavior on the combustion process is non-trivial. The last but not least factor represents the wide range of temperatures associated with heat conduction as well as heat released from exothermic reactions, which also can affect on the kinetic performances of certain reaction stages in a comprehensive combustion process of tested coals, expressed through the isoconversional principles shown in the Fig. 9 and Fig. S3 (Supplementary material). In this case, dynamic measurements are limited for this complicated condition. So, conventionally, the isothermal measurements are recommended. This technique allows keeping the temperature constant at the several values that artificially narrow the temperature range. Beside this technique, as a resulting solution, the ramped temperature oxidation (RTO) tests can also be implemented. This should be guidelines for our future research on these coal systems. Arithmetic mean activation energy values for oxidation reactions (mainly the central part in the plots Ea — α shown in Fig. 9) were 84.72 kJ mol-1 (TENT A (2015)), 79.20 kJ mol-1 (TENT A (2018)), 77.73 kJ mol-1 (Kolubara (2015)) and 65.23 kJ mol-1 (Kolubara

(2018)). The activation energies increase as the rank of the coal increased. It was announced that for a low rank sub-bituminous coal (rank A), the activation energy amounts Ea =76 kJ mol-1 [56], where the obtained values of Ea for TENT A (2015)/(2018) coals are in very good agreement with previous findings reported in literature. Also, this is in very good agreement with the classification of coals presented in the sub-section 4.1. On the other hand, the obtained values of Ea for Kolubara (2015)/(2018) coals are in very good agreement with reported values of Ea attributed to oxidation process of lignite (Ea = 61 – 79 kJ mol-1) [56]. The detailed inspection of the plots presented in Fig. 9 and Fig. S3 shows that TENT A (2015) coal stands out from the rest of coals concerning the variation of the Ea values during process progression, which may indicates that higher content of mineral matter present in this coal (Table 1) can affect the combustion kinetics. At the initial stage of thermo-oxidative decomposition restrained the thermally induced oxidative destruction of the organic matter matrix, and also chemical bonds between the organic and inorganic matter with the following formation and emission of different gaseous compounds through the number of chemical reactions. So, it can be concluded that initial stage in coal oxidation process was controlled by chemical reactions. At higher values of conversion, oxidative decompositions of organic matter which has passed certain transformations at previous stage continue and the oxidation of char formed takes place. The slopes of the Ea – α plots and increasing trends of Ea values in the last parts of isoconversional dependencies shown in the Fig. 9, indicate that the char combustion process probably proceeds through the reaction mechanism which differs for Kolubara and TENT A coals. However, the analysis shows that carbon content into the solid fuel (Table 1) and the coal char structure originated from various low-quality coals, can affect the magnitude of Ea in the last stage of air-combustion process (TENT A (2018) coal contains the highest carbon content (20.44 %), where the Ea for high conversion values exhibits higher values than those identified for other coals (Fig. 9)). The Ea value can depends on the coal rank, and the activation energy has a tendency to be increased for coals which posses the quite high content of mineral matter (MM). In order to check correctness of application of model-free methods/ models for investigation of air-combustion process of studied low-rank coals, the kinetic parameters estimated by these methods are used for further optimization of the process, based on Eq. (1). Fig. 10 shows experimental and optimized conversion curves of combustion process in an air atmosphere at the various heating rates (10, 20 and 30 °C min-1), for all studied coals. Within optimization procedure, the moisture evaporation stage was not included in the analysis. From obtained results (Fig. 10), we can see that kinetic parameters estimated from model-free approach very well describes the entire air-combustion process for all considered coals. In all cases, the correlation coefficient (R) is very high, which means that the numerical optimization confirms the true kinetic parameters established for all studied cases. In order to determine individual reaction mechanisms and type of reactions that occurs within each of above-indicated transformations through which coal samples pass through, the model-based analysis was applied. This approach allows determination of combustion reaction pathways scheme, for each considered coal sample. By applying the procedure described in the sub-section 3.2., it was found that for all coals, the combustion process in an air, obeys to Fn reaction type model (n-th order reactions involved in combustion mechanism scheme). However, the reaction pathways between Kolubara and TENT A coals differ in that respect, where combustion process of Kolubara coals proceeds through two-parallel independent reactions with existence of sequential steps, while combustion process of TENT A coals proceeds through two-parallel independent reactions, but one of them is complex, which is separated by two independent products. Results of model-based analysis with all details regarding kinetic quantities associated to Kolubara (2015), (2018) and TENT A (2015), 12

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Fig. 10. Model-free optimization (using FR results) of air-combustion processes of investigated coals (Kolubara (2015)/(2018) and TENT A (2015)/(2018)) at various heating rates (10 °C min-1: ◊ - experimental; —fit, 20 °C min-1: ◊ - experimental; — fit; 30 °C min-1: ◊ - experimental; — fit).

(2018) air-combustion processes are summarized in Table S1-S4 (Supplementary material). From obtained results, for Kolubara (2015)/ (2018) air-combustion process, the following conclusions can be drawn: 1.a) Both coals are characterized by two-parallel independent reactions, where the first reaction is complex consisting two consecutive steps (A → B → C). The reaction series A → B → C correspond to coal devolatilisation (A → B) and volatiles combustion (B → C) steps, respectively. For these steps, Kolubara (2015) coal has higher values of kinetic parameters (A, Ea, and n (the reaction order)) than those for Kolubara (2018) coal (Tables S1 and S2 (Supplementary material)). In both cases, the devolatilisation reaction step has the highest contribution in overall process. However, the contribution of this step for Kolubara (2018) coal is higher than in the case of Kolubara (2015) coal, since it is possesses the higher content of volatile matter (VM) (Table 1). Generally, the relatively lower Ea values for devolatilisation step, is because just releasing the gases with small molecules and some light molecules, arising from the rupturing of the weak bonds. The higher n values indicate the greater thermal stability during combustion and more complex combustion conditions [57]. For combustion of volatized products in the case of Kolubara (2015) coal, it is necessary to input a much more energy than in the case of Kolubara (2018) coal, where the combustion of the products liberated from devolatilisation takes place in a much more complex manner. By transition period, from A → B to B → C steps for considered coals, the Ea increased, which means that reactivity slightly decreases. So, the highest reactivity was related to devolatilisation step, but enters into combustible step, the volatiles concentration increased, and therefore, the energy required for combustion was elevated, where the reactivity was reduced. 1.b) For both considered coals, the step D → E (Tables S1 and S2 (Supplementary material)) belongs to the char oxidation process. In considered cases, this step takes the smallest contribution to overall process. In analyzed step, we can notice the main difference between these two coals. For Kolubara (2018), the char oxidation occurs with a high activation energy barrier in comparison with one presented in Kolubara (2015) (Tables S1 and S2 (Supplementary material)). However, the characteristics of the char depend on the type and the

size of original coal as well as on heating conditions. In addition, for Kolubara coals, the n values are very low (n ˜ 0.020/0.010), which may indicates that the oxygen consumption rate increases with an increasing of temperature [58]. It can be observed that for Kolubara (2018) coal, the reaction step where volatiles combustion (primary combustion) takes place (B → C) is characterized by the lower Ea value, as compared to char combustion (secondary combustion) step (D → E) [59], but, for Kolubara (2015) coal, we have the opposite case (Tables S1 and S2 (Supplementary material)). This is a consequence of the transient nature of thermal analysis measurements, in which physical structure of coal is continuously changing during reaction. At higher temperatures, where secondary combustion takes place, the reaction probably tends to be controlled by intra-particle diffusion. As combustion proceeds, the carbon is continuously removed from particles thereby opening pores and reducing diffusion resistances. The conversion mode of combustion may also become dominant, and thereby enlarging the fraction of a particle actually available to reaction. These effects enhance the reaction rate and contribute to a decreasing of Ea. At the same time, with decreasing of diffusion resistances, the total specific surface area is supposed to be reduced by both, the changing of -micro and -mesopores into macropores. Taking into account these facts, it can be concluded that in the view of Ea reduction in char combustion step for Kolubara (2015) coal, it appears that effect of reducing diffusion resistances predominates over the opposite effect of loosing the surface area. The higher temperatures and consequent decreased kinetic resistances could be the main reasons for enhanced the reaction and decreasing trend of the Ea, within the D → E step (Tables S1 and S2 (Supplementary material)). For obtained results of TENT A (2015)/(2018) air-combustion process, the following conclusions can be drawn: 2.a) Unlike air-combustion process of Kolubara coals, the combustion mechanism of TENT A coals is something different. The devolatilisation step and char oxidation step occur simultaneously as parallel reactions (likely as competing reactions), where participation of pyrolysis pathway dominates almost double (in terms of contribution) in comparison with combustion of volatiles (Tables S3 and S4 13

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(Supplementary material)). In comparison with Kolubara (2015) coal, both TENT A coals (2015 and 2018), are characterized by lower Ea value for primary combustion step (A → C) than for secondary combustion step (D → E). This is the main difference, which separates these coals from the previously analyzed. For D → E reaction step, TENT A coals show an increase in the value of n (n > 2.50), where char oxidation process becomes more complicated. This can be linked to remarkably surface/morphology transformation of the char particles. In this case the oxygen diffusion from gas environment through boundary layer is significantly more difficult, and making it difficult to finally escapes CO2 in a gaseous boundary zone around the char particle. However, for TENT A coals, it can be assumed that the cross-linking reaction temperatures are altered (associated with higher heating rates) compared to those related for Kolubara coals. Since that cross-linking reactions may controls the tar yield, reactivity, as well as the char surface area, we can assume that the TENT A coals probably contains higher proportion of micropores, where the gas diffusion in these pores is to high (the increased Ea values for D → E step (Tables S3 and S4 (Supplementary material))). In addition, it can be assumed that the thermal shrinkage of the pores also takes place [60]. 2.b) The high content of mineral matter (MM) for these coals (especially for TENT A (2015) coal) (Table 1) may affect the reactivity by both blocking oxygen access to active carbon sites and influencing the microscopic carbon structure that evolves during combustion (changing the surface morphology of char) [61,62]. Obviously, the char oxidation stage can be significantly affected by the MM presents in coal samples, yielding the high ash content (Table 1). The included minerals can affect the conversion kinetics of the char, as the minerals may fuse and coat the surface of burning char particles, reducing the rate of the char combustion [63]. So, the MM content can affects on the difference in activation energy values of coal samples. On the other hand, TENT A (2015) coal has the least volatile matter content (Table 1) than other coals, so in its case, the maximum reactivity temperature can be affected by the reaction surface area. Since that the Tm values increase with an increasing of the heating rate (Table 2), and varied with VM contents (Table 1), these phenomena can be conditioned by the changes in coals and obtained chars fluidity as well as in the change of the cross-linking temperatures with the volatile content and the heating rate [64]. A general representative schematic diagram of reaction mechanisms of the combustion processes of studied coals in an air atmosphere is shown in Scheme 1. Fig. 11 shows comparison between experimentally obtained and predicted conversion curves, from model-based analysis including the established air-combustion mechanism schemes for all investigated

coals. It can be observed that excellent agreement between experimental and model-based predicted conversion curves exists, where high correlation coefficient (R) values (R > 0.99950) were obtained. Sensitive deviations were not observed between the kinetic parameters quantities among all studied coals, where the high reliability between model-free and model-based approaches appears, and established kinetic models very well describes the complete thermo-oxidative histories of investigated coals. 5. Conclusions This study investigated thermal and kinetics behavior of coals from different coal-fired power plants and different annual periods (Kolubara (2015)/(2018) and TENT A (2015)/(2018)) during combustion process in an air atmosphere, using simultaneous TGA-DTG-DTA experiments, coupled with mass spectrometry (MS) for gas evolving analysis. Fourier transform infrared (FTIR) spectroscopy was used to gain additional information on coals structures. From obtained results of analyses, the following conclusions were derived: 1) The lowest CO2 emission coefficient (KCO2) has the TENT A (2015) coal, while for Kolubara (2015) coal, the KCO2 value is the highest among all coals. However, considering high ash content of studied coals, the “best” technical properties for firing in power-plants has Kolubara (2018) coal (the smallest percentage of ash, 56.03 % and highest percentage of VM). For Kolubara (2018) coal (with VM = 24.46 % and fuel ratio around 0.50), the tangential firing units at pithead stations seem as the best option for its firing. For TENT A (2015) and Kolubara (2015) coals which are characterized by lower VM content (14.05 % and 18.69 %, respectively, and with ash content that exceeds 65 %), their best firing performances may be achieved in double arch U-flame furnaces. In the case of TENT A (2018) coal, which approximately belongs to medium VM coals, its optimal burning may be achieved in modified tangential fired burners which can provide high primary and secondary air temperature and pressure as well as chrome ore reflectors in vicinity of the burners. These proposals were implemented by considering the percentage of the ash in studied coals. 2) It was established that Kolubara (2018) coal shows better combustion reactivity in comparison with Kolubara (2015) coal, which was identified from the lower peak temperature, Tm, as well as oxidation onset temperature (Ti) values. TENT A coals exhibit higher peak temperatures compared with ones attached to Kolubara coals, showing reduced reactivity, and where DTG thermal evolution profile for TENT (2018) coal manifests some disruption with an increasing of heating rate. It was found that one of the reasons for this behavior lies in certain differences in decomposition kinetics of these two coal types (with altered cross-linking reaction temperatures related to TENT A coals). 3) The MS analysis revealed that hydrocarbon fuels are successfully converted into CO2 and H2O plus additional heat which take place through the exothermic transformations. By analyzing the MS signals of the main gaseous combustion products, the qualitative correlation between the amounts of emitted CO2 and KCO2 magnitude could not been developed. 4) The activation energy values obtained from Friedman (FR) and Kissinger-Akahira-Sunose (KAS) isoconversional model-free methods are in good agreement with the ranges of activation energy values which belong to determined coal samples rank classification. The obtained results also agree with ones reported in literature. 5) Model-based analysis was provided a more detailed description of air-combustion mechanisms. For combustion of volatilized products in the case of Kolubara (2015) coal, it is necessary to input a much more energy than in the case of Kolubara (2018) coal, where the combustion of the products liberated from devolatilisation takes place in a complex manner. Conclusions obtained here, are based on

Scheme 1. A graphic representation of the proposed reaction mechanisms through individual steps (Supplementary material, Tables S1 – S4) for aircombustion processes of Kolubara and TENT A coals. 14

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Fig. 11. The comparison between experimental (symbols) and model-based (full lines) predicted conversion curves for air-combustion processes of investigated coals at different heating rates (10 °C min-1: ◊ - experimental; — predicted, 20 °C min-1: ◊ - experimental; — predicted; 30 °C min-1: ◊ - experimental; — predicted).

foundations from model-free one-step reaction methods. However, some differences in combustion mechanisms between Kolubara (2015) and (2018) coals were identified. It was found that for Kolubara (2015) coal, when high temperatures are reached and where the secondary combustion takes place, the reaction tends to be controlled by the intra-particle diffusion. As combustion proceeds, the carbon is continuously removed from particles thereby opening pores and reducing diffusion resistances. This reaction pathway significantly affects on the gas escaping rates, especially for CO2 in a gaseous boundary zone. It was found that included minerals have a higher tendency to remain in the char during combustion, which is more pronounced for TENT A coals.

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