Geology Reference
In-Depth Information
Table 12.1 Fuel-use data for selected mineral commodities according to Chapman and Roberts
(1983)
F m F c
F mm F s
MJ/ton
ore
MJ/ton ore
GJ/ton metal
GJ/ton metal
Aluminium
200-500
20-30 (Bayer pro-
cess - per ton of
alumina)
40-60
270
Cobalt
N.A.
N.A.
N.A.
129
Chromium
345
65-420
1.5 - per ton
FeCr
62.6 - per ton of
FeCr
Copper
15-35
240-320
50-75
25-50
Lead
270
280
9.5
8.3
Manganese
345
65
5.21 - per ton of
FeMn
51.7-57.3 - per
ton of FeMn
Mercury
N.A.
N.A.
157
252
Molybdenum
165
280
136
12
Nickel sulphides
680
500
N.A.
N.A.
Nickel laterites
-
100 (F c + F m )
N.A.
N.A.
Tin
-
15 (F c + F m )
187.5 (alluvial) -
157.3 (hard rock)
19.6 (alluvial)-
127.0 (hard rock)
Titanium (Rutile)
38
130
2.8
575
Titanium (Ilmenite)
95
300
23
687
Tungsten
642
420
213
144
Zinc
290
310
11.7
49.6-63.3
get an overall notion, the typical value for R c is 0.85 and R s 0.90; the stripping
ratio S is assumed at 2.0 for open-pit mines and 0.1 for underground mines.
From Eq. (12.6) and Eq. (12.7), one could assume that the energy required in
the mining and concentration processes can be assumed as a constant divided by
the ore grade. Note that the curve derived from the expression of F mm is similar
to the curve derived from Eq. (9.30), i.e. as the ore grade tends to zero, the
energy required to mine tends to infinity. With this very rough approximation,
one could then theoretically calculate the energy required for each value of x m .
Obviously the results obtained through this approach are questionable since the
energy consumption as a function of the ore grade does not necessarily follow the
path of x m , nor must technologies be the same for all ranges of x m . A more precise
evaluation would require more research and compilation efforts of real data sets from
companies in the mining industry similar in nature to those performed by Mudd
(2007b) or Norgate and Jahanshahi (2010). As the reader will see in the following
examples, empirical data of energy consumption, as a function of the ore grade,
suggest relationships varying from x 0: m to x 0: m . Hence, for these calculations, it
is preferable to work with empirical data of energy vs. ore grade. Unfortunately
it is very uncommon to find data for many commodities and one needs more often
than not to resort to approximations.
 
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