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Breaking down energy consumption in industrial grinding mills
Conference Paper · January 2017
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BREAKING DOWN ENERGY CONSUMPTION IN INDUSTRIAL GRINDING MILLS
*Jocelyn Bouchard1, Gilles LeBlanc2, Michelle Levesque3,
Peter Radziszewski4, and David Georges-Filteau1
1
Université Laval, Département de génie des mines, de la métallurgie et des matériaux,
LOOP (Laboratoire d'observation et d'optimisation des procédés), Centre E4m,
Pavillon Adrien-Pouliot, 1065 avenue de la médecine, Québec, Québec, Canada, G1V 0A6
(*corresponding author: jocelyn.bouchard@gmn.ulaval.ca)
CanmetMINES / CanmetMINING
Ressources naturelles Canada / Natural Resources Canada
2
1 Promenade Haanel, Bâtiment 10, Ottawa, Ontario, Canada, K1A 1M1
3
1079 Kelly Lake Road, Sudbury, Ontario, P3E 5P5
4
Metso Minerals Canada
795 Avenue George V, Lachine, Québec, Canada, H8S 2R9
ABSTRACT
Grinding mills are infamous for their extremely low energy efficiency. It is generally accepted
that the energy required to produce new mineral surfaces is less than 1% of the electricity consumed to
operate ball mills. The remaining 99% is assumed to be dissipated as noise, vibration, and heat, but there is
no clear picture on how much is lost in the air or serves to heat the slurry. This paper reports the results of
an investigation targeting two objectives: (1) characterising energy outputs in industrial grinding mill
circuits, and (2) identifying the potential for recovering energy from grinding circuits. Agnico-Eagle
Goldex Division, Mine Canadian Malartic, and New Gold New Afton Mine participated in the study by
providing operating data for 3 semi-autogenous grinding (SAG) mills and 4 ball mills. Results show on
average that 79% of the supplied electrical energy converts to heat absorbed by the slurry, 8% is lost
through the drive system, and about 2% of the energy is transmitted to ambient air. The analysis reveals
that 91 % of the input energy, currently wasted as heat, could potentially be recovered using suitable
technologies or integrated energy management systems, but this topic remains to be further investigated.
KEYWORDS
Comminution processes, Grinding mills, Energy recovery potential
INTRODUCTION
Grinding is largely recognised as a very inefficient process; energy efficiency estimates range
from <1 to 2 % (Fuerstenau and Abouzeid, 2002; Tromans and Meech, 2002, 2004) when comparing the
input energy to that required for generating new mineral surfaces. Criticising an “ill definition of the
reference for the output energy”, Fuerstenau and Abouzeid (2002) proposed instead to use “ the energy for
producing new surface area by the compression loading or impact loading of single specimens” in order to
provide a “more meaningful baseline”. Based on this reference they concluded that “the ball mill is
reasonably efficient energetically”, e.g. exhibiting an efficiency of ~15 % for quartz.
Schellinger and Lalkela (1951) and Schellinger (1951) defined thermodynamic efficiency as the
ratio of the effective work to the energy input. The effective work in the comminution process corresponds
to the difference between the energy input and that lost as heat. It was determined that the thermodynamic
efficiency of comminution ranged from 10 to 20%.
Tromans (2008) introduced the “relative efficiency ratio” involving the concept of “maximum
ideal limiting efficiency” against which the conventional energy efficiency is compared. Even using this
definition, efficiency figures remain very low, ranging from 3 to 26%.
Although it is commonly known that industrial grinding circuits loose – or generate – significant
amounts of heat, little information is known about the actual energy balance. Radziszewski (2013)
estimated using a thermodynamic analysis that 43% of the energy input in a typical mill is transferred to
the slurry, raising the temperature of the discharge product. It is only recently that a more precise picture
started emerging with a pilot project for developing a methodology to map energy flows (Bouchard et al.,
2016). Following the idea of recovering energy currently being wasted (Radziszewski, 2013; Radziszewski
and Hewitt, 2015), the objective was to propose a tool for characterising the complete potential.
This paper reviews the results of the pilot project conducted at Agnico Eagle Mines Goldex
Division, and supplements them with new data from two other operations, namely Canadian Malartic
(Agnico Eagle Mines and Yamaska Gold) and New Afton (New Gold). It first introduces the underlying
concepts for quantifying comminution energy use and recovery potential, and then presents the results of
the survey. A discussion follows, comparing the data, analysing the potential for energy recovery, and
examining avenues to increase energy efficiency.
QUANTIFYING COMMINUTION ENERGY USE AND RECOVERY POTENTIAL
Characterising energy flows in a comminution circuit requires i) defining a control volume around
the relevant pieces of equipment, and ii) determining the input and output energy streams within this
control volume, as illustrated in Figure 1 where:
•
•
•
•
•
•
𝑚 represents a mass flowrate,
ℎ represents the specific enthalpy,
subscripts sl, air, in and out are used for slurry, air, inlet, and outlet streams respectively,
𝑊!"!! is the electrical power input,
𝑄!"#$ encompasses all heat losses (evaporation, convection/radiation, dissipated in
mechanical and electrical components), and
𝑊!"#$ corresponds to the work output (creation of new surface, liners and grinding media
wear, plastic deformation, and mechanical losses).
The energy balance can be written around the control volume as
Ẇ
frag
Q̇ lost
ṁ air in h air in
ṁ air out h air out
ṁ sl in h sl in
ṁ sl out h sl out
Ẇ
elec
Figure 1 – Control volume around a grinding circuit
𝑊!"!# − 𝑊!"#$ − 𝑄!"#$ = 𝑚!" !"# ℎ!" !"# − 𝑚!" !" ℎ!" !" + 𝑚!"# !"# ℎ!"# !"# − 𝑚!"# !" ℎ!"# !"
(1)
In equation (1), the term corresponding to 𝑊!"!# and those on the right hand side can all be
characterised from operation data, temperature measurements, and slurry composition.
Quantifying Heat Losses
The power losses corresponding to 𝑄!"#$ can be broken down into 3 subcomponents:
1) heat dissipated in the electrical and mechanical components,
2) heat dissipated at the mill shell through convection/radiation,
3) latent energy absorbed by water during evaporation.
The main mechanical and electrical components typically installed in a grinding mill are the
transformer, variable speed drive (for mill speed modulation), electric motor, gearbox, mill trunnions and
oil cooling system. Power losses from a given component are dissipated as heat and are proportional to the
power transferred to the equipment and its efficiency.
The mechanical and electrical components used to power a grinding mill are commonly used in
industrial applications, thus technical data for these are available from manufacturers. Heat losses are
estimated using these data.
The remaining heat losses corresponding to convection, radiation and evaporation can be
estimated from equipment dimensions and operation data, providing a few simplifying assumptions as
demonstrated by Radziszewski and Hewitt (2015).
Quantifying the Work Output
Estimating the power output 𝑊!"#$ resulting from mechanical work performed inside the control
volume is more challenging. There are essentially four main sources of mechanical work:
1)
2)
3)
4)
ore comminution,
wear (grinding media and liners),
plastic deformation (grinding media and liners), and
vibration and noise.
The interpretation of the concept of mechanical work in a grinding system used in this paper
follows the one postulated by Schellinger (1952), i.e. it is “the disappearance of energy […] caused by the
creation of surface energy within the tumbling chamber”. In other words, it is the “energy absorption from
the tumbling system” calculated as a difference using equation (1): after quantifying 𝑊!"!# , 𝑄!"#$ , and the
members on the right hand side, the only remaining unknown is 𝑊!"#! .
As discussed by Bouchard et al. (2016), grinding media and steel liners typically do not undergo
important plastic deformation. This suggests that very little of the mill power draw is involved in
mechanical deformation work. Nevertheless, the resulting distribution, as a percentage of electrical input
power is included in 𝑊!"#$ .
Unlike other sources of mechanical work, energy dissipated as vibrations and noise was
independently quantified in the pilot phase of this project at Goldex Division. Results revealed that sound
and vibrations exhibit negligible power levels (~1.1 kW) (Bouchard et al., 2016). They were therefore
considered as part of 𝑊!"#$ for the remainder of the project.
Input power used to perform mechanical work cannot be recovered. Thus the potential for energy
recovery corresponds to that within 𝑄!"#$ as well as that in the mass flows of the air and slurry streams.
RESULTS
Three different processing plants, namely Goldex, Canadian Malartic, and New Afton participated
in the study.
Agnico Eagle Mines Goldex Division is located in Val-d'Or (North-western Quebec, Canada). It
is an underground mine extracting 5 100 t/d grading 1.5 g/t to produce 100 000 ounces of gold per year.
ROM ore feeds a 2-stage crushing circuit before entering the processing plant. An open-circuit SAG mill
(7.32 × 3.73 m effective grinding length (EGL); 3 357 kW) processes the product from the crushing stage.
The discharged slurry is further ground in a pre-classification closed-circuit ball mill (5.03 × 8.23 m EGL;
3 357 kW) reducing the P80 (sieve dimension greater than 80% of ore particles in the product, weight basis)
to ~100 µm.
Canadian Malarctic, a Yamana Gold – Agnico Eagle Mines joint venture, operates an open pit
mine in Malartic (25 km west of Val-d'Or, Quebec, Canada) extracting 55 000 t/day averaging 1.2 g/t to
produce 580 000 ounces of gold and 600 000 ounces of silver per year. After a 2-stage crushing, the ore is
processed in a closed circuit SAG mill (30 × 7 m EGL; 14.5 MW). Oversize material then feeds a pebble
crusher while the passing product goes to two identical (treated as one in this study) parallel ball mills (7. ×
22 m EGL; 11 MW).
The New Afton operations are located 10 km south of Kamloops (British Columbia, Canada).
New Gold Inc. operates this underground blockcaving mine with an output of 15 000 t/day, producing over
85 000 ounces of gold and 75 million pounds of copper per year. The grinding circuit is comprised of a
SAG mill (8.5 × 4 m EGL; 5 220 kW), pebble crusher processing the oversize material, and secondary ball
mill (5.5 × 10 m; 5 220 kW) processing the undersize fraction.
The control volume for each mill used in this case study is depicted in Figure 2, and shows the
following eight output heat flows:
•
•
power loss dissipated as heat in the transformer (𝑄! ), variable frequency drive (𝑄! ), electric
motor (𝑄! ), gearbox (𝑄! ), trunnion cooling system (𝑄! ), convection and radiation around the
mill shell (𝑄! );
enthalpy flows with air (𝑄! ) and slurry (𝑄! ) streams at the mill discharge.
Q˙ 1
Q˙ 2
Q˙ 3
Q˙ 4
ṁ air in h air in Q˙ 5
Q˙ 6
Q˙ 7
Ẇ
elec
Transformer
Variable
Frequency
Drive
Electric
Motor
Ẇ
Gearbox
frag
Q˙ 8
ṁ sl in h sl in
Figure 2 – Energy flow model
The distribution of the power output as a percentage of the electrical power input is presented in
Figure 3 and Figure 4 for the SAG and ball mill circuits respectively.
Grinding
Slurry
Evaporation
Convection & radiation
Cooling
Gearbox
Motor
VFD
Transformer
0%
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
New Afton
Canadian Malartic
Goldex Division
Figure 3 – Distribution of the power output in SAG mill circuits
Grinding
Slurry
Evaporation
Convection & radiation
Cooling
Gearbox
Motor
VFD
Transformer
0%
10%
New Afton
20%
30%
40%
Canadian Malartic
50%
60%
70%
80%
90%
Goldex Division
Figure 4 – Distribution of the power output in the ball mill circuits
Results from the case studies reveal that energy transferred to the slurry is by far the most
significant loss of grinding processes. In fact, it represents on average 79.4 % of the total electric energy
supplied to the grinding mills. All the other types of heat loss only account for about 11.7 %.
The remaining energy is therefore converted into mechanical work. As mentioned previously, ore
grinding and internal wear characterize the major part of this expenditure. This fraction of the energy is
also unrecoverable. On average, it corresponds to 8.2% and 9.6% respectively for the SAG and ball mills,
or 8.9% for the overall circuits. These values are consistent with those reported by Schellinger and Lalkela
(1951) and Schellinger (1951), i.e. 10 to 20%, which were determined using a similar approach in a
laboratory environment.
It is also interesting to note that results are relatively consistent from one mill to another,
especially for ball mills, and also quite similar between the SAG and ball mills. This suggests that the
energy distribution established here could be used as an initial approximation of energy losses simply upon
knowing the total power supplied to a grinding system.
Table 1 provides some key temperature values in the circuits namely for the process water, and
mill discharges. It shows that even though most energy is captured in the slurry, it consists of low-grade
sources as the temperatures reached are below 50°C.
Table 1 – Temperature values [°C]
Process water
SAG mill discharge
Ball mill discharge
Goldex
Division
20
35
38
Canadian
Malartic
26
26
36
New Afton
6
19
16
DISCUSSION
Energy recovery potential
Thermal energy losses accounts on average for about 91% of total supplied energy identified in
this study i.e.: 79% converts to heat absorbed by the slurry, 8% is lost through the drive system, and about
2% of the energy is transmitted to ambient air. The total heat loss share could theoretically be recovered,
transferred to other heat consumers or used to produce electricity. The most obvious application would be a
heat exchanger between the slurry and a cold fluid (air or solution) requiring to be preheated. However,
one could also contemplate extracting the heat from electrical rooms (hosting motor control centers and
drives) with heat pumps also using cold fluids requiring to be preheated as heat sinks.
Heat exchanger applications generally exhibit efficiencies of around 75%. However in this case,
considering the low temperature range (16°C to 38°C) encountered in the survey, recovering thermal
energy could be difficult. Moreover, even if a substantial amount of heat is potentially available for
recovery, it can only be useful if a demand is identified. Various scenarios could be further investigated
with this regard. The low-grade stored energy could be transferred for instance in first preheating stages for
several applications, ranging from elution solution in a cyanidation plant, to underground ventilation air, to
reagent preparation.
On the other hand, it must be emphasised that thermal losses through convection and radiation, as
well as those from the electric motor, gearbox, evaporation and trunnion cooling system are de facto
recovered during the cold weather period of the year as they contribute to heating the building. This is also
true for a portion of the enthalpy conveyed by the slurry in pump boxes and in downstream processing
tanks. Energy management systems must therefore integrate both passive and active heat recovery
strategies, and exploit their full potential, using for instance forced convection means to facilitate
distribution inside a large volume or between levels.
Conversion to electrical energy is another avenue. As explained by Radziszewski (2013), “[h]eat
engines convert heat to a mechanical form of energy that can be used directly or converted into electricity
where the maximum theoretical efficiency is defined by Carnot’s efficiency relationship”. Table 2
illustrates that Carnot efficiencies are very low and hardly acceptable within the temperature ranges
observed in the survey data (calculations are made assuming water at 10°C as the cold sink). Trying to
generate electrical energy is simply not currently worthwhile with a Carnot thermal efficiency averaging
6,7 %. Solutions are therefore needed to improve the Carnot efficiency. Radziszewski (2013) presented
some ideas for this purpose, such as dry milling since it involves temperatures of around 85°C, which are
more favorable for energy recovery. Technologies for low-grade heat recovery (heat pumps, heat
exchangers, and power generators, etc.) must also be examined.
Table 2 – Carnot efficiency data
†
Goldex
Malartic
New Afton
Heat losses
(𝑄! to 𝑄! )
[kW]
Energy stored in
the slurry 𝑄!
[kW]
Max. slurry
temp.
[°C]
Carnot
efficiency †
5,072
34,623
9,917
4,116
30,739
8,865
38
36
18
9.00%
8.41%
2.75%
using 10°C water
Beyond energy recovery considerations, the impact of modifying operating temperatures should
also be analysed carefully before any action is taken in this respect (Radziszewski, 2013). In fact,
decreasing the grinding mill slurry temperature could have an impact on ball and lifter plate wear. It could
be positive if it increases the life of the consumables or negative if it accelerates corrosion degradation. A
similar investigation should also be undertaken regarding flotation performance. Flotation chemicals react
differently depending on the temperature. For gold leaching plants, the slurry temperature impacts both the
kinetics of the reaction and oxygen solubility. Higher temperatures tend to accelerate mass transfer and
chemical reactions, but reduce the availability of oxygen, a reactant in the dissolution of gold with
cyanides. The optimum temperature is found when the oxygen supply becomes insufficient to fuel the
reaction.
Reducing the specific energy consumption
Results of the survey show that a whopping share of the energy footprint of conventional
SAG/ball mill circuits is currently being lost as heat. Inadequate or suboptimal operation explains to some
extent the inefficiency, and part of the solution can be found in processing more ore, which should have
very little effect on the powerdraw. The issue of reducing the specific energy consumption (power drawn
per ore throughput, e.g. kWh/t) can be tackled by operating closer to design capacity (Levesque and Millar,
2015), and using process control capabilities (Nunez et al., 2009).
Properly designed process control strategies allow reducing the variability of key process
variables, which in turn can increase the comminution efficiency, and decrease grinding requirements. As
discussed in depth by Bouchard et al. (2017), “automation, process control and real-time optimization
allow not only to determine the proper operating point, but also provide means to reach and maintain it
over time, regardless of fluctuating input material attributes, and process disturbances”.
For new applications, taking advantage of more energy efficient processing equipment (e.g. high
pressure grinding rolls, crushers) (Morrell, 2009; Nordell et al., 2016; Van Der Meer and Gruendken,
2010) is also an avenue that must be considered to reduce the specific energy consumption of comminution
circuits.
Reducing comminution requirements
Perhaps a more holistic approach is to contemplate the overall comminution chain as put forward
in the so called “mine-to-mill” concept (i.e. consistent and fine blasting product) (Kanchibotla et al., 1999).
With this regard, Torrealba-Vargas et al. (2016) recently presented a simulation case study at Canadian
Malartic showing that more “aggressive” blasting strategy can significantly reduce grinding requirements.
Results demonstrated that increasing the powder factor from 0.28 to 0.93 kg/t would increase the mass
fraction of material < 3.175 cm (1¼ in) at the SAG feed from 34% up to 47%, hence a reduction of the F80
(sieve dimension greater than 80% of ore particles in the feed, weight basis) from 89 mm down to 79 mm.
Total economical benefits exhibited reductions of operating costs from 7.0 to 24.5 %, increasing with the
powder factor.
Comminution requirements obviously also decrease with the amount of material being processed
or reprocessed in grinding equipment. Ore sorting (Lessard et al., 2014), coarse particle processing
(Awatey et al., 2015), flash separators (Tbaybi, 2015), or improved particle classification (Silva et al.,
2012) are all means of investing comminution energy on particles requiring to be fragmented, and therefore
increase the share of mechanical work in the overall energy balance.
CONCLUSION
This paper provided a snapshot of how energy consumption can be broken down in grinding
circuits. Three SAG/ball mill circuits were surveyed revealing that on average, 91% of the supplied energy
results in heat losses, leaving only 9% for ore breakage. The slurry absorbs most of the energy, i.e. 79%,
raising the temperature between the mill inlet and outlet, 8% is lost through the drive system, and about 2%
is transmitted to ambient air. Results from all three circuits were consistent.
All these losses add up to a significant amount of potentially recoverable energy. However, it is
stored in the slurry and air at relatively low temperature (< 50°C), making the conversion into a useable
supply challenging.
Future work could examine various strategies and technologies for low-grade heat recovery, such
as heat pumps, heat exchangers, and power generators among others. Energy recovery solutions would
require retrofits in existing plants with the purchase and installation of heat transfer/conversion equipment,
which may impact operation, metal recovery and maintenance. Thus a techno-economic assessment would
be required to determine whether the options are financially viable. Efforts should also aim at determining
how design practices could be adapted for future mine sites to enable an integrated approach of energy
management.
For existing plants, process control and real-time optimisation offer great potential to reduce the
specific energy requirements of comminution circuits with minimum capital investment. Even though the
general principals are known, work is still required to benchmark energy efficiency gains, develop
advanced applications (e.g. observers for unmeasured process variables, economic model-based predictive
control), and determine the optimal load of every stage of the comminution chain.
ACKNOWLEDGEMENTS
The authors would like to thank Agnico Eagle Mines, Mine Canadian Malartic, New Gold New
Afton Mine and CanmetMINING for granting permission to publish this work. Further acknowledgements
have to be given to Sam Marcuson (CMIC), Carl Weatherell (CMIC), Nabil Bouzoubaâ (CanmetMINING)
for championing this R&D initiative.
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