Cost of underground coal mining in Illinois

IMNr

(^ick SuuOU

1

COST OF

UNDERGROUND

COAL MINING

IN ILLINOIS

November 1982

Illinois Mineral Notes 84

Illinois Department of Energy and Natural Resources

STATE GEOLOGICAL SURVEY DIVISION

Subhash Bhagwat
Philip Robare

Cover design: Craig Ronto

Editor: Ellen Stenzel

Bhagwat, Subhash

Cost of underground coal mining in Illinois/ Subhash Bhagwat
and Philip Robare. -- Champaign, IL : Illinois State Geological
Survey, November 1982.

14 p. ; 28 cm. - (Illinois-Geological Survey. Illinois mineral
notes ; 84).

1. Coal mines and mining-Costs. I. Title. II. Robare, Philip.
III. Series.

ILLINOIS STATE GEOLOGICAL SURVEY

Printed by authority of the State of Illinois: IMN 84/1982/1800.

3 3051 00005 9927

COST OF

UNDERGROUND

COAL MINING

IN ILLINOIS

Subhash Bhagwat
Philip Robare

CONTENTS

Introduction

The Model

Limiting Factors

Discussion of the Model Results

Concluding Observations

References

Appendix A

ILLINOIS KalCAL Appendix B

SURVE- Y

m 10 ib*a

ILLINOIS STATE GEOLOGICAL SURVEY
Robert E. Bergstrom, Chief

Natural Resources Building

615 East Peabody Drive ILLINOIS MINERAL NOTES 84

Champaign, I L 61820 November 1982

Cost of Underground Coal Mining in Illinois

Illinois State Geological Survey Illinois Mineral Notes 84

PaRge dumber 2: for Every 100 ton increase read Every 10 ton increase

COST OF

UNDERGROUND

COAL MINING

IN ILLINOIS

INTRODUCTION

Economic data on the coal industry is limited to annual
production, sales, and coal prices. Not much is published
about the cost of existing mine operations and about how
various factors correlate to cost per ton of clean coal.
The objective of this report is to use easily available data
and parameters to develop information on the cost of under-
ground mining in Illinois, and to use the information for
mine comparisons as well as for regression analysis of
the factors that significantly explain the variability in cost
per ton.

The approach used in this investigation is similar to
the engineering analysis approach used in the Electric
Power Research Insitute's (EPRI) cost model for under-
ground coal mines (1). This report, however, limits itself
to existing mines only and does not use the broader mine-
planning and financial -analysis approach emphasized in
the EPRI model. Other models have been developed for the
purpose of cost estimation in underground coal mines, most
notably by the U.S. Bureau of Mines (2), using the same
engineering-analysis approach. Basically, our model is not
new. The value of our model lies in its application to
existing mines. Illinois underground mines are suitable
for this kind of investigation because of their large average
production per year (more than 1 million tons per year
compared to the national average of less than 200,000
tons per year). Furthermore, there were only 30 under-
ground mines in 1980 and all could be included in the
investigation, which obviated the task of selecting represen-
tative mines. Thus, the conclusions drawn from their analy-
sis are statistically valid for the purpose of assessing costs
of future underground mines. Availability and reliability
of data do not pose significant problems in Illinois as all
data are taken from reports filed with the Illinois Depart-
ment of Mines and Minerals.

COST OF UNDERGROUND COAL MINING IN ILLINOIS

Technical data
collection/filing

Engineering analysis of the

existing underground

coal mines

I

Input of technical
data on mines

♦

Input of
economic data

Economic data
gathering/filing

1

Mine development
submodel

Mine operation and
maintenance submodel

Underground haulage
system submodel

Coal preparation
plant submodel

*

Total cost
per ton clean coal

Simple and multiple
regression analyses

♦

Graphic outputs

ISGS 1982

Interpretation of
results by user

Figure 1. Schematic of the model for calculation of cost of mining coal by underground methods.

THE MODEL

Engineering analysis (fig. 1) involves categorization of
mines by the type of mine opening, the mining technology
used, and the determination of mathematical cost functions
for each (Appendix A). Three types of mine openings
occur in Illinois— shaft, slope, and drift. In 1980, seven of a
total 30 mines had both a shaft and a slope. Five mines
employed longwall technology, whereas 25 worked con-
ventionally, using room -and -pillar technology.

Technical data include seam depth and thickness,
numbers and types of equipment, number of labor and
supervisory personnel, number of workdays per year and
shifts per day, size of 1980 annual production, types of
coal cleaning practiced, and mine age. The data are readily
available from publications of the Illinois Department of
Mines and Minerals (4) and the Keystone Coal Industry
Manual (5).

Economic data were collected from various sources
(1 ,2,3; and personal communications) and include costs of
equipment (1980 dollar base), energy, shaft sinking, slope
and drift construction, drift portal construction, labor,
overhead, and royalties; investments in preparation plants
(dollars per ton annual capacity); and the material consump-
tion ratio (percent of operating costs). No distinctions
were made between individual mines regarding the eco-
nomic data.

Mathematical relationships established in the engi-
neering analysis as well as the subsequent data collection
were used to calculate the total cost per ton clean coal
in four submodels on mine development, operation and
maintenance, haulage, and coal preparation.

Regression analyses, simple and multiple, were per-
formed with cost per ton clean coal as the dependent

ISGS/ILLINOIS MINERAL NOTES 84

variable and the following as independent variables: mine
development cost (dollars per ton annual capacity), depth,
seam thickness, mine age, annual production, labor pro-
ductivity (tons per worker-year), total investment in equip-
ment, and the level of coal cleaning. Simple regression
analyses were also performed to determine the relations
of some independent variables.

LIMITING FACTORS

The analysis is based on results computed from 27 of the
30 underground coal mines operating in Illinois in 1980.
Three mines were not included because of production
problems. No distinction was made between conventional
mines and those using longwall technology because of the
small number of longwall mines, all operated by the same
company under similar conditions of seam depth and
thickness. The total number of longwalls in Illinois mines
is too small to be investigated alone.

The areal expansion of a mine depends essentially
upon its age, annual production, and seam thickness.
Since the three factors vary considerably in Illinois, incor-
porating the variations in mine expanse into the model
would involve additional data collection but not signifi-
cantly add to the accuracy of the model. Therefore, a
uniform pattern of main entries, cross cuts, and section-
entry lengths was assumed for all mines. As a result, the
cost calculations of the submodels on haulage and main-
tenance must show some inconsistencies. However, the
overall effects of the inconsistencies are estimated to be
minimal because mine expanse is inversely related to seam
thickness, and the annual production of a mine is exponen-
tially related to the increase in the field radius.

For example: A mine with 500,000 tons per
year production from a 5 -foot seam expands
at a rate of 56 acres per year, while a mine pro-
ducing 3 million tons' per year from a 7 -foot
seam expands at a rate of 238 acres per year. In
the former case, the radius equals 300 yards as
compared with 600 yards for the larger mine.
Thus, although the production in the latter case
is 6 times larger than the former, the mean trans-
portation length is only 2 times the former length.

Geologic factors (roof and floor conditions, tectonic
disturbances) and operational factors (implementation of
health and safety regulations, differences in maintenance
man-hours between mines) have not been explicitly built

into the model. This simplification is not considered
significant, however, because productivity (tons per worker-
year) indirectly covers the omitted factors better than
individual coverage of all the factors could.

It has been assumed that mining equipment is replaced
after 10 years. This may not be true for some mines, thus
resulting in some errors in estimating costs. Also, prices
used are from the higher end of the price range for each
type of equipment, contributing somewhat to cost
overestimates. Moreover, a 100-percent debt financing
is assumed instead of the 50-percent rate used by other
models. Based on the volume of investments required,
we expect total debt financing in the future.

DISCUSSION OF THE MODEL RESULTS

Cost per Ton of Clean Coal

The cost of underground coal mines and coal cleaning
in Illinois ranged from $14.20 to $40.70 per ton of clean
coal (fig. 2). The overall weighted average cost per ton
of clean coal was $23.70. The estimated 1980 average
value of coal mined by underground methods in Illinois
was about $26.00 per ton.

The seven mines displaying costs less than $20.00 per
ton of clean coal averaged 2 million tons annual production
per mine, and a correspondingly high labor productivity
of 3,600 tons per person per year. In comparison, the
average of all Illinois underground coal mines for 1980
was 1.16 million tons production per mine and a labor
productivity of 2,775 tons per person per year. Three out
of five mines that did not clean their coal displayed costs
less than $20.00 per ton, while the remaining two mines
were close to the overall average costs of $23.70 per ton,
indicating the importance of having coal reserves with
low ash, low sulphur, and high Btu.

Of the seven mines with costs more than $30.00 per
ton clean coal, one was shut down in 1981. Three other
mines in this group had yet to reach their planned pro-
duction in 1980 as they were relatively new mines. In the
remaining three mines, a combination of thin seams, use of
conventional cutters instead of modern continuous miners,
and difficult roof conditions (observed by ISGS geologists),
resulted in high costs per ton.

Cost per ton in mines using longwall technology did not
differ significantly from the overall weighted average cost
of all Illinois underground mines indicating that the mines
may be successfully using the longwall technology in spite
of encountering some difficult geologic roof conditions.

COST OF UNDERGROUND COAL MINING IN ILLINOIS

6-

E 5

a) 4 -

.O

E

I 3

Figure 2

<20 20-25 25-30 30-35

Cost category ($/ton)

>35

ISGS 1982

4H-

£ 40-

•

■

o

u

•

c

•

S 32-

u

•

o

%

c

2 24-

•

•

■

• • •

a

M

. •

(A

£ 16

•
•

•

8

0-

1 T

200

Figure 3.

Simple Regression Analyses

Simple regression analyses were performed, and an attempt
was made to determine whether a statistical correlation
exists between cost per ton clean coal (dependent variable)
and the following independent variables:

Depth (feet)

Level of coal cleaning (0 through 4)

Age of mine (years)

Mine development cost

(dollars per ton annual production)
Annual production (tons per year)
Labor productivity (tons per worker per year)
Seam thickness (feet)

Depth of mines (fig. 3) seemed to have little effect on cost
of coal production per ton. Although a trend line could
be drawn to indicate rising cost per ton as depth increases,
the confidence interval of the slope of the trend line at
90 percent confidence level included the zero value for
the slope, indicating that no definite relationship between
mine depth and cost per ton exists in Illinois.

As indicated in figures 4 and 5, level of coal cleaning
and mine age indicate a positive and a negative correlation
respectively with cost per ton, i.e., cost per ton increases
with greater sophistication in coal cleaning and decreases
with increasing age of mine. However, in both cases the
confidence level in the slopes of the trend lines is low

48

— 40

|

1 1 1 1
400 600 800 1000

i i
1200 1400

c 32

a

S

Depth of mine (ft)

ISGS 198?

S

o

224

5
a

§16

o

Y = 19.15069+ 1.715401 . X
Very low confidence level in slope

1 2

Coal cleaning level

1

4

ISGS 1982

Figure 4.

ISGS/ILLINOIS MINERAL NOTES 84

because the lower ends of confidence intervals for the
slope just about include the zero value. Nevertheless,
the effects of more sophisticated coal-cleaning methods,
as seen in figure 4, are confirmed by common experience.

The apparent contradiction in figure 5— the older the
mine, the lower the cost— is explained by further regression
analyses indicating that older mines tend to work thicker
seams and also tend to be larger in terms of annual pro-
duction. Combined with the effects of inflation, they lead
to lower per ton mine development cost as well as lower
overall costs per ton. Figure 6 indicates that initial mine
development costs significantly affect overall costs per ton
of clean coal. The mine development costs per tons of
annual production are presented in 1980 dollars, which
results in lower development costs per ton for older mines
and helps explain the lower overall costs of older mines.
On the average it is estimated that each additional dollar
(per ton production capacity) spent on mine development
may increase the per ton cost by nearly $.40, with actual
values ranging between $.26 and $.53 per ton. However, the
correlation between mine age and mine size was found to
be extremely weak, and the confidence level of the slope of
the trendline representing the relationship between mine
age and seam thickness was found to be low, as indicated
by the broken trend line in figure 7.

Statistically reliable cost -lowering effects of larger mine
sizes, greater labor productivity, and thicker coal seams

48-1

■5 40
o

32

24

16

8

Y = 28.11713-0.2442808 • X
Low confidence in slope

16

24 32

Age of mine (yrs)

40

48 56

ISGS 198?

50

40-

30-

o 20

10-

:*>

Y = 17.78 + 0.395. X
0.257 < B < 0.533

— r~
20

- 1 —
30

— i —
50

10 20 30 40

Mine development cost ($/ton annual capacity)

ISGS 198?

Figure 6.

12n

10

c 8

v> 4

2

Y = 6.306020 + 0.03231 144
Low confidence in slope

16

24 32 40

48 56

Age of mine (yrs)

ISGS 198?

Figure 7.

Figure 5.

COST OF UNDERGROUND COAL MINING IN ILLINOIS

48

■5 40
o

o 32

o

£ 24

a.

o
u

16

8-

Y = 33.88202 -0.7054618 • 10" ! • X
0.469* 10 5 «S B < 0.941 . 10" »

0 500 1000 1500 2000 2500 3000 3500

Annua! production (short tons) (in thousands)

Figure 8.

are indicated in figures 8, 9, and 10, respectively. The results
of figures 8, 9, and 10 can be summarized as follows:

1. Every 100,000 ton increase in the annual pro-
duction of a mine could lead to a decline of $.47 to
$.94 in the cost per ton of clean coal, with an
estimated average decline of $.70.

2. Every 100 ton increase in labor productivity (tons
per worker -year) could lead to cost decreases
of $.055 to $.095 per ton with an average $.075
per ton cost decline.

3. An increase in seam thickness by 1 foot could lead
to cost decreases of between $1.10 and $4.70
per ton with an estimated average decrease of
$2.90 per ton clean coal produced.

Simple regression analyses help study the relation
between variables. However, they should not be used
separately for cost predictions for the obvious reason
that no single factor can satisfactorily explain the variations
in overall costs per ton.

48

32-

24-

Q.

a 16

o

u

\
\

-

. \

V

\

<

X X
X \

\ •: \ \

\

"H'X

V

\

\

\ \
\
\

\

Y

= 45.88337 -

0.7529152. 10

1 .X

\

0.55416 •

1

10" a

< B < 0.951670 •

10"'

\

800 1600 2400 3200 4000 4800 5600

Productivity (tons/worker -year) ,SGS ,982

48

40

32

o 24

c
o

~16

o

o

8-

Y = 44.73599 - 2.93802 • X
1.1 < B < 4.768

4 6 8 10 12 14

Seam thickness (ft) isgs 1982

Figure 9.

Figure 10.

ISGS /ILLINOIS MINERAL NOTES 84

Multiple Regression Analysis

The multiple regression analysis was performed with cost
per ton of clean coal as the dependent variable and the
following as independent variables:

Mine development cost

Number of longwalls

Depth of mines

Annual production (mine size)

Mine age

Seam thickness

Labor productivity

Investment in equipment per ton

annual production
Level of coal cleaning

At a 90 percent significance level, the multiple regression
analysis showed the following five factors to explain about
90 percent of the variations in cost per ton:

Mine development cost

Labor productivity

Mine size

Mine age

Level of coal cleaning

Because the multiple regression selects factors with sig-
nificant marginal contributions in explaining the variability
of the target function (cost per ton) over and above that
explained by other factors, factors such as seam thickness
and depth do not appear as significant, although they have
strong correlations with productivity and mine size as
shown in previous Illinois State Geological Survey investi-
gations (6). Those correlations are confirmed by the results
of this investigation as indicated in the final printout of the
multiple regression analysis (Appendix B). Parts I and II
in Appendix B compare the regressions performed with
9 and 5 independent variables and suggest that the simple
5 -factor model (with R2 = 0.897) is as good as the larger
9-factor model (with R2 = 0.908).

The resulting equation for cost per ton of clean coal
(Y) is:

Y = 44.22
(6.3166*10"3)X3

-(1.9085*10-6)X, -0.19906*X2 -
+ (6.4903 *10_2)X4 + 0.75955*X5

where: X, = annual production (tons per year)
X2 = age of mine (years)

X3 = labor productivity (tons per worker per year)
X4 = mine development cost (dollars per ton

annual production)
Xs = coal cleaning level (0 through 4)

In figure 1 1 , about three fourths of the cost predictions
based on the above five variables fall within a ±10 percent
range of those calculated with the full cost model based
on cost functions developed earlier. In four notable excep-
tions, the predicted values differ by 15 to 30 percent from
the calculated values. These exceptions cannot be explained
with the limited approach of the present investigation.
However, the regression equation with 5 variables tends to
underestimate the costs of large and highly productive
mines with lowest cost per ton of clean coal, indicating
that investments made toward improving productivity
escape consideration in the approach used by this model.
Generally, cost predictions based on the regression equation
tend to be slightly above the costs calculated by the full
model, as reflected by a larger number of points above
the zero line than below it.

Data on actual cost per ton of clean coal for each
Illinois underground mine are not available and, for com-
petitive reasons, are not likely to be available in the future.
The model approach used here succeeds in comparing
costs by mines and analyzing the variables affecting the
costs. Since the model takes a snapshot look at the mining
and coal cleaning costs as of 1980, some mines with tem-
porary production problems may appear as high- cost
mines. In the long run, they may not be so. On the other
hand, some mines may have had an exceptionally problem-
free year, although their long-term costs may be slightly
above the costs calculated here. It is estimated, however,

COST OF UNDERGROUND COAL MINING IN ILLINOIS

S +20%

o +10% +■ - — - — i-

0
-10%
-20%
-30%

10 15 20 25 30

Cost per ton of clean coal ($)

35

40

Figure 11. Deviation of cost predictions based on regression equation
from cost calculations based on full model.

that the number of mines with production and/or mar-
keting problems in 1980 was larger than the number of
mines without problems. It is not possible to determine
what percentage of production was affected in 1980
without investigating at least 5 consecutive years con-
cerning cost per ton. Considering the employment and
productivity data of the mines under study, it is estimated
that 10 to 15 percent of production could have been
affected in 1980 due to either geological, technical, or
market factors. The net effect on cost per ton of the
positive and negative influences cannot be quantified.
However, they might result in a generally somewhat lower
actual cost per ton of clean coal than calculated in this
investigation for all Illinois underground mines.

CONCLUDING OBSERVATIONS

Cost estimates for existing underground coal mines could
be useful in negotiating long-term coal delivery contracts
and property transactions. Low -cost mines are likely to
offer more stable contract conditions than high-cost mines.
With simplicity of data collection and calculation as the
goal, an attempt has been made to construct a model for
cost calculation. The results are found to correlate well
with the average value of coal mined by underground
methods in Illinois.

The simple regressions generally confirm the expecta-
tions based upon experience. A multiple regression analysis
established mine development cost, labor productivity,
mine size, mine age, and the level of coal cleaning practiced

as the most significant factors in explaining variations in
cost per ton. Obtaining input data pertaining to these
factors is not difficult and the cost -predicting capability
of the multivariate equation, with about 75 percent of
cost predictions within ±10 percent of the calculated full
model costs, could be considered as an acceptable first
approximation. The model should be useful for com-
parisons between mines.

Improvements in the cost -estimating capability of the
model are tied to expansion in the amount and accuracy
of input data. To produce any improvement in cost esti-
mation would probably require an exponential increase
in the collection of data.

The model could be expanded in more than one way.
Similar models have been developed for financial analysis.
With minor changes, the model could be used to test the
sensitivity of costs per ton to changes in individual cost
factors such as labor, overhead, and energy. Applying the
model to assess resource utilization is also conceivable,
with appropriate modifications.

REFERENCES

1. Electric Power Research Institute, 1981, Underground coal
mining cost model, v. 1; Users' guide for the underground coal
mining cost model, v. 2: EPRI EA-1273 Coal Mining Cost Model.

2. U.S. Department of the Interior, Basic estimated capital invest-
ment and operating costs for underground bituminous coal
mines: Bureau of Mines Information Circulars, IC 8632 (1974),
IC 8682 (1975), IC 8682A (1976).

U.S. Department of the Interior, Basic estimated capital invest-
ment and operating costs for underground bituminous coal
mines developed for longwall mining: Bureau of Mines Infor-
mation Circulars, IC 8715 (1976), IC 8720 (1976).

3. Thompson, Burdock C, and Mark Wesley Edwards, 1978,
Estimated impact of the 1978 UMWA contracts on the cost of
mining coal: U.S. Department of Energy, Energy Information
Administration, Washington, D.C., DOE/EIA-0102/2.

4. Illinois Department of Mines and Minerals, 1980 Annual coal,
oil, and gas report.

5. 1981 Keystone Coal Industry Manual: McGraw Hill, New York.

6. Malhotra, R., 1975, Factors responsible for variation in pro-
ductivity of Illinois coal mines: Illinois State Geological Survey
Mineral Notes 60.

ISGS/ILLINOIS MINERAL NOTES 84

APPENDIX A. Cost Functions for Underground Coal Mines in Illinois

Part I: Development Costs

D(J)

Development costs for mine J

Td^J) + D2(J) + D3(J) + D4(J) + DS(J) I . Y(J) • UL + 25|($/yr)

Shaft construction = [a(J) * B(J) * 5,500 J + 650,000 ($)

G(J)

where D^J)

D2(J) = Slope construction = F(J) * 2,600 *

in 17°

+ 500

($)

D3(JJ

D4(J)
DS(J)

Cost per portal for drift mines = 150,000 ($)

Main entries and longwall development, if any = Th(J) * 2 • 3 • 2000j + [2 • 5 • 3000 • 200J <$)

Other surface facilities excluding preparation plant related facilities

T(J)

0<T(J) < 1,000,000

1,000,001 < T(J) < 2,000,000

2,000,001 < T(J) < 3,000,000

DS(J)

2,500,000 ($)

3,000,000 ($)

4,000,000 ($)

A(J) = Number of shafts

B(J) = Average depth of each shaft (ft)

F(J) = Number of slopes

G(J) = Vertical distance covered by the slope (ft)

H(J) = Number of longwall faces

Y(J) = Deflation index corresponding to the year V(J) in which the mine started (see Part X)

T(J) = Annual production of the mine (t/yr)

Cost of shaft sinking 5,500 ($/ft)

Shaft hoist, lining, etc. 650,000 ($)

Slope construction cost 2,600 ($/ft)

Underground development entries cost 200 ($/ft)

(net after adjusting for coal value produced)

Depreciation period assumed to be 20 years.

Average interest rate assumed to be 10% p.a. on 50% of initial investment (1980 U.S. dollars assumed).

COST OF UNDERGROUND COAL MINING IN ILLINOIS

Part II: Mine Operation and Maintenance Cost

L(J) = Labor cost of mine J = K(J) • 81.0 • N(J) • 1.93 ($/yr)
S,(J) = Salary costs = 0(J) • 20,000* 1.5 ($/yr)

C( (J) = Machine depreciation and interest

11

Y Q(I,J)'S(I)

L I = 1

Y,(J)

_]_ 1

To 20

($/yr)

C2(J) = Longwall depreciation and interest = H (J) • 7,000,000 •

\8 20 }

where K(J) = Number of labor on payroll

N(J) = Days worked/year

81 = Dollars/day wages

1.93 = Labor overhead including payments agreed to in the UMWA contracts

O(J) = Number of salaried persons on payroll

20,000 - Dollars/year average annual salary

1.5 = Salary overhead

Q(I,J) = Number of machines of type I in mine J

S(l) = 1980 price ($) of machine of type I (See Part VIII)

Y[ (J) = Deflation index; 10 year machine life expected; interest paid on 50% of investment
over the expected life period

7,000,000 = Investment in a 500-ft longwall face; 8-year life expectancy assumed

E(J) = Energy cost ($/yr)

11

+ (300 • 3 • Z(J)/2) + [(F(J) +1) • 300] +

} Q(I,J) • R
I = 1

(A(J) * 1,000) + (H(J) * 780) > * X(J) • 5 ■ N(J) • 0.08

where R(l) = Installed kilowatt/machine of type I

Z(J) = Number of production units excluding longwalls; two production units served by a system
of 3 conveyors each with a 300 kw drive

Conveyors in slopes assumed to need an additional drive
Shaft hoist and ventilation fan installed power = 1000 kw
Longwall installed power = 780 kw

X(J) = Number of shifts worked/day

Total hours/shift for which machines actually run = 5
Price of electricity = 0.08 ($/kwhr)

10

ISGS/ILLINOIS MINERAL NOTES 84

Part III: Underground Haulage

C3(J) =

ft $/ft $/terminal

, / / / I r 7 Z(J)\

(3,000 • 37) +61,500 + ( 3,000 • 25 • ) +50,000

[(500* 25«Z(J)) +50,000J + (l,000 • 30 • H(J))

Investment ($) in the haulage system in main entries, crosscuts, sections, and longwall sections respectively
(slope conveyors not included)

C4(J)

G(J)
sin 17°

+ 500 * 37

+ 75,000 = Investment ($) in belt construction in slopes and belt terminal

C5(J) = f C3(J)+C4<J) J * I — + — )= Annual underground haulage cost; 5-year depreciation period assumed ($/year)

(-**- )

\5 20 J

C6(J) = Auxiliary equipment investment = Z(J) * 125,000

($/yr)

Part IV: Mining Related Cost ($/yr)

Sum of Parts I, II, and III plus materials, supplies, and royalties

M(J) = I D(J) + L(J)+Sj(J) +Ci(J)+C2(J)+Cs(J) + C6(J) + E(J)| * 1.22
(10% materials and supplies and 12% royalties based on cost)

Part V: Coal Preparation Cost ($/yr)

T(J)

P(J) =

P(J) =
PI (J) -

PKJ) •

N(J) * 14

.[-1+1+ 1

,20 20 200

6,000 * N(J) * 14 • 0.08

Annual coal preparation costs ($/yr)

Preparation plant investment ($/t/hr) (see Part VIII)

3.5% of investment for maintenance and supplies

20 year depreciation

10% interest on V2 of investment

6000 kw installed power

14 hrs/day working time

0.08 $/kwhr electricity price

Part VI: Total Cost Per Ton Clean Coal
M(J) + P(J)

Cost (J)

T(J) * 0.9

Preparation plant recovery factor 0.9

&UN0IS GEOLOGIC/
SURVEY

MAY 10 1y83

COST OF UNDERGROUND COAL MINING IN ILLINOIS

11

Part VII: Coal Preparation Investment ($/t/hr)

^~~~--~^Level of Coal Cleaning
Tonnage Category T(J) ^^---^^

2

3

4

0 < T(J) < 1,000,000

8,200

23,500

48,600

1,000,000 < T(J) < 2,000,000

6,800

22,000

47,300

2,000,000 < T(J) < 3,000,000

5,800

20,000

45,400

U(J) = 2
U(J) = 3
U(J) = 4

Heavy media separators

Heavy media separators and centrifuges and/or cyclones

Heavy media separators and centrifuges and/or cyclones and flotation

Part VIM: Mining Machine Prices (1980 dollars) SO)

Continuous Miner

450,000

Cutter

250.000

Loader

180,000

Pump/Compressor

10,000

Rock Duster

50,000

Locomotive

75,000

Mine Car

40,000

Shuttle Car

250,000

Airdox Machine

150,000

Drill

150,000

Roofbolter

120,000

Data adapted from Electric Power Research Institute (1) and USBM (2).

Part IX: Deflation Index

1980

1.000

1970

0.559

1960

0.440

1979

0.940

1969

0.524

1959

0.434

1978

0.880

1968

0.490

1958

0.429

1977

0.823

1967

0.484

1957

0.423

1976

0.765

1966

0.477

1956

0.417

1975

0.731

1965

0.471

1955

0.411

1974

0.696

1964

0.465

1954

0.406

1973

0.662

1963

0.459

1953

0.400

1972

0.628

1962

0.453

1952

0.394

1971

0.593

1961

0.446

1951

0.388

Data adapted from U.S. Department of Commerce Quarterly Business Review.

12

ISGS/ILLINOIS MINERAL NOTES 84

APPENDIX B:

Part I: Regression analysis of cost with 9 independent variables

Correlations

Longwalls

Cost

Depth Mine size Minage

Level

of

Develop-

Seam

Produc-

Invest-

coal

ment

thickness

tivity

ment

cleaning

cost

Longwalls

Cost

Depth

Mine size

Minage

Seam

thickness
Productivity
Investment
Level of coal

cleaning
Development

1.000

.170

.499

-.020

-.167

.517

-.185
.692
.244

-.087

1.000

-.133 1.000

-.714 .449

-.319 .191

- .481 .705

.791
.550
.310

.535

.184
.252
.051

.396

1.000
.258
.549

.457
-.385
-.128

-.658

1.000
.258

-.072

-.189

.013

-.291

1.000

.234
.144
.056

-.468

1.000

-.446 1.000

-.180 .397

-.164

.343

1.000
.156

1.000

Coefficients

Variable

B (Std. V)

B

Std. Error (B)

T

Longwalls

.0316

4.0141E-01

1.7618E+00

.228

Depth

-.0246

-7.9420E-04

4.3689E-03

-.182

Mine size

-.1184

-1.1703E-06

1.3221E-06

-.885

Minage

-.2239

-1.7129E-01

6.7187E-02

-2.549

Seam

-.1522

-9.3005E-01

8.9985E-01

-1.034

thickness

Productivity

-.6304

-5.9982E-03

1.1168E-03

-5.371

Investment

.0808

1.0913E-01

1.9090E-01

.572

Level of coal

.1286

7.1165E-01

4.4947E-01

1.583

cleaning

Development

.1622

5.2223E-02

3.6310E-02

1.438

cost

Constant

0

4.8030E+01

4.7132E+00

10.191

Summary

Multiple R

R -Square

Unadjusted .9528
Adjusted .9269

.9079
.8591

Std. Dev. of Residuals = 2.8368E+00
N = 27

COST OF UNDERGROUND COAL MINING IN ILLINOIS

13

Part II: Optimal regression model for cost with 5 independent variables

Correlations

Level

of

Mine

Produc- coal

Development

Cost

size

Minage

tivity cleaning

cost

Cost

1.000

Mine size

-.714

1.000

Minage

-.319

.258

1.000

Productivity

-.791

.457

-.072

1.000

Level of coal

.310

-.128

.013

-.180 1.000

cleaning

Development

.535

-.658

-.291

-.164 .156

1.000

cost

Coefficients

Variable

B (Std. V)

B

Std. Error (B)

T

Mine size

-.1932

-1.9085E-06

1.0599E-06

- 1.801

Minage

- .2602

-1.9906E-01

5.7449E-02

- 3.465

Productivity

- .6639

-6.3166E-03

7.9042E-04

- 7.991

Level of coal

.1373

7.5955E-01

3.9859E-01

1.906

cleaning

Development

.2016

6.4903E-02

3.1171E-02

2.082

cost

Constant

0

4.4220E+01

2.6854E+00

16.466

Summary

Multiple R

R -Square

Unadjusted .9469

.8967

Adjusted .9339

.8721

Std. Dev. of Residuals = 2.7028E+00

N = 27

14

ISGS/ILLINOIS MINERAL NOTES 84