Statistical appraisal of intrinsic factors affecting spontaneous combustion of coal

Mining Science and Technology, 4 (1987) i55-165 Elsevier Science Publishers B.V., Amsterdam - Printed in The Netherlands

155

STATISTICAL APPRAISAL OF INTRINSIC FACTORS AFFECTING SPONTANEOUS COMBUSTION OF COAL R.N. Singh and S, Demirbilek Department of Mining Engineering, University of Nottingham, University Park, Nottingham, NG 7 2RD (United Kingdom) (Received March 4, 1986; accepted May 2, 1986)

ABSTRACT This study is concerned with the statistical prediction of the liability of coal to spontaneous combustion. The experimental data consisted of adiabatic oxidation test results on 47 different coals, together with the laboratory evaluation of intrinsic properties of individual coal samples. The measures of propensity of coal to spontaneous combustion was based on the initial rate of heating and total temperature rise in an adiabatic oxidation experiment. These parameters were statistically correlated with various intrinsic properties of coal to isolate the most important intrinsic factors affecting the spontaneous heating potential of coal. A multiple re-

gression analysis between initial rate of heating and total temperature rise and thirteen independent variables have generated a set of equations to predict the liability of coal to spontaneous combustion. It is shown that the predictive equations derived by subdividing the data set according to rank classification can permit accurate prediction of temperature rise, thus evaluating the liability of coal to self-heating. The contribution made by various intrinsic factors to the self-heating potential of coal have also been evaluated by using isolated factor analysis techniques.

INTRODUCTION

been attributed to the intrinsic as well as the extrinsic properties of coal [1,2]. The purpose of this paper is to examine various intrinsic properties of coal that contribute to its self-oxidation potential. For the purpose of this investigation, the measure of liability of coal to spontaneous combustion is taken as the initial rate of heating and total temperature rise in an adiabatic oxidation test conducted under controlled laboratory conditions. The results of adiabatic oxidation tests

Coal is a complex, heterogeneous substance formed of organic as well as inorganic constituents, and has a non-uniform internal structure. To a certain extent, all coals are liable to spontaneous combustion when exposed to a favourable environment. However, various coals respond differently to self-heating when exposed to similar atmospheric conditions. In the past, this behaviour of coal has

156 for 47 different coal samples are given elsewhere [3]. As the intrinsic properties of coal, such as density, calorific value, moisture content, volatile matter, fixed carbon, ash, superficial moisture, total moisture, total iron, non-pyritic iron, total sulphur, pyritic sulphur, organic sulphur and sulphate sulphur contents of coal, are characteristics of individual coal, their influence on the self-heating potential of coal is investigated. It has also been observed that the rank of coal has a major influence on its susceptibility to spontaneous combustion [2]. Therefore, the results were classified according to their ranks and the influences of the intrinsic factors were examined for each coal rank. The present paper gives a statistical interpretation of coal analysis data and attempts to evaluate various intrinsic factors affecting the liability of coal to spontaneous combustion.

DATA A C Q U I S I T I O N ANALYSIS APPROACH

AND

FACTOR

Appraisal of the intrinsic factors contributing to spontaneous combustion was initially

based on the observed adiabatic oxidation test data and the intrinsic properties of corresponding coal samples determined in the laboratory. In order to facilitate data analysis, it is useful to classify the data into independent and dependent variables. The observed temperature values in the adiabatic oxidation test are functions of coal structure, coal-forming elements and specific coal properties. Thus, the factor analysis was accomplished by correlating the initial rate of heating and total temperature rise values referred as dependent variables with the intrinsic properties of coal as independent variables (Table 1). When the oxidation data of coal is statistically analysed, it is very difficult to isolate the effect of a single contributory factor to spontaneous combustion. The effect of various other contributory factors can mask the influence of the individual factor under examination. Bearing this in mind, an analytical approach for the appraisal of contributory factors to spontaneous combustion of coal, consisted of the following discrete steps: • Attempt to develop tentative relationships between observed oxidation potential and individual intrinsic properties of coal by

TABLE 1 Independent variables used in statistical analysis Independent variables Moisture (a.d. %) Vol. matter (a.d. %) Ash (a.d. %) Fixed carbon (a.d. %) Cal. value (J/g) Density (g/cm3) Total moisture (%) Total sulphur (%) Pyritic sulphur (%) Organic plus sulphate sulphur (%) Total Iron (ppm) Non-pyritic iron (ppm) Superficial moist. (%)

Symbols Range M VM A FC CV RD TM TS PS OSS TI NPI SM

Average values

0.60 10.37 17.79 43.00 1.99 28.96 38.69 69.83 20000 -35200 1.2001.725 1.35 17.02 0.1626.000 0.0183.979 0.0094.88 2.02 0.40 -

3.386 582.5 372.6 9.79

All coals

HVABC

HVBBC

MVBC

2.895 31.82 12.47 52.81 27494 1.362 5.568 0.925 0.194

2.664 35.89 9.058 52.40 30080 1.302 4.672 1.344 0.218

4.546 36.48 19.22 48.75 27174 1.336 8.733 1.263 0.309

1.291 21.56 19.18 57.97 24719 1.463 3.039 0.412 0.099

0.510 51.50 18.72 2.75

0.904 41.29 11.15 2.07

0.722 49.38 12.35 4.32

0.172 70.51 56.52 1.77

157 means of statistical analysis of a data base. • Evaluation of the combined effect of the most dominant intrinsic properties of coal on its oxidation potential for predictive purposes by means of multiple regression analysis. • Isolation of the most important intrinsic factors contributing to the spontaneous combustion of coal (Isolated Factor Analysis). The general data base comprised the oxidation results and intrinsic properties of 47 samples which were grouped according to rank so as to highlight the influence of major contributory factors in the following manner: (a) Treating the entire data base as a whole (regardless of the rank of coal); (b) Classifying coal in the following groups: (1) in high volatile A bituminous coals (H.V.A.B.C.); (2) in high volatile B bituminous coals (H.V.B.B.C.); (3) in medium volatile bituminous coals (M.V.B.C.).

STATISTICAL ANALYSIS OF DATA BASE As a preliminary step in the factor analysis, the data were analyzed using standard statistical routines to determine if any of the individual parameters were linearly related to the oxidation potential of coal [4]. In order to analyse the data for trends and relationships, it was necessary to classify the data into groups and variables as dependent and independent variables. Scatter plots of d e p e n d e n t / i n d e p e n d e n t variable pairs were drawn for each data group (coal--47 samples - - , H.V.A.B.C., H.V.B.B.C. and M.V.B.C.) and, in all cases, linear relationships together with correlations were sought. The correlations between dependent and independent variables are shown in Table 2. The statistical results did not, however, demonstrate any absolute linear relationship

between dependent and independent variables, even though very high correlation coefficients were obtained in some cases. The analysis of variable pairs (moisture/initial rate of heating; moisture/total temperature rise; fixed carbon/initial rate of heating; fixed carbon/total temperature rise; density/initial rate of heating; density/total temperature rise; calorific value/initial rate of heating, and calorific v a l u e / t o t a l temperature rise), in four data sets, indicated unanimous trends, i.e. the oxidation trend is decreasing with increasing fixed carbon and calorific value, and increasing with increasing moisture and relative density. Logically, different-rank coals will behave differently during exposure to similar atmospheric conditions due to differences in their physical and chemical properties. Classifying the results according to their rank decreases the extend of change in physical and chemical properties of samples. For example, the porosity and aromaticity of samples will fall between two closer limits than in the case of unclassified data, and the variation of calorific value, moisture content, etc., will also diminish, thus enabling more reliable conclusions to be drawn about the oxidation behaviour of certain rank coals. The higher correlation coefficients obtained fom the data sets of rank groups (Table 2) suggests that this is the best way to prevent the overriding effect of different rank groups on analysis of the general oxidation trends, and so obtain a result which is purely an indication potential of certain rank coals. It has also been thought that sulphur and iron content of coal might have a functional relationship rather than a linear relationship on the oxidation potential of coal. To elucidate this option, transformations were made and regression analysis was carried out on a linear/log basis. For the majority of cases, the correlations were higher on linear/log basis rather than linear/linear basis (Table 2).

158 TABLE 2 Correlation coefficients between independent and dependent variables (obtained from linear regression); values in brackets obtained by using In values of independent variables Independent variables

Moisture (a.d.) Vol. matter (a.d.) Ash (a.d.) Fixed carbon (a.d.) Cal. value Density Total moisture (a.r.) Total sulphur Pyritic sulphur Organic and sulphate sulphur Total iron Non-pyritic iron Superficial moisture

Correlation coefficients Initial rate of heating

Total temperature rise

All coals

H.V.A.B.C.

H.V.B.B.C.

M.V.B.C.

All coals

H.V.A.B.C.

H.V.B.B.C.

0.095 0.032 0.173 0.282 0.063 0.427 0.173 0.006 (0.044) 0.054 (0.181)

0.311 0.356 0.038 0.155 0.179 0.063 0.238 0.145 (0.148) 0.223 (0.260)

0.523 0.190 0.170 0.114 0.011 0.207 0.764 0.063 (0.054) 0.089 (0.030)

0.663 0.100 0.467 0.575 0.094 0.705 0.767 0.031 (0.138) 0.646 (0.569)

0.105 0.114 0.221 0.167 0.134 0.523 0.173 0.070 (0.031) 0.122 (0.236)

0.337 0.250 0.155 0.206 0.293 0.248 0.200 0.583 (0.472) 0.726 (0.591)

0.422 0.259 0.258 0.096 0.031 0.279 0.582 0.044 (0.077) 0.114 (0.054)

0.857 0.190 0.502 0.506 0.173 0.739 0.844 0.005 (0.070) 0.642 (0.629)

0.063 (0.387) 0.275 (0.333) 0.417 (0.274) 0.212

0.089 (0.031) 0.200 (0.345) 0.158 (0.109) 0.130

0.433 (0.155) 0.178 (0.100) 0.118 (0.308) 0.710

0.863 (0.819) 0.816 (0.796) 0.795 (0.785) 0.659

0.070 (0.333) 0.300 (0.374) 0.368 (0.314) 0.202

0.031 (0.054) 0.707 (0.640) 0.031 (0.234) 0.031

0.436 (0.245) 0.232 (0.150) 0.200 (0.317) 0.582

0.781 (0.635) 0.680 (0.779) 0.642 (0.745) 0.638

PREDICTIVE FORMULAE FROM MULTIPLE REGRESSION ANALYSIS

Predictive equations were derived by using thirteen independent variables, as listed in Table 1, but none of the derived equations is based on all thirteen variables. During multiple regression analysis some of the variables were highly correlated with other independent variables and consequently do not appear in the predictive equations. Several multiple regression calculations were carried out to find the best equation, which gives the highest correlation coefficient and lowest standard error of estimate, for four data sets grouped as All Coals, H.V.A.B.C., H.V.B.B.C. and M.V.B.C. Fi-

M.V.B.C.

nally, eqns. (1) and (2) in Table 3 provide the best prediction of initial rate of heating (I.R.H.) and total temperature rise (T.T.R.) for unclassified coals (on a 47-sample basis) according to their rank. Variables FC and S M had been omitted automatically from the equations due to their high correlation with other predictor variables. Despite this fact, the correlation coefficients are quite good, the standard error of estimate values are high and the predictions are significantly unacceptable when compared with the observed temperature values (Fig. 1). Equations (3) and (4) in Table 3, derived for High Volatile A Bituminous Coals produced better results, while giving very good

159

TABLE 3 Predictive equations derived by multiple regression Equation

Correlation coefficient

Standard error estimate

0.736

0.730

T.T.R. b = _ 35.6 + 1.46 0 n ( T / ) - 0.268 l n ( N P I ) - 0.54 ln(TS) - 0.41 In ( P S ) - 0.193 l n ( O S S ) + 16.5( R D ) + 0.0004( C V) + 0,185(M) + 0.0112(VM) - 0.0144(A) + 0.173(TM)

0,731

2.117

I.R.H. = 1 7 . 8 + 1.5 l n ( T I ) - 0,211 l n ( N P I ) - 2 . 7 ln(TS) + 0.33 l n ( P S ) + 0.65 l n ( O S S ) - 0.4( R D ) - 0.0004(C V) - 0.29(M) - 0,136(VM) - 0.346( A ) + 0.463(TM)

0.95

0.289

T.T.R. = 23.5 + 0,473 l n ( N P I ) + 0.765 l n ( P S ) - 2.07 ln(OSS - 1 . 4 4 ( R D ) - 0.0003(C V) - 1 . 4 ( M ) - 0.135(VM) - 0,245(8iA) + 0.916(TM)

0,913

0.685

I.R.H. = - 1 8 0 . 0 - 3.13 ln(TI) + 0.977 l n ( N P I ) - 0.092 ln(TS) + 0.59(RD) - 0.0001(C V) + 2.24 l n ( P S ) + 0.27 l n ( O S S ) + 1.91(M) + 2.03(VM) + 1.92(A) + 1.86(FC) + 0.185(TM)

0.970

0.298

T.T.R. = - 17.0 + 0.432 l n ( N P I ) + 1.15

0.96

0.933

I.R.H. = - 4.38 + 4.55 ln(TI) - 4.36 l n ( N P I ) + 1.38 ln(TS) 6.75(RD) + 0.0001(C V) - 1.70 l n ( P S ) - 1.85 l n ( Q S S ) + 3.26(M) + 0.074(VM) + 0,017(A ) + 0.15(TM)

0.998

0.208

T.T.R. = 0,487 + 2.18 ln(TI) - 2.91 l n ( N P I ) + 2.36 ln(TS) - 1.91 l n ( P S ) - - 2.84 l n ( O S S ) - 16.8(RD) + 0.0001(C V) + 10.7(M) + 0.3(VM) - 0.1( A ) + 1.07(TM)

0.997

0.780

Equations derived by multiple regression

no.

(1) All Coals

(2) All Coals

(3) H.V.A.B.C.

(4)

H.V.A.B.C.

(5) H.V.B.B.C.

(6) H.V.B.B.C.

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ln(OSS)

-O.O004(CV)-O.509(M)+O.IO9(VM)

+

V)

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19.9(RD) 0.273(A)

+ 0.64(TM)

(7) M.V.B.C.

(8) M.V.B.C.

a I.R.H. = Initial Rate of Heating. b T.T.R. = Total Temperature Rise.

correlation coefficients, the standard error of estimate values had been reduced to acceptable limits. The equations derived for High Volatile B (eqns. (5) and (6)) and Medium Volatile Bituminous (eqns. (7) and (8)) Coals are even better than those derived for High Volatile A Bituminous Coals. Correlation plots of predicted initial rate of heating and total temperature rise values, using eqns. (3)-(8) against observed values, are shown in Figs. 2-4.

ISOLATION OF VARIOUS INTRINSIC FACTORS ON THE SPONTANEOUS COMBUSTION OF COAL A statistical routine from the multiple regression results was chosen to expand the investigation and evaluation of the effect of various coal properties on the self oxidation potential of coal. This was carried out by calculating the initial rate of heating and total temperature rise changes due to one intrinsic

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163 factor, while suppressing the effect of others by using the mean values of independent variables in Table 1 in eqns. (1)-(8) in Table 3. The use of this routine provided indications that variations in oxidation potential of coal due to one intrinsic factor, while others are considered as constants, could be explained to a great degree. Hence, this technique could improve the available information about the relative effects of individual intrinsic contributory factors to spontaneous combustion of coal. The intrinsic factors which had been omitted from eqns. (1)-(8) due to their high correlation with each other were also automatically omitted from this analysis. (i) Effect of main constituents on oxidation potential of coal Moisture: Figure 5 shows the effect of the moisture content (a.d.) of coal on the oxidisability of coal regardless of rank. In general, increasing moisture content will cause an increase of the predicted values of initial rate of heating and temperature rise. On the other hand, the evaluation according to rank classes indicated either positive or negative effect on oxidation. Volatile matter: The oxidation capacity of coal was found to decrease with increasing volatile matter content for High Volatile A Bituminous Coals, and increase with increasing volatile matter content for the other data groups. Thus, it may be concluded that the increasing volatile matter content will increase the oxidation capacity of coal in general (Fig. 6). Ash content: The increasing ash content affects the oxidation by causing relatively high initial rates of heating and low total temperature rise (Fig. 7). Hence, it could be said that coals with high ash content will show slightly high reactivity with air but low total temperature build-up capacity, while coals with low ash content seem to have low reactivity at the initial stages of the oxidation but are capable

of prolonging the oxidation reaction at the same rate for a longer period of time. When the effect of ash is considered on the classified coals according to rank, it is negative for High Volatile A Bituminous Coals and positive for Medium Volatile Bituminous Coals with highly fluctuating values. Fixed carbon: The effect of fixed carbon is not analysed since it was omitted from the equations because of its high correlation with other predictor variables. (ii) Influence of gross calorific value From the linear regression analysis, it could be concluded that coals with a gross calorific value over 30,000 J / g tend to possess relatively moderate reactivity with air. The reactivity of coal slightly decreases with increasing gross calorific value. The individual factor analysis indicated that, if the contribution of the other factors is ignored, the influence of calorific value is positive both for coal in general and for medium volatile bituminous coals, and negative in H.V.A.B.C. and H.V.B.B.C. (Fig. 8). It is believed that the positive effect on coals in general is due to the overriding effect of medium volatile bituminous data on the high volatile A and B bituminous data in the analysis. The negative indication for rank classes (high volatile A and B bituminous) agree with the conclusion of linear regression analysis. The positive influence of gross calorific value in the medium volatile bituminous coals is rather unexpected. This could be the correct indication due to changes in physical and chemical properties of coal of different ranks, since the calorific value is actually a complex function of the elemental composition. (iii) Influence of relative density Relative density is also a complex function of elemental composition and structural characteristics of coal. Relative densities of 47

164 coal samples vary between 1.200 g / c m 3 and 1.725 g / c m 3, and the majority of samples has a density value between 1.250 g / c m 3 and 1.350 g / c m 3. The correlations obtained by linear regression impose a relatively good linear relationship between relative density and oxidation potential of coal, and it could be said that this is the most relevant factor to the inherent spontaneous combustion liability of coal. The isolated factor analysis also agrees with the linear relationship (Fig. 9) but illustrates contradictory effects on the oxidation trend of coals of different ranks.

tent above 1.5% tend to have a pattern quite parallel to x-axis. This leads to a conclusion that there is no linear relationship between the total sulphur content and oxidation potential of coal. The results obtained from the isolated factor analysis of all data groups ensured that this was the case and indicated a positive or negative exponential relationship (Fig. 11). Apart from this, the important range of total sulphur content could be pinpointed as 3.0%. Since further increase in sulphur content does not affect the oxidation potential of coal significantly.

(iv) Influence of total moisture The total moisture content of coal is strongly dependent on the porosity and moisture absorption capacity of coal. The original bed moisture content of coal will drop to balance itself with ambient humidity conditions, and somehow motivate the oxidation of coal [1]. The coal samples with a total moisture content of 5-6% displayed variable responses to the oxidation tests and generated highly different temperature/time characteristics. Outside the 5-6% total-moisture-content range, the fall of points in scatter plots shows an increase in the initial rate of heating and total temperature rise with the increase of total moisture content. The results obtained by isolated influence analysis has shown a unanimous agreement that the oxidation potential of coal increases with increasing total moisture content, and the relationship is linear (Fig. 10), illustrating the importance of original bed moisture content of coal.

(v) Influence of sulphur (a) Total sulphur The scatter plots of temperature versus total sulphur had shown that the coals with total sulphur content up to 1.5% could have an oxidation potential which is almost unpredictable although the coals with sulphur con-

(b) Influence of pyritic sulphur More than half of the samples have a pyritic sulphur content below 0.4%. Here again, scatter plots indicate that the corresponding initial rate of heating and total temperature rise values display a wide variation. They drop drastically while pyritic sulphur content approaches to 1.5% and then increase slightly with the increasing pyritic sulphur content above 1.5%. Linear regression and isolated factor analysis results are quite similar to those obtained from the total sulphur content data. Therefore, an analogous conclusion can be drawn about the relationship between pyritic sulphur content and oxidation potential of coal.

(c) Influence of organic and sulphate sulphur Scatter plots of organic and sulphate sulphur versus temperature measurements gave a good distribution of points; however, no correlation or linear relationship was established. The results of isolated influence analysis are similar to those of total and pyritic sulphur results. An exponential influence, either positive or negative, between the oxidation potential and the organic and sulphate sulphur content of coal exists and seems significant when organic and sulphate sulphur values are lower than 1.8%.

165

(vi) Influence of iron content The scatter plots of total-iron and nonpyritic-iron content of coal versus temperature measurements do not suggest that there is a linear relationship even though the correlation coefficients are higher than those of other intrinsic factors. Isolated factor analysis illustrated that the relationship between either total iron or non-pyritic iron and the oxidation potential of coal is exponential and significant when iron content values are lower than 150 p p m (Fig. 12).

CONCLUSIONS The linear regression analysis had shown that the use of single variable equations for the prediction of oxidation potential of coal is totally out of the question. The lack of correlation even with the classified data groups cannot enable isolation of the most important intrinsic properties affecting oxidation of coal. This suggests that, during the oxidation process, these intrinsic factors have an overruling effect on each other to some extent, and this interdependence of intrinsic factors determines the liability of coal to oxidation. This in turn means that the most favourable conditions for oxidation to take place depend upon the interrelation of intrinsic factors. The multiple regression of initial rate of heating and total temperature rise on thirteen independent variables have generated satisfactorily accurate predictive equations as indicated by the correlations and standard error of estimates. Only the general formulae derived from unclassified general data gave comparatively low correlations, together with a higher standard error of estimates, higher than the repeatability range of the oxidation apparatus. These equations are not recommended as predictive formulae. The formulae derived for various rank classes produced very good correlations, with a far lower standard

error of estimates, within the limits of repeatability of the oxidation apparatus. Consequently, in order to estimate the oxidation potential of coal by predicting the initial rate of heating and total temperature rise, the use of formulae derived for the rank classes will give considerably more accurate predictions. It should be emphasised that further refinement of these formulae, by enlarging the data base, is necessary because the number of observations has a great influence on the statistical analysis. The isolated factor analysis is based on the equations generated by multiple regression. The contribution of intrinsic factors to the liability of coals to self-heat can generally be evaluated more clearly by these models than by linear regression. The analysis results may only be used indirectly, as a useful device to support the previous conclusions reached by various researchers upon the role of intrinsic factors in the oxidation process of coal.

ACKNOWLEDGEMENTS The authors are indebted to Professor T. Atkinson, Head of Department of Mining Engineering, Nottingham University for his support and encouragement of the research project.

REFERENCES 1 M. Guney, An Adiabatic Calorimetric Study of the Influence of Moisture on the Spontaneous Heating of Coal and Gaseous Products. Ph.D. Thesis, Mining Eng. Dept., Univ. of Nottingham, U.K., 1969, 352 PP. 2 D. Humphreys, D. Rowlands and J.F. Cudmise, Spontaneous combustion of some Queensland Coals. In: Proc. Ignitions, Explosions and Fires in Coal Mines Symp., Australian IMM, Illawara Branch, May 1981, pp: 5.1-5.18. 3 R.N. Singh, A practical system of classifying coal seems liable to spontaneous combustion, J. Coal Qual., 5(3) (July 1986): 108-112. 4 C. Chatfield, Statistics for Technology, 2nd ed., Wiley, New York, 1978, 359 pp.