Spontaneous ignition behaviour of coal dust

ARTICLE IN PRESS

JID: PROCI

[m;June 21, 2018;18:21]

Available online at www.sciencedirect.com

Proceedings of the Combustion Institute 000 (2018) 1–11 www.elsevier.com/locate/proci

Spontaneous ignition behaviour of coal dust accumulations: A comparison of extrapolation methods from lab-scale to industrial-scaleR Dejian Wu a, Martin Schmidt b,∗, Jan Berghmans c a School

b BAM

of Chemical Engineering, Sichuan Univerisity, Chengdu 610065, China Federal institute for Materials Research and Testing by Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, D-12205 Berlin, Germany c Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300A, B3001 Leuven, Belgium Received 29 November 2017; accepted 30 May 2018 Available online xxx

Abstract Smouldering fires and explosions arising from self-ignition of coal dust deposits represent a serious hazard for human beings, environment and industry. It is essential for plant operators to know the conditions (temperature, duration and quantities) at which storage will be safe. In this work, self-ignition behaviour of three bituminous coal dusts in large scales are theoretically studied, based on the experimental data obtained via a standardized hot-basket apparatus. A comprehensive 2-D transient model is developed, using a 2nd-order reaction kinetics considering both coal and oxygen consumptions, to investigate self-ignition parameters of coal dust accumulations. The numerical model shows a less conservative prediction compared with the steady-state methods. The computed self-ignition temperature and ignition delay time show a satisfaction agreement with lab-scale experimental results. In addition, the influences of ambient temperature and moisture content are analysed. The result shows that the moisture content delays the ignition and a small variation of the ambient temperature nearby the critical condition will lead to a large difference in the ignition delay time. © 2018 The Combustion Institute. Published by Elsevier Inc. All rights reserved. Keywords: Self-ignition temperature; Ignition delay time; Coal dust; Modelling; Large scale

1. Introduction Dust accumulation often occurs in coal mills and power plants that handle, store or process dust R

Colloquium: FIRE RESEARCH. Corresponding author. E-mail address: [email protected] Schmidt). ∗

(M.

or other bulk materials. Once in contact with the oxidant and a mild heat source, the accumulated dust may self-ignite to initiate smouldering fires which can further generate hot spots to trigger dust explosion [1–4]. Therefore, self-ignition of coal dust accumulations is of interest in the fields of industrial safety, pulverized coal combustion, as well as a fundamental issue in combustion science and technology [5–7]. The self-ignition process refers to a physicochemical process of thermal

https://doi.org/10.1016/j.proci.2018.05.140 1540-7489 © 2018 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Please cite this article as: D. Wu et al., Spontaneous ignition behaviour of coal dust accumulations: A comparison of extrapolation methods from lab-scale to industrial-scale, Proceedings of the Combustion Institute (2018), https://doi.org/10.1016/j.proci.2018.05.140

ARTICLE IN PRESS

JID: PROCI

2

[m;June 21, 2018;18:21]

D. Wu et al. / Proceedings of the Combustion Institute 000 (2018) 1–11

Nomenclature Ac c d D Hc Ea hm ht j M r R Rr t T V/S Y SIT Greeks α ɛ δ cr υ λ ρ

pre-exponential factor, m3 /kg-s specific heat, J/kg-K diameter of a single dust, m mass diffusivity, m2 /s heat of oxidation reaction, kJ/kg apparent activation energy, kJ/mol mass transfer coefficient, m/s overall heat transfer coefficient, W/m2 -K mass flux, kg/m2 -s molar mass, kg/mol rate of reaction, kg/m3 -s ideal gas constant, J/mol-K radius of basket, m time, s/h/d temperature, K ratio of volume to surface, m mass fraction self-ignition temperature,°C thermal diffusivity, m2 /s bulk porosity critical Frank-Kamenetzkii parameter stoichiometric coefficient heat conductivity, W/m-K density, kg/m3

Subscript 0 initial a ambient ac active component of coal b bulk of basket c coal cb centre of basket ct calculation termination g gas i gas species SI self-ignition wall wall of basket runaway, which depends on the characteristics of combustible bulk materials and packing conditions as well as the ambient conditions [1,6,8,9]. The vast literature related to various effects on self-ignition of coal [7,10,11–13], biomass [10], shale rock [14] and other granular materials has been investigated in lab-scale sizes by researchers worldwide. However, yet little attention has been paid to verify the volume dependence of the selfignition behaviour of dust accumulations from a lab size to an industrial size. Plotting ln(δcr TSI2 /R2r ) of variously sized dust accumulations versus the reciprocal values of the respective self-ignition temperature (1/SIT), characterises the self-ignition behaviour of dust accumulations of a different scale [1]. This steady-state

method based on the thermal explosion theory was developed by Frank-Kamenetzkii (F-K) [15]. The critical F-K parameter is defined for some simply geometries such as sphere, equidistant cylinder and cube, etc. A simplified, empiric method is the Pseudo-Arrhenius (P-A) or scaling method. These methods enshrined in European standard EN 15188 [16] involve the standardized basket test to obtain the correlations between volumes, SITs and ignition delay times (i.e., induction time, ti ), which is widely used for practical applications [13,17]. Nevertheless, the above steady-state 0-D models have some restrictions on sample geometries and boundary conditions and exclude the effect of oxygen diffusional transport. In fact, the typical self-ignition and the following smouldering are governed by the O2 supply and heat losses in the porous fuel bed [1,2,10,18]. Several numerical models coupling heat and mass transfer equations, as well as O2 transport have been developed to study the process of self-ignition or subsequent smouldering combustion of porous beds of peat [19], coal [20], biomass [21] or polymer material [22] in recent years. Previous studies have not considered simulations of size effects or 2nd-order kinetic models. This work theoretically evaluates the selfignition parameters of three coal samples in industrial scales. We use a comprehensive 2-D model and a previously developed one-step 2nd-order reaction kinetics [23,24] considering both coal density and oxygen density to investigate the ignition behaviour of coal dust accumulations. The computational results are compared with experiments in lab scales and the extrapolated results based on stead-state methods in industrial scales. The effects of moisture content and ambient temperature on self-ignition parameters are analysed. 2. Standardized hot storage test The basic experimental apparatus included a laboratory oven following the standardized basket test [4,16]. An inner chamber was installed inside of the regular heat storage oven to better control the flow near coal dust samples, as illustrated in Fig. 1. Five equidistant cylindrical mesh wire baskets with volumes of 25, 50, 100, 400 and 1600 mL were used. For each basket, the central temperature (Tcb ) was measured by a K-type thermocouple. An additional thermocouple was located outside of the basket to monitor the oven ambient temperature (Ta ) in the inner chamber. More thermocouples placed at different locations of the basket were optional as shown in Fig. 2. All thermocouple readings were recorded simultaneously and every 30 seconds. The fixed gas mixture flux of 2 L/min was monitored by a flowmeter and was preheated with a copper pipe coil before entering the inner chamber. Once the oven was stabilized at the target temperature (± 1 °C), the sample in the basket


JID: PROCI

ARTICLE IN PRESS

[m;June 21, 2018;18:21]


Fig. 1. Schematic diagram of the hot-oven test.

was hung by a hook with an oscillating tungsten wire. Three different coal dusts: sub-bituminous Indonesian Sebuku (IS) coal, medium-volatile bituminous South African (SA) coal and highvolatile bituminous Pittsburgh No.8 (P8) coal were tested in this study. The properties of these three coal samples were given in [7,24], including particle size distribution, proximate and ultimate analysis. Figure 2 shows an example of the temperature evolution (T-t curve) of 400 mL SA coal dust at 110 °C (a supercritical condition) in air flow including the processes of heating/drying, self-heating, ignition and smouldering propagation. Three more thermocouples were placed to measure the temperature information at the different locations. Three typical inflections were observed on Tcb -t curve, i.e., the solid line shown in Fig. 2. The results from the endothermic evaporation reaction of coal significantly retards the heating, which is the major contributor for the 1st inflection around 50-60 °C. With rising temperature, the exothermic reaction

3

rate increases to facilitate a further temperature rise as a positive feedback. Once Tcb overtakes Ta , it tends to be the highest temperature due to the better heat insulation in the centre of the basket until ignition at around 5.92 h, denoted the 2nd inflection point in the T-t curves at all locations. Then a self-sustained smouldering fire starts to spread out and burnout the dust sample. A typical smouldering propagation includes the competing oxidation and pyrolysis reactions [18,19]. The 3rd and the 4th inflection points on the Tcb -t curve and T1 -t curve are attributed by the synergistic effect of endothermic pyrolysis and exothermic oxidation reactions. The 3rd is dominated by the pyrolysis reaction due to the insufficient oxygen supply at the centre of the basket (i.e., further away from the ambient), while the 4th inflection point is dominated by the oxidation of both solid pyrolyzed product (i.e. char) and the rest of coal particle.

3. Computational model The 2-D axisymmetric transient conservation equations for the bulk material were solved. The domain for the computation is shown in Fig. 3, i.e., only 1/4 symmetry domain of coal dust is modelled. The model geometry consists of two open boundaries (bottom and right sides) and the axisymmetric axis or vertical centreline r = 0 (left and top sides). Both the radius and the height of the dust accumulation domain are Rr for the equidistant cylinder baskets. For simplicity, the following assumptions are made: • only the diffusional effect is considered as this is the dominant transport mechanism;

Fig. 2. Experimental temperature profiles of the centre and the off-centre for 400 mL SA coal dust in air at 110 °C.


ARTICLE IN PRESS

JID: PROCI

4

[m;June 21, 2018;18:21]


Fig. 3. Simulation domain (1/4 of an equidistant cylindrical wire mesh basket with sample).

• only the heat conduction is considered as the heat convection and buoyancy effects are low; • the drying process in the simulations is not considered due to the small moisture content of the coal; • properties such as the bulk porosity, thermal conductivity, specific heat capacity and stoichiometric coefficients are independent of time and temperature; • all the boundaries have the same heat transfer coefficient; • a one-step global oxidation reaction formulation is considered as shown in Eq. (1). 3.1. Chemical kinetics A one-step global oxidation reaction is used to characterise the self-ignition process of the coal in the dry basis at low temperatures. The average stoichiometric coefficient for the coal with moisture content can be determined according to the elemental and proximate analyses [23,24]. Therefore, South African (SA) coal combustion can be simply described as C56.3 H42.6 O7.3 +1.5H2 O + Ash +54.6O2 Coal·H2 O

→ 40CO2 +16CO + 22.1H2 O(g) + 0.34CH4 Gasproducts

+ Ash,

(1)

where C56.3 H42.6 O7.3 is the chemical formula of the active component on a basis of 1000 g coal. More details are given in [24]. The 2nd-order heterogeneous oxidation rate of coal dust samples can be formulated by the Arrhenius law as: Ea . rc = −(1 − εb )ρO2 ρc Ac exp − (2) RT

Thus, the reaction rate of the oxygen and the gas products can be specified as ri =

νi Mi rc . νac Mac

(3)

3.2. Governing equations Based on the assumptions above, the conservation equations describing the mechanisms of heat and mass transfer in the immobile porous coal bed are the following: ∂T =∇ ∂t · (((1 − εb )λc + εb λg,a )∇T ) + rc Hc ,

[(1 − εb )ρc cc + εb ρg cg,a ]

( 1 − εb )

∂ρc = −rc . ∂t

(4)

(5)

The air combustion system mainly contains three gaseous components: inert gas (N2 ), oxidant (O2 ) and gas products (CO2 , CO and H2 O are considered, and CH4 is neglected due to small volume shown in Eq. (1)), where the gas species concentrations are in the same order of magnitude and none of the species can be identified as a solvent. Therefore, the Maxwell-Stefan equations combined with the ideal gas law [25] accounting for all interactions of species in a mixture are used in this paper. The gas species transport mass conservation reads as: ∂ (ρgYi ) + ∇(ji ) = −ri , ∂t

(6)

where the subscript i denotes the gas species and ji is the mass flux relative to the mass averaged velocity resulting from the diffusion or convection. The details of gas species transport mass conservation are given in [23,24].


ARTICLE IN PRESS

JID: PROCI

D. Wu et al. / Proceedings of the Combustion Institute 000 (2018) 1–11 Table 1 Input parameters for the SA coal sample computations. Parameters

Value

Source

Hc λc Ea Ac ρ b, 0 ɛb cc dc Mac cg, a α g, a λg,a ht

27.37 kJ/kg 0.172 W/m-K 98.5 kJ/mol 1.5e7 m3 /kg-s 600 kg/m3 0.5 1130 J/kg-K 1.5e−5 m 833 kg/mol 1013 J/kg-K 2.55e−5 m2 /s 0.028 W/m-K 11 W/m2 -K

Exp. Exp. [24] [24] Exp. Exp. Exp. Exp. [24] [27] [27] [27] [13]

3.3. Boundary and initial conditions A Neumann condition was used for both the temperature field of porous dusts and the concentration field of gas species [11,23]. This suggests that a heat flux and a mass flux were defined for the heat transfer and the gas species transport, respectively. The boundary conditions for Eq. (4) are

∂T

− (1 − εb )λc + εb λg,a ∂z z=0

∂T

= − (1 − εb )λc + εb λg,a (7)

∂r r=Rr

∂T

∂T

= ht (Ta − Twall ); = = 0. ∂z z=2Rr ∂r r=0 Similarly, for the boundary conditions of mass species [26], we have

ji(z ) z=0 = ji(r ) r=Rr = hm (Yi,a − Yi )(ρg + ρg,a )/2 and

ji(r ) r=0 = ji(z ) r=0 = ji(r ) z=0 = ji(z ) r=Rr

= ji(r ) z=Rr = ji(z) z=Rr = 0, (8) where hm can be obtained from the analogy of heat and mass transfer [26], ht = ρg,a cg,a Le1−n , hm

(9)

where Le = αg,a /Dg,a is the Lewis number (the ratio of thermal diffusivity and mass diffusivity). It is reasonable to assume a value of n = 1/3 for most applications [26]. At t = 0, the entire coal accumulation is unreacted, the temperatures of the coal and air are constant. Thus, T0 is 20°C, and YO2 ,0 is 0.23 for air condition. 3.4. Simulation SA coal dust sample was selected for simulation and the input parameters are summarised in Table 1. The air properties were selected at 50 °C and 1 atm [27]. The ignition criterion was defined

[m;June 21, 2018;18:21]

5

when the measured sample temperature exceeds the oven temperature by 60 °C. Once ignition was observed, the resolution between ignition and no ignition cases was fine tuned to within 1 °C. Then the SIT was regarded as the temperature at the max. subcritical case, and ti was defined as the interval of time between reaching the oven temperature and the event of an ignition from the T-t curves of the min. supercritical case. All the simulations were terminated once the temperature reached an ignition threshold because the current model is only valid for the self-heating until ignition. For simplicity and coherence, 210 °C was arbitrarily selected as the calculation termination temperature (Tct ) which meets the requirement for the minimum size of 25 mL according to the experimental work [23,24]. The average diameter of grid of 4 × 10−4 m was found to generate sufficiently grid independent results of ignition and was used in all the simulations. The time step of simulations was set as 30 s for output of results which is consistent with the frequency of the experimental data collecting. Moreover, the calculation termination time or computational duration (tct ) was set as 24 h for the cases from 25 to 1600 mL, and it was gradually increased for the cases from 3 L to 10 m3 . For solving the system of equations numerically the commercial Finite-Element-Code COMSOL was used.

4. Results and discussion 4.1. Steady-state model Table 2 summarises the measured self-ignition parameters for three coal dusts with the error ± 1 °C due to the accuracy of the isothermal heat oven and test procedure [16]. According to Table 2, plotting the lg(Vb /Sb ) and ln(δcr TSI2 /R2r ) of differently sized dust deposits versus 1/TSI respectively, we have correspondingly Figs. 4 and 5. For a certain ambient temperature, the critical storage volume and the induction time can be extrapolated or interpolated. Extrapolated SIT values of several typical volumes in practical scale for three coal samples are summarised in Table 3 as an example. Compared with measured SITs, a distinct difference is found between IS coal and other two coals as shown in Fig. 4. Note that the left ordinate and the right ordinate have the same meaning in Fig. 4, but in two different expressions. This difference of SIT increases with increasing volumes and the order of SIT follows IS > SA ≈ P8 for large volumes (> 3 L), which is following the reverse order of the coal maturity. In addition, there is no distinct difference of SIT values for all three coal dusts between the P-A model and the F-K model as summarised in Table 3, which is consistent with the finding by Torrent et al. [17].


ARTICLE IN PRESS

JID: PROCI

6

[m;June 21, 2018;18:21]


Table 2 Measured self-ignition parameters in hot-oven test for three coal dusts [7]. Samples IS coal P8 coal SA coal

Self-ignition temperatures (°C) 25 ml 50 ml 100 ml 400 ml

1600 ml

Ignition delay time (h) 25 ml 50 ml 100 ml

400 ml

1600 ml

134 136 134

100 97 96

0.36 0.45 0.49

2.58 3.31 3.62

6.71 8.32 9.08

128 128 127

122 124 122

110 109 108

0.59 0.75 0.81

1.06 1.15 1.17

Fig. 4. Pseudo-Arrhenius plot of self-ignition temperatures for three coal dusts. Table 3 Extrapolated self-ignition temperature for three coal dusts in air. Equidistant cylinder volume (L) Model

SIT (°C) IS P-A/F-K

SA P-A/F-K

P8 P-A/F-K

3 10 100 400 1000 4000 10,000

95/95 86/86 71/71 62/62 57/57 49/49 44/44

91/91 82/82 65/65 55/56 49/50 41/41 35/36

92/92 82/82 65/65 55/56 49/50 41/41 35/36

4.2. Numerical model The numerical calculated SIT and ti are determined from the max. subcritical and the min. supercritical temperature evolutions with time (T-t curves). Fig. 6 gives an example of both numerical experimental T-t curves for 400 mL SA coal sample with both ignition and non-ignition cases. Generally, it shows a satisfactory agreement fitting between numerical and experimental T-t curves for the cases of 108 and 110 °C, except for the duration from around 60–120 °C. The reason is probably because of the drying or evaporation reactions which

may distinctly retard the ignition. Further discussion will be given in the Section 4.3 of this work. Moreover, 107 °C was found to be the max. subcritical temperature (i.e., num. SIT) instead of 108 °C for the 400 mL SA coal sample according to the numerical simulations. And the corresponding numerical ti is 446 min at 108 °C, which is much longer than the experimental ti (123 min) at a storage temperature of 110 °C. This reveals that ti is overestimated by the numerical model without considering the drying process. Note that the experimental ti here is slightly different with the one shown in Fig. 2 (around 2.5 h), suggesting the ignition delay


JID: PROCI

ARTICLE IN PRESS

[m;June 21, 2018;18:21]


7

Fig. 5. Frank-Kamenetzkii plot of self-ignition temperatures for three coal dusts.

Fig. 6. Comparison of the numerical and experimental central temperature evolution of 400 mL SA coal dust sample.

may vary even at the “same” oven ambient temperature. This is because thermal stability of the isothermal hot-oven is ± 1 °C, and this small variation may trigger a big difference of the ignition delay time when the ambient temperature is close to the supercritical ignition temperature. More discussion will be given later in this text. Table 4 lists the numerically calculated SITs and ti for SA coal dust at different volumes. SIT decreases from 127 to 42 °C as the coal sample volume increases from 25 mL to 10 m3 , showing the very strong effect of the bulk volume, agreeing with the experimental observations summarized in Table 2.

However, comparatively large deviations between experimental and numerical SITs are found for small volumes (≤ 100 mL). This is probably caused by the improper kinetic parameters (see Table 1) derived by the modified 2nd-order F-K model based on the assumption of an infinite Biot number [24]. In other words, the F-K model becomes less accurate with decreasing sample volume. In addition, ignition delay time increases with increasing dust volume as general trend, agreeing with experiments. However, it is unmeaning to compare the computed ignition delay time with the experimental result at different supercritical ignition temperatures.


ARTICLE IN PRESS

JID: PROCI

8

[m;June 21, 2018;18:21]


Table 4 Computational self-ignition temperature and ignition delay time for SA coal dust in air. Parameters SIT (°C) ti (h)

Dust volume of SA coal dust (L) 0.025 0.05 0.1 0.4

1.6

3

10

100

400

1000

4000

10000

127 2.5

97 14.8

92 18

83 42

69 60

59 167

53 322

47 366

42 593

122 4.3

117 5

107 7.4

Fig. 7. Comparison of self-ignition temperature by various models.

Figure 7 compares the computational SITs with the extrapolated SITs based on the steady-state methods for SA coal sample. Note that the left ordinate is only for P-A model, while the right ordinate is used for all three methods in Fig. 7. There is a pronounced crossing point between the trendlines of the numerical model and the steady-state models around 1 L at 100 °C. When the sample volume exceeds this crossing point, all the computational SITs are clearly higher than the extrapolated SITs, and this trend becomes more pronounced with increasing dust volume because oxygen diffusion is increasingly important for large volumes. This result might reveal that the numerical method is less conservative than the former steady-state models. The steady-state models tend to give an underestimated SIT because the oxygen supply is assumed to be always sufficient. Therefore, the current numerical model may be more practical-oriented. Respectively plotting the experimental lg ti (see Table 2) based on the P-A model and the numerical lg ti (see Table 4) generate two linear correlations for SA coal as shown in Fig. 8. The comparison shows that the overestimated computational ti becomes shorter for larger volumes compared to the extrapolated value by the P-A model based on the experimental data as the dust sample value ex-

ceeds a certain volume (i.e., around 8 L). The main reason is that different SITs were used for the PA method and the numerical method, respectively; the induction time strongly depends on the storage temperature. For example, the num. SIT is 53 °C (see Table 4), while the extrapolated SIT is 49 °C (see Table 3) for 1 m3 SA coal sample, suggested that no ignition occurs in the numerical simulation when SIT is no more than 53 °C, i.e., numerical ti is infinite. Generally, the P-A method underestimates the ignition delay time, i.e., the extrapolated ti should be shorter than the practical experimental value because the P-A method is a 0-D model without the limitation from the oxygen diffusion. For a given coal sample, thus the value of ti strongly depends on the storage temperature. Consequently, the numerical simulation is the only way to estimate the ignition delay time of large scales in dependence of the storage temperature, different geometries and other varying boundary conditions. 4.3. Further influencing factors To verify the dependence of the ignition delay time on ignition temperature, an example of the typical central temperature evolution (Tcb -t) curves for 1600 mL SA coal dust sample in air is shown


JID: PROCI

ARTICLE IN PRESS

[m;June 21, 2018;18:21]


9

Fig. 8. Comparison of ignition delay time by various models.

Fig. 9. Central temperature evolution for 1600 mL SA coal dust air at different supercritical temperatures.

in Fig. 9, including both experiments and simulations nearby the critical ignition temperature. Inevitably, a large deviation is observed between the computed and the experimental Tcb -t curves after around 60 °C as expected. The relatively higher computed temperature after 60 °C during the heating process is mainly caused by the neglected latent heat of water content, i.e., the drying process or the evaporation of water is a typical endother-

mic reaction [13,19]. In addition, the experimental errors such as the inaccurate placing position of the central thermocouple and the unstable Ta (± 1 °C) also contribute to this deviation on the Tcb t curves. Note that when the Ta is close to the critical ignition temperature, the ignition delay time becomes greatly sensitive, e.g., only 0.7 °C (from 98 to 97.3 °C) difference of Ta results into around 7 h difference of ignition delay time (see Fig. 9).


ARTICLE IN PRESS

JID: PROCI

10

[m;June 21, 2018;18:21]


Fig. 10. The effect of water content on heating process in the case of 100 mL SA coal dust.

This is reasonable because the ignition delay time decreases from infinite at SIT (97 °C in this case, i.e., max. subcritical) to a certain value for supercritical ignition temperatures. To confirm the effect of moisture content (MC) on the heating process, 100 mL SA coal dust for both dried sample (in a vacuum oven for ∼24 h at 50 °C) and raw sample (2.7% MC by mass) was tested in air at an oven temperature of 108 °C, and the result is illustrated in Fig. 10. It shows that there is a slight difference in the beginning due to the different moments of placing coal sample, but the distinct difference can be seen after around 53 °C. This result confirms that MC distinctly retards the ignition. 5. Conclusions In this work, a theoretical evaluation of key selfignition parameters has been conducted from lab scale to industrial scale for three coal dust samples. Two steady-state models are used for extrapolation based on the standardised hot-basket test data, and a comprehensive 2-D transient model with a 2nd-order heterogeneous kinetics is implemented to predict the self-ignition characteristics. Results show that the difference between these two steadystate methods is not pronounced. The numerical model is assumed to be more reliable than the former steady-state methods to evaluate self-ignition temperature. Only numerical simulation is the way to estimate the induction time of coal samples in industrial scales in dependence of storage temperature and further influencing factors as shape of the deposit, the availability of oxygen and moisture content of the material. Sensitivity analysis shows that the dependence of ignition delay time on am-

bient temperature significantly enhances nearby the critical ignition temperature, and the presence of moisture content increases the certainty for ignition delay time estimation. Further medium/largescale experimental verifications are necessary for the spontaneous combustion phenomenon.

Acknowledgments D. Wu is supported by “the Fundamental Research Funds for the Central Universities”. The authors are grateful to Dr. Frederik (Adinex) for assistance running the experiments and to Ms. Meike Kerl for proofreading. Valuable comments from reviewers are also acknowledged. References [1] P.C. Bowes, Self-Heating: Evaluating and Controlling the Hazards, Elsevier, London, 1984. [2] T.J. Ohlemiller, Prog. Energy Combust. Sci. 11 (1985) 277–310. [3] V. Babrauskas, Ignition Handbook, Fire Science Publishers, Washington, 2003. [4] D. Wu, M. Schmidt, X. Huang, F. Verplaetsen, Proc. Combust. Inst. 36 (2017) 3195–3202. [5] R.H. Essenhigh, M.K. Misra, D.W. Shaw, Combust. Flame 77 (1989) 3–30. [6] U. Krause, Fires in Silos: Hazards, Prevention, and Fire Fighting, Wiley-VCH, Weinheim, Germany, 2009. [7] D. Wu, X. Huang, F. Norman, F. Verplaetsen, J. Berghmans, E. Van den Bulck, Fuel 160 (2015) 245–254. [8] J.N. Carras, B.C. Young, Prog. Energy Combust. Sci. 20 (1994) 1–15.


JID: PROCI

ARTICLE IN PRESS D. Wu et al. / Proceedings of the Combustion Institute 000 (2018) 1–11

[9] H. Wang, B.Z. Dlugogorski, E.M. Kennedy, Prog. Energy Combust. Sci. 29 (2003) 487–513. [10] P.C. Bowes, P.H. Thomas, Combust. Flame 10 (1966) 221–230. [11] M. Schmidt, C. Lohrer, U. Krause, J. Loss Prev. Process Ind. 16 (2003) 141–147. [12] K.A. Joshi, V. Raghavan, A.S. Rangwala, Proc. Combust. Inst. 34 (2013) 2471–2478. [13] C. Lohrer, M. Schmidt, U. Krause, J. Loss Prev. Process Ind. 18 (2005) 167–177. [14] F. Restuccia, N. Ptak, G. Rein, Combust. Flame 176 (2017) 213–219. [15] D.A. Frank-Kamenetskii, Diffusion and Heat Transfer in Chemical Kinetics, Plenum Press, New York, London, 1969. [16] CEN, European Committee for Standardization, CEN, Brussels, 2007. [17] J.G. Torrent, Á.R. Gómezb, E.Q. Aragón, C.G. Olmedo, L.M. Pejic, J. Hazard. Mater. 213–214 (2012) 230–235. [18] G. Rein, et al., SFPE Handbook of Fire Protection Engineering, Springer, New York, 2016, pp. 581–603.

[m;June 21, 2018;18:21]

11

[19] X. Huang, G. Rein, H. Chen, Proc. Combust. Inst. 35 (2015) 2673–2681. [20] Z. Song, H. Zhu, B. Tan, H. Wang, X. Qin, Fire Saf. J. 69 (2014) 99–110. [21] A.H. Mahmoudi, F. Hoffmann, M. Markovic, B. Peters, G. Brem, Combust. Flame 163 (2016) 358–369. [22] S. Wang, H. Chen, N. Liu, J. Hazard. Mater. 283 (2015) 536–543. [23] D. Wu, F. Norman, M. Schmidt, et al., Fuel 188 (2017) 500–510. [24] D. Wu, Self-Ignition Characteristics of Coal Dusts in Oxy-fuel Combustion Atmospheres Ph.D. thesis, KU Leuven, 2016. [25] C.F. Curtiss, R.B. Bird, Ind. Chem. Res. 38 (1999) 2515–2522. [26] T.L. Bergman, et al., Fundamentals of Heat and Mass Transfer, 7th Ed., John Wiley & Sons publishing, Danvers, 2011. [27] Transport properties calculator, http://navier.engr. colostate.edu/?dandy/code/code-2/.
