OPTIMIZATION OF CAPITAL COSTS AND CARBON FOOTPRINT IN OPEN CAST
MINING
D.BAG
FACULTY,
MBA Dept.,
NIT ROURKELA,
India, Dinabandhu.bag@gmail.com, 916612462803
Abstract
The capital cost of an opencast mine is determined based on many irreversible geological
factors e.g., topography, ore grade, degree of mechanization, etc. Mining equipment(s) are
procured during the stage of project initiation. However midway into the life of the mines,
half from the original list may remain untouched. Why are capital equipment needed in
mining operations? The mining industry is constantly in search of rapid capacity expansion,
increased productivity, digital technology adoption, and reduced costs. Besides the
economics, the new dimension of development focuses on automation and a clean mining
system. To achieve a faster rate of production, mechanization at higher degrees are
imperative. Mine optimization deals with conventional tools of production planning
ignoring the drain on capital and related GHG implications. When the idle time of
machineries increases, it leads to increase in production cost. In order to reduce the idle time
or waiting time the number of machineries may be increased. Due to large number of
machines greater capital investments are needed that contributes to higher total GHG. The
limitation is JIT (Just in Time) model of procurement of spares, does not exist in mining
towns.
Keywords: carbon footprint, trade off, operating costs, equipment’s, carbon gearing ratio
JEL Codes:
1. Introduction
In mining projects, ambitious product targets especially during the short term plan period for
6 months, a much higher daily rate of production will obviate the need for number of
machines. Mining operations cannot mimic a perfectly automated process industry. Capital
planning is not a reversible action. Since mining operations are non-sequential and follow
product layout, capital equipment can be shared across disjoint operations. However, the
degree of sharing is not possible 100%. Therefore, the higher the number of equipment, the
higher is the total capital costs. Long-term production planning for a horizon is generally 2530 years, whereas in the medium-term is it is 1-5 years, The long term production planning
aims to maximize the net present value of the total profits from the mining operations against
given pit slope design, such as mining slope, grade blending, winning ratio, replacement of
the stock, etc. The medium-term mine plan in the range of 5-10 years aims to maintain
enhance the operating life of the fleet. The short range mine planning is for 1-6 months that
aims to achieve zero down time of the fleet.
Commercial mining is aimed at maximizing expected profits of the firm (Michail B. Kahle).
However, mining is also aimed to minimize carbon footprint (Fred J. Scheaffer). In pure
commercial projects, the capital cost is critical to decide whether projects will proceed, or be
abandoned and hence the accuracy in their determination is critical (Shafiee & Topal, 2012).
The lack of literature or publicly available data on capital costs in many countries has been
highlighted (Hall, 2013). Harper (2008) estimated the capital costs for coal mines in
Australia. Dipu (2011) conducted capital costs determination for India. In many countries the
infrastructure of railway lines, roads and townships are developed which escalates the total
mining costs (Rudenno, 2009). The possibility of reducing off-mine infrastructure has been
mentioned (Jourdan, 2008). However the empirical models of capital costs attributed to pure
mining is limited in Indian setting, especially for open cast mines,.
Ordinarily, the geological properties of the reserves such as seam thickness, metalliferous
veins, or sedimentary beds would create the bounds of pit design. However the parameters
of that are taken into selecting size of machines include purchase and operating costs,
replacement of tires, spares and routine maintenance, workshop, etc. Equipments are chosen
when they provide maneuverability, flexibility in deployment and involve minimum labour
hours to displace, or dissemble. At first, for a given production rate, number of loaders
needed, are determined. Later, the number of haulers are obtained, to match the dimensions
of the loader. The fleet mix deployment schedule is a jointly and simultaneous consideration
of the hourly production rate, hauling time and distance, loader cycle time, etc. The capacity
rate of each of equipment are chosen subject to the dimensions available with the OEM
(Original Equipment Manufacturer) against the fact that the ratio of operating cost, or
purchase cost would vary across OEMs. The objectives of the study includes; explore the
trade-off between optimal capital costs and optimal GHG emissions, arrive at the fleet mix
and ownership mix (self or lease) of scheduled equipment that minimizes the total capital
costs, reduce the total GHGs emitted from face operations and mining activities for open
cast mines respectively.
The rest of this paper is organized as follows. BAU (Business as Usual) section provides the
existing practices of choosing fleet mix of open cast equipment and the process of mine
planning. The Base Model section formulates a simple multi-objective linear programming
model and the steps. The Carbon Inventory sections characterize the limits and boundaries
of GHG emission factors and the productivity costs. The Scenario and discussions sections
provide the impacts, outcomes and the comparative assessment of the tools and goals of the
methodology. The conclusions section summarizes and suggests future research direction.
2. BAU (Business as Usual)
To minimize the capital costs of an open cast mine by direct method of project appraisal
techniques against the target levels of production (MT/annum) and Manpower (000’s). For
e.g., this will identify core and critical equipment’s which are directly owned by the mining
entity against sub critical or non-essential supplementary and irregularly used equipment
which could be procured on need basis and lease rental basis.
A deployment solution must intend to minimize the net emissions of COX, NOX and SOX
from mechanized fleets at the mine face and sites. The overall goals are to minimize power
consumption, petroleum consumption, consumption during blasting, excavation, haulage,
washery, siding, drainage, and accumulation of liquid waste, etc. For example, to begin with,
one may focus on the ratio of GHG/capital costs (CO2 eqv./ USD) and GHG/operating costs
(CO2 eqv./ USD) during the planning of daily production volumes (MT/annum). The mine
manager has the option to choose the average production levels to control emissions so long
as the environmental standards around the mine area are maintained.
Long-Term Production Planning:
The long term production planning aims to maximize the net present value of the total profits
from the mining operations against given pit slope design, such as mining slope, grade
blending, winning ratio, replacement of the stock, etc. The duration of medium-term mine
plan is in the range of 5-10 years period that aims to maintain enhance the operating life of
the fleet. The short range mine planning is for a period of 1-6 months aims to achieve zero
down time of the fleet. The launch start of the mines involves mechanized removal of o/b and
deforestation to prepare the men and tools to proceed towards the mining face. The following
steps describe the need for machines and relevance.
Major operations in open cast mining
a. Before the mining ore production starts the following activities are carried out;
 Supply of power
 Forest clearance
 land preparation
 ground leveling
 Blasting of O/B
 O/B removal, haulage, and shifting and transport of o/b material
 5 staking dumping and lavelling at a distant off site location.
 Barricades to preserve the top soil over burden (O/B).
The Supply of power during pre-preparation work for men and machines are needed. Later
on, when the mining operation starts to generate ores, depending on the degree of
mechanization undertaken. After the mine is abandoned, removal of litter, waste, scrap, men
and material alongside usual attempts to land restoration must happen, by refilling the pits,
sand stowing, reforestation, top soil recovery and landscaping, all of which needs machines in
the form of Re-claimers, dozers, etc. The next section explains the methodology of
evaluation.
3. Base Model
The base GHGs are calculated against base fleet scheduled deployment. Later, assuming a
temperature increase of 1.5 degrees, against the revised inputs of machine performance data,
the new schedules are calculated. The mine owner has the option to combine the fuel mix of
stationary equipment’s (e.g., CNG, Petrol, Diesel, Electric, shown in Table 5) to choose the
best combination that minimizes emission inventory for the site location.
The selection of ratings of equipment for the mine fleet is carried out. The loading height of
the excavators is determined and then the drilling machine heights and loader heights are
determined. The borehole diameter determines the height of the drilling equipment and the
volume of blasting material to be picked up for loading and operating height and the hauling
capacity. A base model assumes that the net GHG emissions are zero when operating costs
are zero. The base fleet mix deployment schedule for short, medium and long term plans are
arrived at. The base fleet composition of equipment, are ensured to withstand disruptions due
to breakdown, repairing, preventive maintenance, etc. It is a dual objective integer
programming problem.
We formulate the problem the Dual objective function mixed integer programming function
intends to minimize both the total capital costs (w1*Vj), and net GHG emissions (w2*ηi*Vj),
respectively.
Minimize Z = ∑ { w1 * Vj + w2 * ηi
*
Vj}
(1)
Where,
Vj
number of machines per type (j)
Qi
Volume of production (Tons/Hr) by ore blend type i
Dj
Haulage Distance travelled inside the mine face per vehicle type (j)
Pi
Number of operators (drivers) by ore blend type i
Ej, Electricity consumed (KWH/year) by machine type j
TKMj Tonnes-Kilometer hauled within the mine area siding (tkm/year) by rail/road by type j
Tj, number of years in the economics life of the mines j
Vj = Oj + Lj,
Where,
Oj is number of owned equipment by type (j)
Lj is the number of leased equipment by type (j)
CO2, emission of CO2 from excavation per HOUR (tons)
SO2, emission of SOX per HOUR (tons)
NOX, emission of NOX from equipment per HOUR (tons)
Where, Vj s are the number of equipment’s that could vary between 0 < Vj <1000.
Where Oj number of equipment owned by the mining company that could vary between 0 <
Oj < 500 and, Lj number of equipment on lease basis availed by the mining company that
could vary between 0 < Oj <500.
All of the Ojs or Ljs are equipment that consumer non–renewable energy and generate GHG
emissions directly in gaseous or generate liquid or solid waste. The volume of liquid waste
and solid waste are converted into CO2 equivalents.
For example, Mitchell Curve and is widely used by equipment managers. Repair costs are
accrued, in part, directly proportional to the hours worked and is reflected by
the A coefficient. The increasing rate of repair cost accrual as the machine ages is reflected by
the B coefficient. Costs associated with maintenance and actions that prevent machine
failures contribute to the A coefficient, while repair actions resulting from failures contribute
to the B coefficient [10]
The basic principle of equipment selection could include;
 matching the life of the mines (Tj) with life of each equipment (Nj).
 matching the hourly target volume of production (Qj) with hourly scheduling capacity of
the machine (Cj).
 matching the hourly target revenue of the mine (Rj) with hourly operating cost (TCj) (all
inclusive)
 loss of revenue of the mines (ΔRj) during breakdown with preventive maintenance costs
of the machine (MCj) .
 arrive at the fleet mix to achieve net GHG target even at a rise in ground temperature by 1.5
degrees (0C).
 arrive at the cumulative aggregate hourly emission (Ej) from the entire combination that
meets hourly GHG rates (CO2 eqv.).
This optimization is undertaken iteratively to ensure that the total capital costs are minimum
in each run of the iteration.
4. Carbon Inventory Data
The secondary data regarding operating cost and use was collected for class 0314 backhoe
loaders and class 0900 motor graders operated as part of a state transportation agency
maintenance equipment fleet. Annual data was collected for all of the machines in the fleet at
least one year in age and for which working hours were recorded. Machines without a
minimum of one full year of use were excluded because initial machine setup costs were
included and are not true operating costs. Machine age was the time from when the machine
was brought into the fleet to the end of the year for which data was collected. The backhoe
loaders ranged in age from 2.8 to 17.3 years, and the motor graders ranged from 3.2 to 21.7
years in age. The total operating cost for each machine included all costs incurred during the
year for maintenance, repairs, fuel, and tires.
Data was collected for 21 Case model 590SM2 backhoe loaders and 34 Volvo model G720B
motor graders. Cumulative operating cost and hours worked were collected for motor graders
10 years in age and backhoe loaders nine years in age, as a small number of backhoe loaders
were brought into the fleet 10 years prior. The data consisted of the hours worked and
operating costs incurred in each calendar year of operation.
Table 1 describes the deployment schedule fleet mix of o/c mines;
TABLE 1 FLEET MIX
Name of the Asset
Purchase Annual
Dimensions Hourly Capacity
Cost
Operating
(Crores) Costs
(Lakhs)
1. trolley mounted
MVA
transformer and diesel
generators
8. Drainage Pumps
2 HP
LPH
9. Water sprinklers
1 HP
LPH
10. O/H Water tanks
KL
LPH
and pipe system
11. Bus Bars, Panels,
MM/METRE
Poles and 33KV cables
12. Staff Transport
Seating capacity
Vans
14. Compressed air
Bore mm
Drilling Machines for
O/B
15.
narrow
gauge
Length Metres
HUALAGE tracks
16. dumpers
Tonnes
17. shovels
Cubic Metre
18. bull dozers
Tonnes
19. substation with
KW
transformers
21. electric drilling
Bore (Hp)
machines
22. heavy duty stone
HP
crushers & Bins
23. heavy duty Bins
Cubic Metre
24. conveyers belt
Width MM & MPH
multi stage
25. siding platforms
Gauge MM
iron or steel or concrete
26. Feeding bin
27. Primary crusher
5 HP
6 TONNES
28.
TEMPRARY
60 TONNES
STORAGE
source
Table 2 describes the emissions factors for HCV (>6000cc) equipment’s & vehicles against
fuel types (BS-II).
Table 2: Emission Factor of HCV under BS-II Fuel standards
Type of HCV
vehicle
Diesel Bus
Diesel Bus
Diesel Bus
Diesel
OEM
Eicher
20/16RHD(4.9lt),
TELCO
LF936CE(5.9ltr),
TELCO LP1510(5.9lt)
Eicher
20/16RHD(4.9lt),
TELCO
LF936CE(5.9ltr),
TELCO
LP1510(5.9lt),
AL
3/1
COMET
(6.54Lit) , etc
Eicher
30.25RHD(4.9lt),
TELCO
LPT2515(5.9lt),
TELCO LP/LPO
/LPS/SE/SK(5.9lt),
Eicher
30.25RHD(4.9lt)
TELCO
LPT2515(5.9lt),
TECO LPT
2518(5.9lt) , etc
Volvo FM9, B7R
(9.4lt) , Volvo FM12
(12.3lt), TECO LPT
2518(5.9lt), TELCO
LPT2515(5.9lt), Volvo
FM9, B7R (9.4lt)
Volvo FM12 (12.3lt),
TECO
LPT
2518(5.9lt)
TELCO
LPT2515(5.9lt) , etc
Volvo FM9, B7R
(9.4lt), Volvo FM12
(12.3lt),
TATA
NOVUS,
TATA
LPT2515, etc
Vintage
CO
(gms/km)
HC
Nox
Co2
PM
Benze ne
(mg/km)
Total
PAH
1991-96
13.06
2.4
11.24
817.52
2.013
0.1529
1.0123
19962000
4.48
1.46
15.25
920.77
1.213
0.1008
3.6515
Post
2000
3.97
0.26
6.77
735.51
1.075
1.1782
0.1749
Post
2005
3.92
0.16
6.53
602.01
0.3
0.0101
1.3715
0.0199
4.5975
CNG Bus
All TATA and AL
CNG Buses , etc
Post
2000
3.72
3.75
6.21
806.5
0.044
Diesel Truck
Eicher
20/16RHD(4.9lt)
TELCO
LF936CE(5.9ltr)
TELCO LP1510(5.9lt)
19912000
19.3
2.63
13.84
837.5
1.965
Diesel Truck
Source
ALCO 3/1 COMET
(6.54Lit)
Eicher
20/16RHD(4.9lt)
TELCO
LF936CE(5.9ltr)
TELCO LP1510(5.9lt)
ALCO 3/1 COMET
(6.54Lit), etc
TECO
LPT
2518(5.9lt) , TELCO
LPT2515(5.9lt)
,
Volvo FM9, B7R
(9.4lt) , Volvo FM12
(12.3lt) , TECO LPT
2518(5.9lt) , TELCO
LPT2515(5.9lt) , etc
Post
2000
6
0.37
9.3
762.39
1.24
0.0049
3.9707
CPCB, New Delhi
Table 3: Emission Factor of EQUIPMENT under BS-II Fuel test CASES
Fuel (kg/hr)
Power
(bhp)
Load %
Engine
load
factor
(ELF)
Brake specific emissions (g/hp-h)
Work performed
ZER
214.5
72.8
0.63
435
0.68
3.72
0.1
133
Digging and backfilling
OADER
42.4
29.8
0.27
624
2.12
4.8
0.28
131
Digging and backfilling
E
33.7
40.4
0.37
557
1.51
5.19
0.32
126
Digging and backfilling
E
38.8
44.2
0.39
596
1.97
4.99
0.64
126
Digging and backfilling
161.3
61.1
0.58
507
3.06
1.78
0.28
129
Digging and backfilling
274.6
54.5
0.51
441
1.88
1.95
0.1
139
Loading trucks
E
25.8
32.6
0.26
615
2.51
5.33
0.68
154
Loading and smoothing
TOR
134.5
55
0.5
587
0.93
2.86
0.21
274
Loading trucks
OADER
81.2
41
0.36
572
2.91
4.5
0.08
128
Scraping dirt
TOR
69
49.2
0.45
547
1
2.65
0.17
109
Scraping dirt
51.8
34.1
0.32
641
2.08
4.24
0.29
365
Loading trucks
45
32.2
0.28
581
3.04
3.59
0.26
491
Pushing trash
0.32
567
3.39
5.16
0.11
84.2
Grading shoulder
OADER
87.1
E
45.3
52.3
0.46
606
1.37
3.35
0.24
97
Cleaning ditch
38.6
28.2
0.24
601
2.49
3.78
0.3
478
Grading dirt road
68.4
42.8
0.41
555
1.91
2.89
0.16
29.1
Grading dirt road
100.7
58.4
0.52
622
1.36
3.13
0.05
142
Grading dirt road
OADER
31.9
26
0.19
573
2.67
5.71
0.3
324
Digging dirt
OADER
89.9
56.1
0.53
558
1.45
3.14
0.15
175
Scraping dirt
OADER
56.1
36
0.33
634
1.74
3.39
0.25
333
Grading road shoulder
TOR
55.6
43.9
0.38
527
0.93
3.07
0.07
152
Pushing Rock
ZER
106.7
35.3
0.36
590
0.43
1.89
0.09
0.36
Pushing trash
ZER
82.2
27.6
0.28
555
-0.09
1.7
0.04
0.18
Pushing Rock
ZER
90.7
40.6
0.41
665
-0.06
1.6
0.04
0.12
Building slope, pushing
ZER
104
39.4
0.33
712
—0.15
2.14
0.06
7.03
Digging Trench
5. Discussions and conclusions
The effort to reduce operating cost would end with energy consumption towards haulage is a
within the mine. The strong relationship between energy use and emissions with respect to
the age of the machines could not been examined. However, it is natural to observe the
impact on truck maintenance and operating costs associated with lower number of fleets.
Table 4 provides the cost benefits of its implementation in terms of physical outputs and
mining benefits
Table 4: COST BENEFIT ANALYSIS AND INTERPRETATION
Capacity of the Mines
1.5MTA
Total project Cost
Rs. 175 Crores
BENEFITS
COSTS
GHG Reduction
Higher Leasing Payments
Lower Capital Costs
Higher Information Technology costs
Lower Operating Costs
Lower average Production
Lower Borrowing and Interest Outgo
Higher average cycle time
Higher Material Recovery rate
Lower Average Money Revenue
Lower dumping costs
Lower volume of hazardous liquid
Reduced Cost of Capital
lubricant waste from central workshop
Lower Employee Health Costs
Higher operating Margin
Lower Environment Monitoring costs
Long term Planning
CONCLUSIONS
This paper explored the trade-off between capital costs, operating costs and energy driven
emissions in open cast mines. It addressed to quantify critical relationships. The mines can
avail of leasing service of heavy machineries instead of spending on OPEX, and CAPEX.
The mines will incur lower rental service expense of equipment rather than owning them.
There are no third-party liability by the mines because the equipment are non-owned by the
mines. There are few limitations of this study. This does not cover the field data and the
analysis of field trials, or experimental response results. Therefore, future research must
include extensive collection of longitudinal test data from a variety of field conditions to
demonstrate the robustness of optimization results. Open cast Mining must attempt to
minimize the large burden of capital costs which cannot be recovered from pricing of the
production or charging to its future generations of customers.
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