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Studies of relationship between petrography and elemental analysis with grindability for Kentucky coals
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Fuel 87 (2008) 707–713 www.fuelfirst.com
Studies of relationship between petrography and elemental analysis with grindability for Kentucky coals E. Jorjani a
a,*
, James C. Hower b, S. Chehreh Chelgani a, Mohsen A. Shirazi c, Sh. Mesroghli a
Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Poonak, Hesarak, Tehran, Iran b Center for Applied Energy Research, University of Kentucky, 2540 Research Park Drive, Lexington, KY 40511, USA c Industrial Engineering Department, K.N. Toosi University of Technology, P.O.B. 16315-989 Tehran, Iran Received 28 January 2007; received in revised form 14 May 2007; accepted 24 May 2007 Available online 29 June 2007
Abstract The effects of macerals, ash, elemental analysis and moisture of wide range of Kentucky coal samples from calorific value of 23.65– 34.68 MJ/kg (10,170–14,910 (BTU/lb)) on Hardgrove Grindability Index (HGI) have been investigated by multivariable regression method. Two sets of input: (a) macerals, ash and moisture (b) macerals, elemental analysis and moisture, were used for the estimation of HGI. The least square mathematical method shows that increase of the TiO2 and Al2O3 contents in coal can decrease HGI. The higher Fe2O3 content in coal can result in higher HGI. With the increase of micrinite and exinite contents in coal, the HGI has been decreased and higher vitrinite content in coal results in higher HGI. The multivariable studies have shown that input set of macerals, elemental analysis and moisture in non-linear condition can be achieved an acceptable correlation, R = 90.38%, versus R = 87.34% for the input set of macerals, ash and moisture. It is predicted that elemental analysis of coal can be a better representative of mineral matters for the prediction of HGI than ash. 2007 Elsevier Ltd. All rights reserved. Keywords: Hardgrove grindability index; Coal petrography; Coal elemental analysis
1. Introduction The grindability of coal is an important practical and economical property with applications in coal mining, beneficiation and utilization, particularly for pulverized coal-fired utilities [1–5]. In general, coal grindability characteristics reflect the coal hardness, tenacity, and fracture which are influenced by coal rank, petrography, and the distribution and types of minerals [6]. Grinding properties are important in mining applications since lower-HGI (harder to grind) lithotypes will require a greater energy input [7–9]. Both coal rank and maceral composition have an influence on grinding properties. Previous studies have indi*
Corresponding author. Tel.: +98 912 1776737; fax: +98 21 44817194. E-mail address: [email protected] (E. Jorjani).
0016-2361/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2007.05.044
cated that maceral and microlithotypes influences are more important than the rank influences [10,11]. Coal grindability, usually measured by Hardgrove Grindability Index (HGI), is of great interest since it is used as a predictive tool to determine the performance capacity of industrial pulverizers in power station boilers. Although the HGI testing device is not costly, the measuring procedure to get a HGI value is time consuming. In addition, it is not a routine testing item in coal-fired power plants [12]. However, some researchers have carried out works on the prediction of HGI based on proximate analysis and petrography. Hower and Wild [13] examined 656 Kentucky coal samples to determine the relationship between proximate and ultimate analysis, petrography and vitrinite maximum reflectance with HGI for both eastern and western Kentucky. For eastern Kentucky, the subject of the
708
E. Jorjani et al. / Fuel 87 (2008) 707–713
investigations in this paper, it was found that HGI could be predicted as following equation [13]: HGI ¼ 37:41 10:22 ln ðliptiniteÞ þ 28:18Rmax þ S total
ð1Þ
The relationship between HGI and coal petrography was studied by Hower et al. [4], Hower and Wild [13], Hower [10], and Trimble and Hower [11]. Trimble and Hower evaluated the influence of macerals microlithotypes on HGI and on pulverizer performance in different reflectance range [11]. Recently, Li et al. [14] discussed regression and neural network analyses using 67 coals of a wide rank range of coal quality for the prediction of the Hardgrove grindability index (HGI) on the basis of the proximate analysis [14]. A problem with their analysis was the use of a rank range which spanned the reversal of HGI values in the medium volatile bituminous rank range. The aim of the present work is the assessment of properties of 548 Kentucky coals with reference to the HGI and possible variations with respect to moisture, elemental analysis, and petrography of coal using the SPSS software package. This work is an attempt to solve the following important questions: (a) Is there a relationship between coal macerals, ash and moisture with HGI for a wide range of Kentucky coals? (b) Is it possible to improvement the correlation of predicted formula by replacing of coal ash with coal elemental analysis as a representative of coal mineral matter? Evaluation of coal macerals also elemental analysis effects of wide range of coal samples from calorific value of 23.65–34.68 MJ/kg (10,170–14,910 BTU/lb), on as determined basis, on HGI are evidence of difference between this work and previously published works. On the other hand, there are have not been publications on the use of elemental analysis to predict the HGI of coals.
cients of moisture and ash with HGI were 0.45 and 0.31, respectively. Sengupta, using a second-order regression equation (correlation coefficient of 0.93), found that the correlation between the HGI and the proximate analysis should be non-linear [15]. Relation of HGI with moisture , a measure of coal rank for lower-rank coals, and ash for the Kentucky samples with moisture from 0.93% to 12.30% (average of 3.90%) and ash from 0.73% to 19.95% (average of 8.11%), on as determined basis, are shown in Fig. 1. The following linear equation resulted from the regression of HGI versus moisture and ash: R2 ¼ 0:034
HGI ¼ 42:978 þ 0:201M þ 0:201A
ð2Þ
where M and A denote the percentages of moisture and ash, respectively. The insignificant results show that coal moisture and ash alone cannot be used to predict HGI. 3.1.2. Elemental analysis Ural and Akyildiz [6] studied the effects of mineral matter content on HGI for some low-rank Turkish coals [6]. They found that water- and acid-soluble mineral matter content has a positive contribution to the grindability of the low-rank coals. High ash and water- and acid-soluble content samples present higher HGI values, whereas, high ash and low water- and acid-soluble content samples have lower-HGI values. They did not evaluate the effects of elemental analysis of coal as a representative of mineral matter on HGI.
60 55
HGI
50
2. Experimental data
45
A mathematical model requires a comprehensive database to cover a wide variety of coal types. Such a model will be capable for predicting of HGI with a high degree of accuracy. Data used to test the proposed approach are from studies conducted at the University of Kentucky Center for Applied Energy Research. A total of 595 sets of data were used.
40
3. Results and discussion
55
35 30 0.00
2.50
5.00
7.50
10.00
12.50
Moist 60
3.1.1. Moisture and ash Vuthaluru et al. [16] studied the effects of moisture and coal blending on HGI for Collie coal of Western Australia, finding a significant effect of moisture content on HGI [16]. Li et al. [14] and Sengupta [15] examined the relation between proximate analysis with HGI using a second-order regression equation and found that the correlation coeffi-
HGI
50
3.1. Relation of HGI and individual constituents
45 40 35 30 0.00
5.00
10.00
15.00
20.00
Ash Fig. 1. Relationship between moisture and ash with HGI.
E. Jorjani et al. / Fuel 87 (2008) 707–713
lowing equation resulted between HGI and elemental analysis:
Table 1 The range of elemental analysis of coal samples (as determined) Element
Min
Max
Mean
Std. deviation
MgO Na2O Fe2O3 TiO2 SiO2 K2O Al2O3 SO3
0.00 0.00 0.50 0.20 9.20 0.10 0.10 0.00
3.68 3.90 79.47 5.40 73.60 6.20 43.40 26.3
0.94 0.46 17.25 1.31 43.92 1.88 25.97 3.17
0.52 0.50 14.08 0.65 10.70 0.97 7.31 3.63
HGI ¼ 22:955 þ 3:670Na2 O þ 0:303Fe2 O3 þ 0:002Al2 O3 2:801TiO2 3:359MgO þ 0:737SO3 þ 1:614K2 O þ 0:386SiO2
60
55
55
55
50
50
50
HGI
60
HGI
HGI
60
45
45
45
40
40
40
35
35
35
30
30
30
0.0
1.0
2.0
3.0
4.0
5.0
6.0
0.0
20.0
40.0
60.0
80.0
0.0
55
55
55
50
50
50
45
HGI
60
HGI
60
45 40
40
35
35
35
30
30
30
2.0
3.0
4.0
20.00
60
60
55
55
50
50
45
45
40
40
35
35
30
30 0.0
5.0
10.0 15.0 20.0 25.0 30.0
SO3
40.00
Fe2O3
K 2O
HGI
HGI
0.00
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
Na2O
30.0 40.0
50.0
45
40
1.0
20.0
Al2O3
60
0.0
10.0
SiO2
TiO2
R2 ¼ 0:42 ð3Þ
The results show that there is no acceptable correlation coefficient between coal elemental analysis and HGI. Examinations of the graphs in Fig. 2 demonstrate that certain elements have no relationship to HGI. For example, Na2O is not present in large concentrations in Kentucky coals and is not a significant contributor to the correlation. SiO2, Al2O3, and, to a lesser extent, K2O are associated
The ranges of elemental analysis components: MgO, Fe2O3, SO3, Al2O3, SiO2, TiO2, Na2O and K2O for the Kentucky samples are shown in Table 1. Relations of HGI with elemental analysis are shown in Fig. 2. The fol-
HGI
709
0.00
1.00
2.00
3.00
4.00
MgO
Fig. 2. Relationship between Fe2O3, MgO, TiO2, K2O, SO3, SiO2, Na2O and Al2O3 with HGI.
60.00
80.00
710
E. Jorjani et al. / Fuel 87 (2008) 707–713
with the clay content of the coals and, logically, have an impact on the mechanical properties as a proxy for the clay content.
some promise as a predictor, but resinite and macrinite are present in such low quantities, their inclusion in the equation contributes little if anything to the overall regression.
3.1.3. Coal petrography The ranges of the macerals of micrinite (Mic), exinite (Ex), macrinite (Mac), resinite (Res) and vitrinite (Vit) of the samples are shown in Table 2. Relations of HGI with individual macerals are shown in Fig. 3. The following equation resulted from this figure:
3.2. Correlation of the HGI with petrography, ash and moisture By a least square mathematical method, the correlation coefficients of moisture, ash, micrinite, macrinite, resinite, exinite, and vitrinite with HGI value were determined to be +0.103, +0.163, 0.599, +0.038, 0.433, 0.747 and +0.653, respectively. The results are shown that higher micrinite and exinite contents in coal can result lower HGI and higher vitrinite content in coal results higher HGI. No other parameters were significant. A final equation between the various parameters and HGI can be shown as follows:
HGI ¼ 59:401 1:075Ex þ 3:470Mac 0:779Mic R2 ¼ 0:68
0:989Res 0:064Vit
ð4Þ
It is observed that there is no acceptable correlation coefficient between coal macerals and HGI in multivariable regression (Eq. (4)). The best relationships are with vitrinite and exinite, known from previous studies. Micrinite shows
HGI ¼ 55:365 0:541M þ 0:072A 1:004Ex þ 3:818Mac 0:792Mic 0:888Res R2 ¼ 0:72
Table 2 The ranges of macerals of coal samples (mineral matter free)
ð5Þ
Min
Max
Mean
Std. deviation
Vitrinite (Vit) Resinite (Res) Exinite (Ex) Macrinite (Mac) Micrinite (Mic)
22.2 0.00 0.60 0.00 0.00
95.0 4.70 35.0 8.00 11.3
77.3 0.63 6.30 0.20 2.90
13.0 0.57 4.00 0.49 2.20
HGI ¼ 58:613 1:463M þ 0:459A 1:722Ex 1:114Mic þ 4:832Mac 0:649Res þ 0:073M 2 0:023A2 þ 0:041Ex2 þ 0:047Mic2 þ 0:007Res2 0:575Mac2 R2 ¼ 0:76
60
55
55
55
50
50
50
45
HGI
60
HGI
60
45 40
40
35
35
35
30
30
30
2.0
4.0
6.0
8.0
10.0 12.0
0.0
1.0
2.0
3.0
Mic
Res
60
60
55
55
50
50
HGI
0.0
45
40
35
35
30
30
2
4
Mac
6
8
5.0
0.0
10.0
20.0
Ex
45
40
0
4.0
20.0
40.0
60.0
Vit
Fig. 3. Relationship between coal petrography and HGI.
ð6Þ
45
40
HGI
HGI
Macerals
80.0
100.0
30.0
40.0
E. Jorjani et al. / Fuel 87 (2008) 707–713
60.00000
711
50
HGI=12.741+0.719 Predicted Value R2=71.9%
Mean = -4.8956E- 8 Std. Dev. = 2.87561 N = 548
Frequency
Predicted Value
40
50.00000
40.00000
30
20
10
30.00000 0
30
35
40
45
50
55
-10.00
60
-5.00
0.00
5.00
10.00
HGI Difference
HGI Fig. 4. Linear regression estimated HGI (Eq. (5)) versus actual determined HGI.
Fig. 6. Distribution of difference between actual HGI and estimated (Eq. (6)).
HGI ¼ 59:963 0:69M 0:93Ex þ 3:085Mac 0:814Mic 60.00
0:823Res 2:266MgO þ 1:889Na2 O
HGI=10.753+0.763 Predicted Value R2=76.3%
þ 0:053SiO2 R2 ¼ 0:797 HGI ¼ 57:752 1:632M 1:469Ex 1:061Mic
ð7Þ
þ 3:967Mac þ 0:22Vit 1:042Res 2:241MgO
50.00
0:334Al2 O3 þ 2:470Na2 O 0:033SiO2 45.00
þ 0:068Fe2 O3 þ 0:081M 2 þ 0:033Ex2
40.00
þ 0:037Mic2 þ 0:168Res2 0:001Vit2
35.00
0:44Mac2 þ 0:025MgO2 0:405Na2 O2
30.00
þ 0:001SiO22 0:001Fe2 O23 30
35
40
45
50
55
þ 0:004Al2 O23
60
HGI Fig. 5. Non-linear regression estimated HGI (Eq. (6)) versus actual determined HGI.
Linear and non-linear regression estimated HGI (Eqs. (5) and (6)) versus actual determined HGI are shown in Figs. 4 and 5, respectively. The distribution of difference between HGI predicted from Eq. (6) and actual determined amounts of HGI is shown in Fig. 6. 3.3. Correlation of the HGI with petrography, elemental analysis and moisture By a least square mathematical method, the correlation coefficients of MgO, Fe2O3, SO3, SiO2, K2O, Al2O3, TiO2, and Na2O with HGI are 0.136, +0.378, +0.113, 0.216, +0.111, 0.450, 0.436 and +0.133, respectively. The results show that increase of the TiO2 and Al2O3 contents in coal can decrease HGI. The higher Fe2O3 contain in coal can result in higher HGI. No other elemental analysis parameters were significant. A final equation between the various parameters and HGI can be shown as follows:
R2 ¼ 0:817
ð8Þ
Linear and non-linear regression estimated HGI (Eqs. (7) and (8)) versus actual determined HGI are shown in Figs. 7 and 8, respectively. The distribution of difference between HGI calculated from Eq. (8) and actual determined amounts of HGI is shown in Fig. 9.
60.00000
Predicted Value
Predicted Value
55.00
HGI=9.228+0.797 Predicted Value R2=79.7%
50.00000
40.00000
30.00000
30
35
40
45
50
55
60
HGI Fig. 7. Linear regression estimated HGI (Eq. (7)) versus actual determined HGI.
712
E. Jorjani et al. / Fuel 87 (2008) 707–713
Predicted Value
60.00
the databases of the environmental and quality control of coals on electricity generation plant; otherwise with a minimal effort in sample preparation and in a short measuring time with high accuracy and precision systems, XRF, in comparison to the direct determination of HGI.
HGI=8.293+0.817 Predicted Value R2=81.7%
50.00
40.00
5. Conclusions 30.00
30
35
40
45
50
55
60
HGI Fig. 8. Non-linear regression estimated HGI (Eq. (8)) versus actual determined HGI.
50
Mean = -4.9878E-11 Std.Dev. = 2.52528 N = 548
Frequency
40
30
20
10
0 -6.00 -4.00 -2.00
0.00
2.00
4.00
6.00
8.00
HGI Difference Fig. 9. Distribution of difference between actual HGI and estimated (Eq. (8)).
4. Technical considerations According to Eqs. (5) and (6), which present the relations of HGI with coal petrography, ash and moisture, the correlation coefficients of linear and non-linear regression estimated HGI and actual determined HGI (Figs. 4 and 5) are 0.72 and 0.76 (R: 0.85 and 0.87, respectively). With reference to the Eqs. (7) and (8), which present the relations of HGI with coal petrography, coal elemental analysis and moisture, the correlation coefficients of linear and non-linear regression estimated HGI and actual determined HGI (Figs. 7 and 8) are 0.80 and 0.82 (R: 0.89 and 0.90, respectively). According to the above significant results, it can be concluded that the estimate of HGI from the input set of coal macerals, coal elemental analysis and moisture is better than the input set of coal macerals, coal ash and moisture, with a higher correlation. The elemental analysis of coal: MgO, Al2O3, Na2O, SiO2, Fe2O3, which were used in the Eqs. (7) and (8) as a representative of coal mineral matter for the estimation of HGI, can be ready without any further attempt from
• The correlations between coal macerals and HGI in linear multivariate regression equations show that with the increase of micrinite and exinite in coal, the HGI decreased. Higher amounts of vitrinite in coal have positive effects on HGI. • The results show that higher Al2O3 and TiO2 contents in coal can result in lower HGI and higher Fe2O3 content in coal results higher HGI. No other elemental analysis parameters were significant. • Correlation of HGI with petrography, elemental analysis, and moisture, in comparison to correlation with petrography, ash, and moisture is more suitable for predicting of the HGI with a high precision and an acceptable limit of correlation. • As a work related to this one, Hower and Wild [13] studied relationship between proximate and ultimate analysis, petrography and vitrinite maximum reflectance with HGI for eastern Kentucky coals and found a correlation coefficient (R2) of 0.72 which liptinite, reflectance and sulfur were predictors. In this work better correlation coefficients of R2 = 0.80 and R2 = 0.82 were achieved in linear and non-linear equations in which coal macerals, moisture and elemental analysis were predictors. • It can be concluded that elemental analysis of coal can be used as a predictor than ash for the estimation of HGI. References [1] Barton WA, Condie DJ, Lynch LJ. Coal grindability: relationships with coal composition and structure. In: Proceedings of the sixth Australian coal science conference, 1994; Newcastle, 17–19 October, AIE, Australia, 55–64. [2] Bailey JG, Hodson A. The effect of coal grindability on pulverised fuel combustion. In: Proceedings of the sixth Australian coal science conference, 1994; Newcastle, 17–9 October, AIE, Australia, 40–7. [3] Conroy A. Impact of coal quality on grinding characteristics. Combustion News, Australian Combustion Technology Centre Company Publication, August 1994:1–4. [4] Hower JC, Graese AM, Klapheke JG. Influence of microlithotype composition on Hardgrove Grindability Index for selected Kentucky coals. Int J Coal Geol 1987;7:227–44. [5] Rubiera F, Arenillas A, Fuente E, Miles N, Pis JJ. Effect of the grinding behaviour of coal blends on coal utilisation for combustion. Powder Technol 1999;105:351–6. [6] Ural S, Akyildiz M. Studies of relationship between mineral matter and grinding properties for low-rank coal. Int J Coal Geol 2004; 60:81–4. [7] Mackowsky M-Th, Abramski C. Kohlenpetrographische Untersuchengsmethoden und ihre praktische Anwendung. Feuerungstechnik 1943;31(3):49–64.
E. Jorjani et al. / Fuel 87 (2008) 707–713 [8] Peters JT, Schapiro N, Gray RJ. Know your coal. T Am I Min Met Eng 1962;223:1–6. [9] Hower JC, Lineberry GT. The interface of coal lithology and coal cutting: study of breakage characteristics of selected Kentucky coals. J Coal Qual 1988;7:88–95. [10] Hower JC. Interrelationship of coal grinding properties and coal petrology. Miner Metall Proc 1998;15(3):1–16. [11] Trimble AS, Hower JC. Studies of relationship between coal petrology and grinding properties. Int J Coal Geol 2002;54:253–60. [12] Sun X. Combustion experiment technology and method for coal fired furnace. Beijing: China Electricity and Power Press; 2001.
713
[13] Hower JC, Wild GD. Relationships between Hardgrove Grindability Index and petrographic composition for high-volatile bituminous coals from Kentucky. J Coal Qual 1988;7:122–6. [14] Li P, Xiong Y, Yu D, Sun X. Prediction of grindability with multivariable regression and neural network in Chinese coal. Fuel 2005;84:2384–8. [15] Sengupta AN. An assessment of grindability index of coal. Fuel Process Technol 2002;76:1–10. [16] Vuthaluru HB, Brooke RJ, Zhang DK, Yan HM. Effect of moisture and coal blending on Hardgrove Grindability Index of Western Australian coal. Fuel Process Technol 2003;81:67–76.
Fuel 87 (2008) 707–713 www.fuelfirst.com
Studies of relationship between petrography and elemental analysis with grindability for Kentucky coals E. Jorjani a
a,*
, James C. Hower b, S. Chehreh Chelgani a, Mohsen A. Shirazi c, Sh. Mesroghli a
Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Poonak, Hesarak, Tehran, Iran b Center for Applied Energy Research, University of Kentucky, 2540 Research Park Drive, Lexington, KY 40511, USA c Industrial Engineering Department, K.N. Toosi University of Technology, P.O.B. 16315-989 Tehran, Iran Received 28 January 2007; received in revised form 14 May 2007; accepted 24 May 2007 Available online 29 June 2007
Abstract The effects of macerals, ash, elemental analysis and moisture of wide range of Kentucky coal samples from calorific value of 23.65– 34.68 MJ/kg (10,170–14,910 (BTU/lb)) on Hardgrove Grindability Index (HGI) have been investigated by multivariable regression method. Two sets of input: (a) macerals, ash and moisture (b) macerals, elemental analysis and moisture, were used for the estimation of HGI. The least square mathematical method shows that increase of the TiO2 and Al2O3 contents in coal can decrease HGI. The higher Fe2O3 content in coal can result in higher HGI. With the increase of micrinite and exinite contents in coal, the HGI has been decreased and higher vitrinite content in coal results in higher HGI. The multivariable studies have shown that input set of macerals, elemental analysis and moisture in non-linear condition can be achieved an acceptable correlation, R = 90.38%, versus R = 87.34% for the input set of macerals, ash and moisture. It is predicted that elemental analysis of coal can be a better representative of mineral matters for the prediction of HGI than ash. 2007 Elsevier Ltd. All rights reserved. Keywords: Hardgrove grindability index; Coal petrography; Coal elemental analysis
1. Introduction The grindability of coal is an important practical and economical property with applications in coal mining, beneficiation and utilization, particularly for pulverized coal-fired utilities [1–5]. In general, coal grindability characteristics reflect the coal hardness, tenacity, and fracture which are influenced by coal rank, petrography, and the distribution and types of minerals [6]. Grinding properties are important in mining applications since lower-HGI (harder to grind) lithotypes will require a greater energy input [7–9]. Both coal rank and maceral composition have an influence on grinding properties. Previous studies have indi*
Corresponding author. Tel.: +98 912 1776737; fax: +98 21 44817194. E-mail address: [email protected] (E. Jorjani).
0016-2361/$ - see front matter 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2007.05.044
cated that maceral and microlithotypes influences are more important than the rank influences [10,11]. Coal grindability, usually measured by Hardgrove Grindability Index (HGI), is of great interest since it is used as a predictive tool to determine the performance capacity of industrial pulverizers in power station boilers. Although the HGI testing device is not costly, the measuring procedure to get a HGI value is time consuming. In addition, it is not a routine testing item in coal-fired power plants [12]. However, some researchers have carried out works on the prediction of HGI based on proximate analysis and petrography. Hower and Wild [13] examined 656 Kentucky coal samples to determine the relationship between proximate and ultimate analysis, petrography and vitrinite maximum reflectance with HGI for both eastern and western Kentucky. For eastern Kentucky, the subject of the
708
E. Jorjani et al. / Fuel 87 (2008) 707–713
investigations in this paper, it was found that HGI could be predicted as following equation [13]: HGI ¼ 37:41 10:22 ln ðliptiniteÞ þ 28:18Rmax þ S total
ð1Þ
The relationship between HGI and coal petrography was studied by Hower et al. [4], Hower and Wild [13], Hower [10], and Trimble and Hower [11]. Trimble and Hower evaluated the influence of macerals microlithotypes on HGI and on pulverizer performance in different reflectance range [11]. Recently, Li et al. [14] discussed regression and neural network analyses using 67 coals of a wide rank range of coal quality for the prediction of the Hardgrove grindability index (HGI) on the basis of the proximate analysis [14]. A problem with their analysis was the use of a rank range which spanned the reversal of HGI values in the medium volatile bituminous rank range. The aim of the present work is the assessment of properties of 548 Kentucky coals with reference to the HGI and possible variations with respect to moisture, elemental analysis, and petrography of coal using the SPSS software package. This work is an attempt to solve the following important questions: (a) Is there a relationship between coal macerals, ash and moisture with HGI for a wide range of Kentucky coals? (b) Is it possible to improvement the correlation of predicted formula by replacing of coal ash with coal elemental analysis as a representative of coal mineral matter? Evaluation of coal macerals also elemental analysis effects of wide range of coal samples from calorific value of 23.65–34.68 MJ/kg (10,170–14,910 BTU/lb), on as determined basis, on HGI are evidence of difference between this work and previously published works. On the other hand, there are have not been publications on the use of elemental analysis to predict the HGI of coals.
cients of moisture and ash with HGI were 0.45 and 0.31, respectively. Sengupta, using a second-order regression equation (correlation coefficient of 0.93), found that the correlation between the HGI and the proximate analysis should be non-linear [15]. Relation of HGI with moisture , a measure of coal rank for lower-rank coals, and ash for the Kentucky samples with moisture from 0.93% to 12.30% (average of 3.90%) and ash from 0.73% to 19.95% (average of 8.11%), on as determined basis, are shown in Fig. 1. The following linear equation resulted from the regression of HGI versus moisture and ash: R2 ¼ 0:034
HGI ¼ 42:978 þ 0:201M þ 0:201A
ð2Þ
where M and A denote the percentages of moisture and ash, respectively. The insignificant results show that coal moisture and ash alone cannot be used to predict HGI. 3.1.2. Elemental analysis Ural and Akyildiz [6] studied the effects of mineral matter content on HGI for some low-rank Turkish coals [6]. They found that water- and acid-soluble mineral matter content has a positive contribution to the grindability of the low-rank coals. High ash and water- and acid-soluble content samples present higher HGI values, whereas, high ash and low water- and acid-soluble content samples have lower-HGI values. They did not evaluate the effects of elemental analysis of coal as a representative of mineral matter on HGI.
60 55
HGI
50
2. Experimental data
45
A mathematical model requires a comprehensive database to cover a wide variety of coal types. Such a model will be capable for predicting of HGI with a high degree of accuracy. Data used to test the proposed approach are from studies conducted at the University of Kentucky Center for Applied Energy Research. A total of 595 sets of data were used.
40
3. Results and discussion
55
35 30 0.00
2.50
5.00
7.50
10.00
12.50
Moist 60
3.1.1. Moisture and ash Vuthaluru et al. [16] studied the effects of moisture and coal blending on HGI for Collie coal of Western Australia, finding a significant effect of moisture content on HGI [16]. Li et al. [14] and Sengupta [15] examined the relation between proximate analysis with HGI using a second-order regression equation and found that the correlation coeffi-
HGI
50
3.1. Relation of HGI and individual constituents
45 40 35 30 0.00
5.00
10.00
15.00
20.00
Ash Fig. 1. Relationship between moisture and ash with HGI.
E. Jorjani et al. / Fuel 87 (2008) 707–713
lowing equation resulted between HGI and elemental analysis:
Table 1 The range of elemental analysis of coal samples (as determined) Element
Min
Max
Mean
Std. deviation
MgO Na2O Fe2O3 TiO2 SiO2 K2O Al2O3 SO3
0.00 0.00 0.50 0.20 9.20 0.10 0.10 0.00
3.68 3.90 79.47 5.40 73.60 6.20 43.40 26.3
0.94 0.46 17.25 1.31 43.92 1.88 25.97 3.17
0.52 0.50 14.08 0.65 10.70 0.97 7.31 3.63
HGI ¼ 22:955 þ 3:670Na2 O þ 0:303Fe2 O3 þ 0:002Al2 O3 2:801TiO2 3:359MgO þ 0:737SO3 þ 1:614K2 O þ 0:386SiO2
60
55
55
55
50
50
50
HGI
60
HGI
HGI
60
45
45
45
40
40
40
35
35
35
30
30
30
0.0
1.0
2.0
3.0
4.0
5.0
6.0
0.0
20.0
40.0
60.0
80.0
0.0
55
55
55
50
50
50
45
HGI
60
HGI
60
45 40
40
35
35
35
30
30
30
2.0
3.0
4.0
20.00
60
60
55
55
50
50
45
45
40
40
35
35
30
30 0.0
5.0
10.0 15.0 20.0 25.0 30.0
SO3
40.00
Fe2O3
K 2O
HGI
HGI
0.00
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0
Na2O
30.0 40.0
50.0
45
40
1.0
20.0
Al2O3
60
0.0
10.0
SiO2
TiO2
R2 ¼ 0:42 ð3Þ
The results show that there is no acceptable correlation coefficient between coal elemental analysis and HGI. Examinations of the graphs in Fig. 2 demonstrate that certain elements have no relationship to HGI. For example, Na2O is not present in large concentrations in Kentucky coals and is not a significant contributor to the correlation. SiO2, Al2O3, and, to a lesser extent, K2O are associated
The ranges of elemental analysis components: MgO, Fe2O3, SO3, Al2O3, SiO2, TiO2, Na2O and K2O for the Kentucky samples are shown in Table 1. Relations of HGI with elemental analysis are shown in Fig. 2. The fol-
HGI
709
0.00
1.00
2.00
3.00
4.00
MgO
Fig. 2. Relationship between Fe2O3, MgO, TiO2, K2O, SO3, SiO2, Na2O and Al2O3 with HGI.
60.00
80.00
710
E. Jorjani et al. / Fuel 87 (2008) 707–713
with the clay content of the coals and, logically, have an impact on the mechanical properties as a proxy for the clay content.
some promise as a predictor, but resinite and macrinite are present in such low quantities, their inclusion in the equation contributes little if anything to the overall regression.
3.1.3. Coal petrography The ranges of the macerals of micrinite (Mic), exinite (Ex), macrinite (Mac), resinite (Res) and vitrinite (Vit) of the samples are shown in Table 2. Relations of HGI with individual macerals are shown in Fig. 3. The following equation resulted from this figure:
3.2. Correlation of the HGI with petrography, ash and moisture By a least square mathematical method, the correlation coefficients of moisture, ash, micrinite, macrinite, resinite, exinite, and vitrinite with HGI value were determined to be +0.103, +0.163, 0.599, +0.038, 0.433, 0.747 and +0.653, respectively. The results are shown that higher micrinite and exinite contents in coal can result lower HGI and higher vitrinite content in coal results higher HGI. No other parameters were significant. A final equation between the various parameters and HGI can be shown as follows:
HGI ¼ 59:401 1:075Ex þ 3:470Mac 0:779Mic R2 ¼ 0:68
0:989Res 0:064Vit
ð4Þ
It is observed that there is no acceptable correlation coefficient between coal macerals and HGI in multivariable regression (Eq. (4)). The best relationships are with vitrinite and exinite, known from previous studies. Micrinite shows
HGI ¼ 55:365 0:541M þ 0:072A 1:004Ex þ 3:818Mac 0:792Mic 0:888Res R2 ¼ 0:72
Table 2 The ranges of macerals of coal samples (mineral matter free)
ð5Þ
Min
Max
Mean
Std. deviation
Vitrinite (Vit) Resinite (Res) Exinite (Ex) Macrinite (Mac) Micrinite (Mic)
22.2 0.00 0.60 0.00 0.00
95.0 4.70 35.0 8.00 11.3
77.3 0.63 6.30 0.20 2.90
13.0 0.57 4.00 0.49 2.20
HGI ¼ 58:613 1:463M þ 0:459A 1:722Ex 1:114Mic þ 4:832Mac 0:649Res þ 0:073M 2 0:023A2 þ 0:041Ex2 þ 0:047Mic2 þ 0:007Res2 0:575Mac2 R2 ¼ 0:76
60
55
55
55
50
50
50
45
HGI
60
HGI
60
45 40
40
35
35
35
30
30
30
2.0
4.0
6.0
8.0
10.0 12.0
0.0
1.0
2.0
3.0
Mic
Res
60
60
55
55
50
50
HGI
0.0
45
40
35
35
30
30
2
4
Mac
6
8
5.0
0.0
10.0
20.0
Ex
45
40
0
4.0
20.0
40.0
60.0
Vit
Fig. 3. Relationship between coal petrography and HGI.
ð6Þ
45
40
HGI
HGI
Macerals
80.0
100.0
30.0
40.0
E. Jorjani et al. / Fuel 87 (2008) 707–713
60.00000
711
50
HGI=12.741+0.719 Predicted Value R2=71.9%
Mean = -4.8956E- 8 Std. Dev. = 2.87561 N = 548
Frequency
Predicted Value
40
50.00000
40.00000
30
20
10
30.00000 0
30
35
40
45
50
55
-10.00
60
-5.00
0.00
5.00
10.00
HGI Difference
HGI Fig. 4. Linear regression estimated HGI (Eq. (5)) versus actual determined HGI.
Fig. 6. Distribution of difference between actual HGI and estimated (Eq. (6)).
HGI ¼ 59:963 0:69M 0:93Ex þ 3:085Mac 0:814Mic 60.00
0:823Res 2:266MgO þ 1:889Na2 O
HGI=10.753+0.763 Predicted Value R2=76.3%
þ 0:053SiO2 R2 ¼ 0:797 HGI ¼ 57:752 1:632M 1:469Ex 1:061Mic
ð7Þ
þ 3:967Mac þ 0:22Vit 1:042Res 2:241MgO
50.00
0:334Al2 O3 þ 2:470Na2 O 0:033SiO2 45.00
þ 0:068Fe2 O3 þ 0:081M 2 þ 0:033Ex2
40.00
þ 0:037Mic2 þ 0:168Res2 0:001Vit2
35.00
0:44Mac2 þ 0:025MgO2 0:405Na2 O2
30.00
þ 0:001SiO22 0:001Fe2 O23 30
35
40
45
50
55
þ 0:004Al2 O23
60
HGI Fig. 5. Non-linear regression estimated HGI (Eq. (6)) versus actual determined HGI.
Linear and non-linear regression estimated HGI (Eqs. (5) and (6)) versus actual determined HGI are shown in Figs. 4 and 5, respectively. The distribution of difference between HGI predicted from Eq. (6) and actual determined amounts of HGI is shown in Fig. 6. 3.3. Correlation of the HGI with petrography, elemental analysis and moisture By a least square mathematical method, the correlation coefficients of MgO, Fe2O3, SO3, SiO2, K2O, Al2O3, TiO2, and Na2O with HGI are 0.136, +0.378, +0.113, 0.216, +0.111, 0.450, 0.436 and +0.133, respectively. The results show that increase of the TiO2 and Al2O3 contents in coal can decrease HGI. The higher Fe2O3 contain in coal can result in higher HGI. No other elemental analysis parameters were significant. A final equation between the various parameters and HGI can be shown as follows:
R2 ¼ 0:817
ð8Þ
Linear and non-linear regression estimated HGI (Eqs. (7) and (8)) versus actual determined HGI are shown in Figs. 7 and 8, respectively. The distribution of difference between HGI calculated from Eq. (8) and actual determined amounts of HGI is shown in Fig. 9.
60.00000
Predicted Value
Predicted Value
55.00
HGI=9.228+0.797 Predicted Value R2=79.7%
50.00000
40.00000
30.00000
30
35
40
45
50
55
60
HGI Fig. 7. Linear regression estimated HGI (Eq. (7)) versus actual determined HGI.
712
E. Jorjani et al. / Fuel 87 (2008) 707–713
Predicted Value
60.00
the databases of the environmental and quality control of coals on electricity generation plant; otherwise with a minimal effort in sample preparation and in a short measuring time with high accuracy and precision systems, XRF, in comparison to the direct determination of HGI.
HGI=8.293+0.817 Predicted Value R2=81.7%
50.00
40.00
5. Conclusions 30.00
30
35
40
45
50
55
60
HGI Fig. 8. Non-linear regression estimated HGI (Eq. (8)) versus actual determined HGI.
50
Mean = -4.9878E-11 Std.Dev. = 2.52528 N = 548
Frequency
40
30
20
10
0 -6.00 -4.00 -2.00
0.00
2.00
4.00
6.00
8.00
HGI Difference Fig. 9. Distribution of difference between actual HGI and estimated (Eq. (8)).
4. Technical considerations According to Eqs. (5) and (6), which present the relations of HGI with coal petrography, ash and moisture, the correlation coefficients of linear and non-linear regression estimated HGI and actual determined HGI (Figs. 4 and 5) are 0.72 and 0.76 (R: 0.85 and 0.87, respectively). With reference to the Eqs. (7) and (8), which present the relations of HGI with coal petrography, coal elemental analysis and moisture, the correlation coefficients of linear and non-linear regression estimated HGI and actual determined HGI (Figs. 7 and 8) are 0.80 and 0.82 (R: 0.89 and 0.90, respectively). According to the above significant results, it can be concluded that the estimate of HGI from the input set of coal macerals, coal elemental analysis and moisture is better than the input set of coal macerals, coal ash and moisture, with a higher correlation. The elemental analysis of coal: MgO, Al2O3, Na2O, SiO2, Fe2O3, which were used in the Eqs. (7) and (8) as a representative of coal mineral matter for the estimation of HGI, can be ready without any further attempt from
• The correlations between coal macerals and HGI in linear multivariate regression equations show that with the increase of micrinite and exinite in coal, the HGI decreased. Higher amounts of vitrinite in coal have positive effects on HGI. • The results show that higher Al2O3 and TiO2 contents in coal can result in lower HGI and higher Fe2O3 content in coal results higher HGI. No other elemental analysis parameters were significant. • Correlation of HGI with petrography, elemental analysis, and moisture, in comparison to correlation with petrography, ash, and moisture is more suitable for predicting of the HGI with a high precision and an acceptable limit of correlation. • As a work related to this one, Hower and Wild [13] studied relationship between proximate and ultimate analysis, petrography and vitrinite maximum reflectance with HGI for eastern Kentucky coals and found a correlation coefficient (R2) of 0.72 which liptinite, reflectance and sulfur were predictors. In this work better correlation coefficients of R2 = 0.80 and R2 = 0.82 were achieved in linear and non-linear equations in which coal macerals, moisture and elemental analysis were predictors. • It can be concluded that elemental analysis of coal can be used as a predictor than ash for the estimation of HGI. References [1] Barton WA, Condie DJ, Lynch LJ. Coal grindability: relationships with coal composition and structure. In: Proceedings of the sixth Australian coal science conference, 1994; Newcastle, 17–19 October, AIE, Australia, 55–64. [2] Bailey JG, Hodson A. The effect of coal grindability on pulverised fuel combustion. In: Proceedings of the sixth Australian coal science conference, 1994; Newcastle, 17–9 October, AIE, Australia, 40–7. [3] Conroy A. Impact of coal quality on grinding characteristics. Combustion News, Australian Combustion Technology Centre Company Publication, August 1994:1–4. [4] Hower JC, Graese AM, Klapheke JG. Influence of microlithotype composition on Hardgrove Grindability Index for selected Kentucky coals. Int J Coal Geol 1987;7:227–44. [5] Rubiera F, Arenillas A, Fuente E, Miles N, Pis JJ. Effect of the grinding behaviour of coal blends on coal utilisation for combustion. Powder Technol 1999;105:351–6. [6] Ural S, Akyildiz M. Studies of relationship between mineral matter and grinding properties for low-rank coal. Int J Coal Geol 2004; 60:81–4. [7] Mackowsky M-Th, Abramski C. Kohlenpetrographische Untersuchengsmethoden und ihre praktische Anwendung. Feuerungstechnik 1943;31(3):49–64.
E. Jorjani et al. / Fuel 87 (2008) 707–713 [8] Peters JT, Schapiro N, Gray RJ. Know your coal. T Am I Min Met Eng 1962;223:1–6. [9] Hower JC, Lineberry GT. The interface of coal lithology and coal cutting: study of breakage characteristics of selected Kentucky coals. J Coal Qual 1988;7:88–95. [10] Hower JC. Interrelationship of coal grinding properties and coal petrology. Miner Metall Proc 1998;15(3):1–16. [11] Trimble AS, Hower JC. Studies of relationship between coal petrology and grinding properties. Int J Coal Geol 2002;54:253–60. [12] Sun X. Combustion experiment technology and method for coal fired furnace. Beijing: China Electricity and Power Press; 2001.
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[13] Hower JC, Wild GD. Relationships between Hardgrove Grindability Index and petrographic composition for high-volatile bituminous coals from Kentucky. J Coal Qual 1988;7:122–6. [14] Li P, Xiong Y, Yu D, Sun X. Prediction of grindability with multivariable regression and neural network in Chinese coal. Fuel 2005;84:2384–8. [15] Sengupta AN. An assessment of grindability index of coal. Fuel Process Technol 2002;76:1–10. [16] Vuthaluru HB, Brooke RJ, Zhang DK, Yan HM. Effect of moisture and coal blending on Hardgrove Grindability Index of Western Australian coal. Fuel Process Technol 2003;81:67–76.