Protected B Business Information Version: May 2016

Identifying the Energy Recovery Potential of Grinding Circuits

by Gilles LeBlanc, Eng.

Work performed for: CMIC

Project : P-002334.001

GMIAC Pilot Project on Energy Savings

Report : CMIN 6495267

Version : May 2016

PROTECTED B Business Information

The document is intended only to the members of CMIC

No external distribution without authorization from the Director General’s Office of CanmetMINING..

© Crown Copyrights Reserved 2016.

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DISCLAIMER

Any determination and/or reference made in this report with respect to any specific commercial product, process

or service by trade name, trademark, manufacturer or otherwise shall be considered to be opinion;

CanmetMINING makes no, and does not intend to make any, representations or implied warranties of

merchantability or fitness for a particular purpose nor is it intended to endorse, recommend or favour any specific

commercial product, process or service. The views and opinions of authors expressed herein do not necessarily

state or reflect those of CanmetMINING and may not be used for advertising or product endorsement purposes.

Protected B Business Information Version: May 2016

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Summary In November 2014, the Green Mining Innovation Advisory committee (GMIAC) held a workshop to explore the possibility of accelerating the adoption of green mining technologies by bringing together key partners within the mining value chain to address industry priorities. Participants were representatives of mining companies and associations, universities, provincial and federal departments and organizations. Two priority areas emerged during the workshop: i) saving energy, and ii) water management. CanmetMINING (CMIN) committed to providing resources to conduct R & D in these areas and to work with stakeholders from the mining sector to develop and deploy pilot projects. As a result of this commitment, the Canada Mining Innovation Council (CMIC) and CMIN launched the pilot project to identify the potential for recovering energy from grinding circuits. The objective of this project is to develop a modelling tool to identify the potential for recovering energy losses in grinding circuits. More specifically, the tool will provide an estimate of the distribution of sources of thermal, sound and vibrational energy. CanmetMINING and the Canadian Mining Innovation Council (CMIC) proposed a 5-step project to complete the energy study of three (3) grinding circuits and to develop an MS Excel tool to allow mining operators to identify energy losses. Agnico-Eagle’s Goldex and Canadian Malartic mines, and New Gold’s New Afton Mine participated in the study by providing operating data for 3 semi-autogenous grinding (SAG) mills and 4 ball mills. The development of the model was based on an article by Radziszewski (2013) suggesting identifying energy losses during grinding using a thermodynamic model. This approach was used to estimate the heat dissipated by the electrical and mechanical equipment, the heat dissipated through the shell of the mill by convection and radiation, the latent energy absorbed by water during evaporation and the grinding work which includes ore crushing, ball and lift plate wear, plastic deformation of the structures and the sound and vibrational energy. The tool to identify energy losses in a wet grinding circuit was delivered to CMIC. Despite the complexity of the processes and the difficulty in measuring slurry temperatures, it is relatively easy to determine a circuit’s detailed energy balance. Data from the three grinding circuits studied show that on average 77% of the electrical energy supplied to the grinding circuit is transmitted to the slurry, the grinding work accounts for 10%, 8% is lost through the drive system and about 5% of the energy is transmitted to the ambient air around the grinding mills. This means that approximately 90% of the thermal energy is potentially recoverable. CMIN suggests using this distribution as a simplified model for estimating energy losses. Only one production datum is required, which is the electric power of the grinding mills. Recovery of the thermal energy is not simple since the temperature range of the 7 grinding mills studied ranges from 18

oC to 38

oC. This is a difficult energy source to recover because of the low temperatures involved. For these

reasons, a logical extension of this project would be to carry out a literature survey on technologies for low-grade heat recovery in order to identify the potential of this approach. Other initiatives may be envisaged, and are presented in the expanded conclusion of this report.

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Table of Contents Summary ...................................................................................................................................................... iii

Introduction .................................................................................................................................................. 1

Objective ....................................................................................................................................................... 1

Methodology ................................................................................................................................................. 2

Project definition ...................................................................................................................................... 2

Selection of the mining sites ..................................................................................................................... 2

Literature review ....................................................................................................................................... 3

Thermal energy losses .............................................................................................................................. 3

Sound energy losses .................................................................................................................................. 5

Vibrational energy losses .......................................................................................................................... 7

Data collection .......................................................................................................................................... 7

Modelling tool ........................................................................................................................................... 9

Discussion of results .................................................................................................................................... 11

Agnico-Eagle’s Goldex Mine ................................................................................................................... 11

Description of the plant ...................................................................................................................... 11

Thermal energy losses ........................................................................................................................ 12

Sound energy losses ............................................................................................................................ 14

Vibrational energy losses .................................................................................................................... 18

Agnico-Eagle and Yamana Gold, Canadian Malartic mine ...................................................................... 19

Description of the plant ...................................................................................................................... 19

Thermal energy losses ........................................................................................................................ 19

New Gold, New Afton Mine .................................................................................................................... 21

Description of the plant ...................................................................................................................... 21

Thermal energy losses ........................................................................................................................ 21

Identifying energy recovery potential .................................................................................................... 23

Limitations of the study .......................................................................................................................... 23

General energy distribution .................................................................................................................... 25

Conclusions ................................................................................................................................................. 26

Appendix 1: Literature Review .................................................................................................................... 28

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Appendix 2: Mathematical models for estimating vibrational energy ....................................................... 34

Model 1: Theory of linear elasticity (Hooke) .......................................................................................... 34

Model 2: Instantaneous power of a vibrating mass ............................................................................... 34

Model 3: Statistical Energy Analysis (SEA) .............................................................................................. 34

Bibliography ................................................................................................................................................ 35

List of Tables Table 1: Critical data for modelling ............................................................................................................... 8

Table 2: Measured acoustic power (dB and W) .......................................................................................... 17

Table 3: Maximum values of the standard deviation for the reproducibility of the sound level

measurements ............................................................................................................................................ 17

Table 4: Dimensions of the pillars ............................................................................................................... 18

Table 5: Mechanical properties* ................................................................................................................ 18

Table 6: Estimate of the vibrational energy (W) ......................................................................................... 18

Table 7: Recovery potential ........................................................................................................................ 23

List of Figures Figure 1: Control volume .............................................................................................................................. 4

Figure 2: Electrical and mechanical components of a grinding mill ............................................................. 4

Figure 3: Beamforming antenna ................................................................................................................... 6

Figure 4: Sound map for the secondary grinding mill ................................................................................... 6

Figure 5: Measuring sound intensity at the screen ...................................................................................... 7

Figure 6: Principle of a sound intensity probe .............................................................................................. 7

Figure 7: Protection for measuring instruments........................................................................................... 9

Figure 8: 6-step process ................................................................................................................................ 9

Figure 9: Grinding circuit - Goldex Mine ..................................................................................................... 12

Figure 10: Energy distribution for the Goldex mine grinding mills ............................................................. 13

Figure 11: Sound under the primary grinding mill ...................................................................................... 15

Figure 12: Sound above the primary grinding mill ...................................................................................... 15

Figure 13: Sound at the trommel and at the primary grinding mill motor’s ventilation unit ..................... 15

Figure 14: Sound at the primary grinding mill drive set-up ........................................................................ 15

Figure 15: Sound at the secondary grinding mill ........................................................................................ 16

Figure 16: Sound at the secondary grinding mill motor ............................................................................. 16

Figure 17: Sound at the secondary grinding mill’s drive system ................................................................ 16

Figure 18: Sound from a screen motor ....................................................................................................... 16

Figure 19: Noise from a screen ................................................................................................................... 16

Figure 20: Grinding circuit - Canadian Malartic mine ................................................................................. 19

Figure 21: Energy distribution for the Canadian Malartic mine grinding mills ........................................... 20

Figure 22: Grinding circuit - New Afton Mine ............................................................................................. 21

Figure 23: Energy distribution at the New Afton mine grinding mills ........................................................ 22

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Introduction CanmetMINING (CMIN) commissioned a study in 2013 to better understand the obstacles to the adoption of new technologies in the mining industry (MNP, 2013). The findings highlighted the importance of strengthening the industry’s and the regulatory agencies’ commitment and communications efforts to raise awareness and improve the comfort level of the industry with respect to new technologies. In November 2014, the Green Mining Innovation Advisory committee (GMIAC) held a workshop to explore the possibility of speeding up the adoption of green mining technologies, bringing together key partners within the mining value chain to address industry priorities. Participants were representatives of mining companies and associations, universities, provincial and federal departments and organizations. Two priority areas emerged during the workshop: i) saving energy, and ii) water management. CMIN committed to provide the resources to conduct R & D in these areas and to work with stakeholders in the mining sector to develop and implement pilot projects. This commitment led the Canada Mining Innovation Council (CMIC) and CMIN to launch the pilot project to identify the potential for energy recovery from grinding circuits. The importance of energy-efficient grinding circuits lies in the fact that Canada has 62 concentrators (Geological Survey of Canada, 2014) of which 70% are located in Ontario, Quebec and British Columbia and 83% of the ore is processed using semi-autogenous grinding (SAG) mills and secondary ball mills. Grinding accounts for over 40% of the energy expenditure of a mining complex (Ballantyne 2012) and the energy efficiency of grinding with respect to surface energy is only about 1% to 5% (Lowrinson 1974). Therefore, there is a potential for improvement. Several approaches are being considered by various researchers to address this issue, all of whom want to reduce inefficiencies. The method proposed in this project was introduced by Radziszewski (2013). It follows a thermodynamic approach to recover energy losses. This report describes the experimental methodology used to gather data at three concentrators so as to develop an Excel tool for thermodynamic modelling of energy losses from wet grinding circuits. The results of the energy distribution of the seven grinding mills studied in the course of the project are discussed and the main sources of recoverable thermal energy are identified. Some portions of the introduction and methodology were derived and adapted from the article published in conjunction with the project partners (Bouchard, 2015).

Objective The objective of this project is to develop a modelling tool to identify the potential to recover energy losses from grinding circuits. More specifically, the tool will provide estimates of the distribution of thermal, sound and vibrational energy sources.

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Methodology

Project definition CanmetMINING and the Canadian Mining Innovation Council (CMIC) proposed a five-step project to complete the energy study of three grinding circuits and to develop an MS Excel tool to allow mining operators to identify thermal, sound and vibrational energy losses from their grinding circuits. The five steps are:

Step 1 - Literature review Step 2 - Data collection at mine #1 Step 3 - Development of an energy model Step 4 - Data collection at mines #2 and #3 Step 5 - Validation of the model

Selection of the mining sites Three criteria were used to identify the mining project partners. The statistical data of the Geological Survey of Canada in 2014 indicate that there are 62 concentrators in Canada, 70% of which are located in Ontario, Quebec and British Columbia. The data also indicate that more than 83% of the ore is processed in circuits using SAG mills and ball mills. The first two selection criteria were therefore established based on the location and the type of grinding equipment. We then established that it would be useful for the validation of the MS Excel tool to include sites with production ranges from 5,000 to 55,000 tonnes per day. To these criteria, we added two qualitative elements to finalize the selection of the mining sites. These were the interest shown by the mining company and the proximity to CanmetMINING’s regional laboratories, knowing that there is one in Sudbury, Ontario, and one in Val-d'Or, Quebec. Based on these criteria, Agnico-Eagle’s Goldex mine in Val-d'Or, Quebec, was identified as the first mine. Its proximity to the Canmet Experimental Mine made it an ideal candidate that could enhance the expertise of the CMIN team and ensure optimal data collection for more remote operations. We planned to have a second mine, from the Sudbury region in Ontario. Not having obtained a confirmation of interest from a mining company in the region at the beginning of the project, we opted for Agnico-Eagle’s Canadian Malartic mine located in Malartic, QC. Lastly, the third choice was New Gold’s New Afton Mine near Kamloops, British Columbia.

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Literature review A literature review was conducted throughout the project to identify scientific publications that could

support the work undertaken in this project and to improve the understanding of the energy

distribution within the grinding process. Resources from the NRCan Library, Laurentian University and

Google Scholar were used for the reference research work. The following keywords were combined to

refine the search results: “comminution, grinding, heat transfer, heat exchange, heat loss, energy

balance, thermodynamic, thermal losses, low grade energy, energy efficiency.”

The search identified several publications that were sorted by reading their abstracts, or by a brief

review of the content of the articles that appeared to be relevant to the project. The selected articles

were then classified into four categories: i) relevant, ii) supporting material, iii) other equipment, and iv)

irrelevant.

Publications included in the “relevant” category are studies that have identified heat or energy losses

within the grinding process. The publications in the “supporting materials” category contain items that

could provide information to support the research and demonstrate the ineffeciency of the grinding

process. Finally, the publications in the “other equipment” category contain articles about studies

similar to this project, but which were done with other types of equipment or in other industries (e.g.,

with a crusher or in cement production).

A summary of the items listed in the “relevant” category is found in Appendix 1.

Thermal energy losses The development of the Excel tool was based on an article by Radziszewski (2013) suggesting using a thermodynamic model to identify energy losses during grinding. The methodology for analyzing thermal energy losses, and the equations and figures described in this section were taken from the publication by Bouchard (2015). Energy losses are characterized using a control volume for the equipment featured in the study and by determining energy inputs and outputs with respect to this control volume (Figure 1) where:

�� is an air or slurry (sl) mass flow;

ℎ is the enthalpy of air or slurry;

𝑇𝐼𝑁𝑎𝑛𝑑 𝑇𝑂𝑈𝑇 are input and output temperatures;

𝑊𝑒𝑙𝑒𝑐 represents the electrical energy supplied to the control volume;

𝑊𝑙𝑜𝑠𝑡 is the energy output (creation of new surfaces, wear of balls and lifter plates, plastic

deformation and mechanical losses), and;

𝑄𝑙𝑜𝑠𝑡 represents the energy dissipated as heat (conduction, convection, radiation and

evaporation).

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Figure 1: Control volume

The energy balance for the control volume is obtained by calculating the sum of incoming and outgoing terms as shown in Equation 1.

Equation1

The term 𝑊𝑒𝑙𝑒𝑐 and the terms on the right-hand side of Equation1 are characterized by operational data,

temperatures and slurry composition. The term 𝑄𝑙𝑜𝑠𝑡 is divided into three components:

Heat dissipated by the electrical and mechanical equipment: It is proportional to the efficiency (η) of the equipment available in the manufacturer’s data sheets. It is calculated based on Equation 2. The Excel tool includes the energy losses at the electric power transformer, the variable speed drive, the electric motor, the speed reducer and the trunnion cooling as shown in Figure 2.

Equation 2

Figure 2: Electrical and mechanical components of a grinding mill

Mass flow

Energy flow

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Heat dissipated through the shell of the grinding mill by convection and radiation: 1. By convection: 𝐐𝐂𝐨𝐧𝐯 = 𝐡𝐒(𝐓𝐬 − 𝐓∞) Equation 3

Where h is the convection coefficient; S is the heat exchange surface; Ts is the surface temperature, and; T∞ is the ambient air temperature.

2. By radiation:

𝐐𝐫𝐚𝐲 = 𝛃𝛆𝐒(𝐓𝟐𝟒 − 𝐓𝟏

𝟒) Equation 4

Where S is the heat exchange surface; β is the Stephan/Boltzmann constant; ε is a coefficient of emissivity, and; T1 and T2 are respectively the temperature of the ambient air and of the grinding mill.

Latent energy absorbed by water during evaporation: This energy is calculated using the right-hand portion of Equation 1.

The estimate of the term 𝑊𝑙𝑜𝑠𝑡 , resulting from the grinding work inside the control volume is more

complicated and it was derived from assumptions reported in the literature. This work comes primarily from 4 sources, namely:

grinding of the ore;

wear of the balls and lifter plates;

plastic deformation, and;

noise and vibrations.

Work 𝑊𝑙𝑜𝑠𝑡 is fundamental to the evaluation of grinding efficiency. According to the literature

(Tromans, 2008 and Fuerstenau, 2002), there are several definitions of efficiency. Since this project is consistent with the experiment by Schellinger (1951), it was decided to use a thermodynamic efficiency, which should range between 10% and 20%. This thermodynamic efficiency includes the grinding efficiency defined by Lowrinson, 1974, which represents from 1% to 5% of the total electrical energy supplied to the system.

The Excel tool identifies the energy loss recovery potential as the sum of terms within 𝑄𝑙𝑜𝑠𝑡 since the

term 𝑊𝑙𝑜𝑠𝑡 corresponds to non-recoverable mechanical work.

Sound energy losses At the very beginning of the project, CMIN hypothesized that sound energy was negligible compared to the total electrical energy supplied to the system. This energy source was, however, measured to validate the hypothesis. The study was entrusted to SoftdB Inc., a firm from Montreal, Quebec, because of their expertise in the matter, and the study was limited to the concentrator at Agnico-Eagle's Goldex Mine. The methodology and results presented in this report are taken from the SoftdB 15-05-12 AG report of September 2015.

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Two approaches were used to measure sound energy. First, a Norsonic "Beamforming" antenna (Figure 3) was used. This instrument, which has a web camera and microphones, can produce a video by scanning the plant’s main equipment. The more intense sound locations are quickly identified (Figure 4) in red in the equipment‘s sound map. These hot spots are the places where sound energy is highest.

Figure 3: Beamforming antenna

Figure 4: Sound map for the secondary grinding mill

Using this information, the equipment emitting a high noise level was identified. These included:

the primary grinding mill (SAG);

the secondary grinding mill; and,

the three (3) screens. The sound level of the other equipment in the grinding circuit, like the ore conveyor, the hydrocyclone, the pumps and the pipes through which the slurry flows is significantly lower, so much so that it was not possible to measure how much they contributed. For this reason, the sound energy of this equipment and accessories has not been evaluated. The loudness of the main equipment was then measured using the ISO-9614 standard methodology: “Determination of sound power levels of noise sources using sound intensity.” This standard is applicable to measurements in an industrial environment. An I-Track sound intensity probe (Figure 5 and Figure 6) was used to scan a closed surface around the equipment. The scanned surface being a known entity, the sound intensity is calculated using Equation 5.

𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 =𝑃𝑜𝑤𝑒𝑟

𝑆𝑢𝑟𝑓𝑎𝑐𝑒=

𝐸𝑛𝑒𝑟𝑔𝑦 /𝑇𝑖𝑚𝑒

𝑆𝑢𝑟𝑓𝑎𝑐𝑒=

𝐹𝑜𝑟𝑐𝑒 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒

𝑇𝑖𝑚𝑒 𝑆𝑢𝑟𝑓𝑎𝑐𝑒=

𝐹𝑜𝑟𝑐𝑒

𝑆𝑢𝑟𝑓𝑎𝑐𝑒

𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒

𝑇𝑖𝑚𝑒= 𝑃𝑟𝑒𝑠𝑠𝑖𝑜𝑛 𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦

Equation 5

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Figure 5: Measuring sound intensity at the screen

Figure 6: Principle of a sound intensity probe

Vibrational energy losses To maintain the same scientific rigour as when evaluating the sound energy losses, vibrational energy was measured to validate the hypothesis that it was negligible in terms of the total electrical energy supplied to the system. The study was entrusted to Montreal, Quebec, firm SoftdB Inc. because of their expertise in the matter, and the study was limited to the concentrator at Agnico-Eagle's Goldex Mine. The methodology and results presented in this report are taken from the SoftdB 15-05-12 AG report (2015). The study provides an order of magnitude for vibrational energy. It takes into account:

the energy transmitted to the main equipment foundations;

the fact that deformation of the rotating structures is not evaluated; and,

three mathematical models being applied to define a vibratory power range. The description of these models is found in Appendix 2.

Considering the prevailing limitations, the equipment studied included:

the primary grinding mill (SAG mill);

the secondary grinding mill (ball mill); and,

the three screens. The measurements were performed using a triaxial accelerometer (PCB-356 A17) and a Concerto data acquisition system. Measurements were carried out during normal operation of the grinding mills.

Data collection CMIN was responsible for establishing a data collection strategy without disrupting the productivity of the plants. Data collection started at the Goldex Mine, which served as a springboard we could use to refine our expertise with grinding circuits and optimize data collection for the other two mining sites.

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Data collection began with the analysis of the grinding process flowsheets to identify critical data for modelling energy losses (Table 1). This analysis allowed us to understand the issues associated with each plant, to define the control volume and to define the equipment to be included in the study. Some of the operating data are available from the plants’ process control systems. Additional data were collected with measuring instruments provided by CMIN. The location of these instruments was discussed with the mine operators. There was a plan to acquire additional data for a period of 10 consecutive days, 24 hours a day at each plant. The operating data range must coincide with the period during which additional data is acquired. Table 1: Critical data for modelling

Description

Ore flow IN (tonnes/h)

Ore temperature IN (oC)

Water flow IN (m3/H)

Water temperature IN (oC)

Motive power (kW)

Efficiency of electrical and mechanical components

Diameter, length and RPM of the grinding mill

Trunnion oil flow (m3/H)

Trunnion temperature IN (oC)

Trunnion temperature OUT (oC)

Slurry temperature OUT (oC)

Speed of moist air exiting the grinding mill

Surface area of opening at the grinding mill’s air outlet

Ambient temperature (oC)

Relative humidity (%)

Composition of the ore

Because of the extreme conditions of turbulence and abrasion at the grinding mills’ inlet and outlets,

sheaths were manufactured to protect the measuring instruments. The sheath design also makes it

easier to install the instruments in the concentrator. Figure 7 shows the assembly of the sheath and the

temperature probe.

CMIN characterized the response time of the temperature sensors when they are inserted into the

protective sheaths. The sheath has a moderating effect in that it cuts peak temperature values. CMIN

measured a response time of about 20 seconds. CMIN considers that the sheath has little effect on the

temperature readings.

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Figure 7: Protection for measuring instruments

Modelling tool The modelling goal is to obtain a tool that allows users to enter typical data from a grinding circuit and to identify with minimum effort the potential for recovering thermal energy losses. To develop this tool, CMIN used Microsoft Excel 2010 software because it offers multiple benefits including the fact that:

it is a widely available software product;

CMIN staff are familiar with the advanced functions and VBA programming of this software;

it can be used to develop an application based on intuitive logic where the user is guided through the process to make the experience as effective as possible.

The Excel model was developed using a six-step process (Figure 8) which allows the user to enter the information required for the study. The process steps are as follows:

Figure 8: 6-step process

Initial configuration

Ore Primary grinding

Secondary grinding

Data Energy balance

Stainless steel cable with

protective hydraulic hose

Temperature probe installed

in the protective cylinder

Cylinder connecting

ring with protective

covering

Galvanized steel

threaded protective

cylinder

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Step 1: Initial configuration This step helps to establish a set of basic parameters such as the name of the equipment, availability, annual hours of operation, cost of energy and language, since this application is bilingual.

Step 2: Defining the composition of the ore This sheet allows the user to enter the composition of the ore and to calculate two parameters,

namely the specific heat and density of the ore. A search tool allows the user to search within

212 different types of minerals.

Step 3: Entering the primary grinding mill’s specifications The user identifies the configuration of the grinding mill`s drive system and enters the technical

specifications of the equipment.

The estimate of the energy loss caused by evaporation of the slurry in the primary grinding mill

is not easy to measure. The user can, therefore, choose whether to include this parameter in the

energy balance.

Step 4: Entering the secondary grinding mill’s specifications The technique for entering the secondary grinding mill’s technical data is practically the same as

for the primary grinding mill.

Step 5: Entering production data Essential data identified in Table 1 must be entered in the model by the user. There are two

strategies for entering historical data:

Minimalist strategy: the user enters a single value per parameter, i.e., an average value.

This strategy assumes that the user has thorough knowledge of the grinding process.

The energy balance will provide a rough estimate of the situation.

Global strategy: the user enters as much information as possible. The data must be

copied directly into the Excel template. The user can create scenarios for different

periods; for example, by providing data for one day, a week, a month, a season or even

a year. The statistical analysis tool could be of great use for validating data consistency.

From a statistical perspective, it is usually appropriate to use a sample with, for each

parameter, at least 30 data that were collected with the same frequency and over the

same period of time.

Step 6: Presentation of the energy balance This step presents all the results of the calculations made by the model. Two charts illustrate the energy recovery potential, one for the primary grinding mill and one for the secondary grinding mill. The user has access to a tool to create energy recovery scenarios. The Excel-based tool to identify energy losses was delivered to CMIC. CMIC will be responsible for its distribution to its members. CMIN has intentionally left this Excel application completely open. The spreadsheets and VBA macros are not password protected. An advanced user is, therefore, at liberty to

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improve certain aspects of this application. The author has chosen to use formulas in the Excel spreadsheets maximizing the use of variable names to make the application more robust. However, some features required the use of macros in VBA code (Visual Basic for Applications). The VBA code is mainly used for display purposes. The modelling tool was used to perform the heat balance of the three concentrators studied in this project. The results are presented in the following sections.

Discussion of results The distribution of thermal energy losses from the three concentrators studied is presented in the tables in Figure 10, Figure 21 and Figure 23. These tables refer to the methodology described in the heat loss

section and to the term 𝑄𝑙𝑜𝑠𝑡 in Equation 1. The term 𝑄𝑙𝑜𝑠𝑡

was divided into eight losses named Q1 to Q8. The discussion of results will provide the following:

The total electrical power supplied to the system (𝑊𝑒𝑙𝑒𝑐) ;

Energy losses from the drive system (Q1 to Q4);

Energy losses from trunnions (Q5);

Energy losses by convection and radiation around the equipment (Q6);

Energy losses through evaporation (Q7);

Energy transmitted to the slurry (Q8); and,

𝑊𝑝𝑟𝑜𝑑, which is the total of the other forms of work or energy losses. The term (𝑊𝑙𝑜𝑠𝑡) in

Equation 1 is equivalent to the term 𝑊𝑝𝑟𝑜𝑑 in figures 10, 21 and 23.

Since the results from the Goldex Mine were used to develop the modelling tool, they are presented first, followed by the results from the Canadian Malartic and New Afton mines. The more general discussion on interpreting results that follows helps to identify the grinding circuits’ recovery potential.

Agnico-Eagle’s Goldex Mine

Description of the plant

The Goldex Mine and processing plant are located in Val-d'Or, Quebec, Canada. Agnico-Eagle operates an underground gold mine with an output of 6,000 t/day and an average of 1.6 g/t. The mine produces slighty in excess of 100,000 ounces of gold per year. Mineral processing at Goldex starts with a two-stage crushing process, followed by a two-stage grinding circuit. The grinding circuit equipment included within this study’s control volume (Figure 9) consists of: a) a semi-autogenous mill (SAG 7.3 x 3.7 m 3,357 kW) and b) a ball mill (5 x 8.2 m 3,357 kW) (Bouchard, 2015). Losses of thermal, sound and vibrational energy were evaluated at the Goldex Mine. The following three sections summarize the main results of these energy losses.

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Figure 9: Grinding circuit - Goldex Mine

Thermal energy losses

A summary of the distribution of energy losses is presented in Figure 10. The total electrical energy supplied to the system is 6,450 kW. In this particular case, the 2 grinding mills have almost identical drive systems, since they have the same type of electrical transformers (Q1) and identical motors (Q3) and speed reducers (Q4). The secondary grinding mill’s drive system differs only in that it has no speed control (Q2). The total energy losses from the two grinding mills' drive systems, components Q1 to Q4, are 539 kilowatts or 8.4% of the total electrical energy supplied to the system. This low temperature energy is dissipated into the plant’s ambient air and is used passively to heat the building in winter. Note that an estimate of the concentrator heating load indicates that the coldest winter months would require about 500 kW. Energy losses at the trunnions (Q5)—less than 100 kW—represent less than 1.5% of the total electrical energy supplied to the system. However, this is the source of thermal energy with the highest temperature (48oC) suggesting the potential for energy recovery.

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Figure 10: Energy distribution for the Goldex mine grinding mills

Energy losses through the grinding mills’ shell caused by convection and radiation into the plant’s ambient air (Q6) are not very high. They are of the order of about 56 kW. Convection and radiation are directly affected by the temperature of the slurry inside the grinding mill and the temperature in the plant. The greater the gap, the greater this loss will be. However, this energy loss is not significant, accounting for less than 1% of the total electrical energy supplied to the system. Energy loss from evaporation (Q7) is probably the most difficult form of energy loss to measure. It requires measuring the temperature (around 20oC) and the relative humidity (about 43%) of the air next to the grinding mills, the air flow from the concentrator to the inside the grinding mill, and finally the temperature of this air stream as it exits the grinding mill. The last 2 parameters are difficult and even impossible to measure. We had to assume that the air exiting the grinding mill is 100% saturated and we estimated the air flow as best we could. Since the primary grinding mill is fed using a conveyor and an ore chute, there is a greater possibility of evaporation here than at the secondary grinding mill which is supplied through a sealed tube. Air infiltration must be more difficult at the trommel. Evaporation at the primary and secondary grinding mills is 8.3% and 1.1% respectively. We consider that the evaporation value for the primary grinding mill is too high and should be revised so that it is similar to that of the secondary grinding mill. Nonetheless, this energy loss is not significant. The energy transmitted to the slurry (Q8) is the most significant of all types of energy in the study in the case of both the primary and the secondary grinding mills, respectively representing 69% and 82% of the total energy supplied to the system. The temperature of the slurry used in the primary and secondary grinding mills is respectively 35oC and 38oC. To evaluate this energy, some factors need to be taken into consideration, namely the temperature of the ore or slurry entering the grinding mill and the temperature at which water is injected. The ore is brought to the first grinding mill by a conveyor. The temperature of the ore varies depending on the amount of time it has spent in the storage dome near the plant. We had to determine a method for measuring the temperature of the ore and allow for some adjustments in the calculations to properly estimate this energy transfer. In addition, a variation in the

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temperature of the water injected at the inlet of the grinding mills has a significant impact on the transmission of energy to the slurry. Undoubtedly, the energy transmitted to the slurry is significant. Energy sources identified as Q1 to Q8 represent low temperature thermal energy losses. Water, slurry, oil and air temperatures vary from 10oC to 48oC. While some of these forms of energy technically have some recovery potential, it is recommended that a techno-economic assessment of such projects be evaluated. An important component of the results table is the term 𝑊𝑝𝑟𝑜𝑑 . This is the work done by the grinding

mill. This work is not recoverable. In our definition 𝑊𝑝𝑟𝑜𝑑 consists of the following elements:

the grinding of the ore, which represents from 1% to 5% of 𝑊𝑒𝑙𝑒𝑐 (Lowrinson, 1974);

wear on balls and lifter plates;

plastic deformation, and;

noise and vibrations. According to Schellinger (1951), the thermodynamic efficiency of the system being studied should range from 10% to 20%. The thermodynamic efficiency of the primary grinding mill is 12% (Figure 10), which is within the expected value range. As discussed above, a downward revision of the energy lost to evaporation at the primary grinding mill would be offset by an identical increase in efficiency. If evaporation is reduced to 1%, 𝑊𝑝𝑟𝑜𝑑 will increase to 19% because the principle of conservation of

energy must be respected. The efficiency of the secondary grinding mill is 7% (Figure 10). More energy is transmitted to the slurry. The terms 𝑊𝑝𝑟𝑜𝑑 and Q8 are very sensitive to temperature differences

measured on site. These temperatures were difficult to measure due to the physical constraints of the process. Nevertheless, the data clearly present the reality of the grinding process.

Sound energy losses

Sound energy was only measured at one of the three concentrators, the one at the Goldex Mine. The results are from the SoftdB Inc. report 15-05-12 AG. Figure 11 to Figure 19 show the zones corresponding to the sound energy sources in the grinding circuit namely, the primary grinding mill, the secondary grinding mill and the screens. These images were captured using the "Beamforming" antenna. Noise sources from several other pieces of equipment were measured, but all are negligible, with the noise from the grinding mills dominating. Table 2 shows the results of acoustic measurements on key pieces of equipment. The secondary grinding mill has the highest sound power level, followed by the screen and the SAG. An octave-band analysis shows that the noise comes mainly from low frequencies between 31.5 and 500 Hz. The overall sound power generated by the SAG, the secondary grinding mill and one screen is 33.9 W, or 50.5 W if we include all three screens. Factoring in the uncertainty found in the measurements as described in the ISO-9614 standard and presented in Table 3, the sound power would be between 25 and 73 W. The total electrical power supplied to these five pieces of equipment is more than 6,450 kW. These results unequivocally demonstrate that sound energy is a negligible source.

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Figure 11: Sound under the primary grinding mill

Figure 12: Sound above the primary grinding mill

Figure 13: Sound at the trommel and at the primary grinding mill motor’s ventilation unit

Figure 14: Sound at the primary grinding mill drive set-up

16

Figure 15: Sound at the secondary grinding mill

Figure 16: Sound at the secondary grinding mill motor

Figure 17: Sound at the secondary grinding mill’s drive system

Figure 18: Sound from a screen motor

Figure 19: Noise from a screen

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Table 2: Measured acoustic power (dB and W)

Level in octave bands (dB)

Equipment Description Global (W)

Global (dB)

31.5 Hz 63 Hz 125 Hz 250 Hz 500 Hz 1000 Hz 2000 Hz 4000 Hz 8000 Hz

Screen 8.3 129.2 127.0 125.0 111.6 105.6 101.6 94.3 93.8 91.6 85.8

SAG mill Pump 0.0 106.0 96.1 99.8 99.7 99.0 99.0 87.6 78.7 14.2 47.3

Pump belt 0.0 101.1 99.4 99.4 90.2 86.5 92.3 83.9 77.7 71.6 48.4

Motor 1.1 120.5 114.0 116.2 114.2 109.6 108.4 106.5 85.3 82.4 68.5

SAG 2.9 124.6 114.6 119.8 117.2 115.0 118.0 111.9 102.4 94.2 82.2

Transmission 3.1 125.0 116.8 121.0 115.4 110.4 119.3 108.9 93.5 82.8 70.9

Total 7.2 128.6 120.1 124.2 120.6 117.2 121.9 114.5 103.0 94.8 82.7

Ball mill Motor 4.2 126.2 115.6 121.9 119.1 112.3 121.0 106.1 89.7 79.6 79.5

Ball mill 13.5 131.3 121.0 128.8 124.2 121.3 117.6 107.2 96.3 85.8 72.2

Transmission 0.7 118.4 107.7 111.2 111.4 103.2 114.7 107.0 94.5 76.9 70.5

Total 18.4 132.7 122.2 129.7 125.6 121.8 123.3 111.5 99.0 87.2 80.7

Total 33.9 135.3 128.8 131.8 126.9 132.2 125.7 116.3 104.8 97.0 88.4

Table 3: Maximum values of the standard deviation for the reproducibility of the sound level measurements

Frequency band (Hz)

Maximum values*

50 to 160 2.0

200 to 315 1.5

400 to 5000 1

6300 2.0

Total 1.6

*According to the ISO-9614 standard

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Vibrational energy losses

As for the sound energy, the evaluation of vibrational energy losses was limited to the concentrator at the Goldex mine. The study was limited to the transfer of vibrational energy to the concrete foundations of the three main pieces of equipment, namely the primary and secondary grinding mills and the screens. The physical characteristics and mechanical properties of the foundations are presented in Table 4 and Table 5. Table 4: Dimensions of the pillars

Length (m) x axis

Width (m) y axis

Depth (m)* z axis

Front pillar 0.760 3.200 10.56

Rear pillar 0.760 3.200 10.56

Motor pillar 3.285 1.880 6.700

Slab under screens 7.876 15.549 0.152 *The front and rear pillars are trapezoidal shapes. The length and width represent the dimensions at the surface of the pillar, where accelerometers were installed to take the measurements. The actual depth of the pillar, 7.345 m was adjusted so that the calculation of the actual volume of the foundation is accurate. The motor foundation is cube-shaped and does not require correction.

Table 5: Mechanical properties*

Elasticity modulus 5.00 E+10 Pa

Typical damping 0.1

Estimated density of the concrete

2200 kg/m3

*Reference: SoftdB report

SoftdB defined three mathematical methods for estimating the vibrational energy transmitted to the foundation of the equipment. The three methods are:

Statistical Energy Analysis (SEA)

Theory of linear elasticity

Instantaneous power of a vibrating mass The method based on the instantaneous power of a vibrating mass overestimates the vibrational power because it considers the vibrating mass as a rigid body that moves as a block. It is interpreted as an upper limit value. The results for vibrational energy losses for the three pieces of equipment calculated using the model of the instantaneous power of a vibrating mass are presented in Table 6. Table 6: Estimate of the vibrational energy (W)

Equipment Front pillar

Back pillar Motor Floor Total

Primary grinding mill 115 176 264 555

Secondary grinding mill 220 38 288 546

Screen #1 21 21

Screen #2 21 21

Screen #3 20 20

Total 335 214 552 62 1,163

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The sum of the power transmitted to all equipment foundations using the data to estimate the maximum vibrational energy totals 1,163 W. Since the total electrical energy supplied to the system is more than 6,450 kW we can confirm with certainty, like Schellinger (1951), that vibrational energy is negligible. As expected, this form of energy will not be evaluated in the other two mining sites participating in this project and will not be incorporated into the modelling tool for energy losses from the grinding circuits.

Agnico-Eagle and Yamana Gold, Canadian Malartic mine

Description of the plant

The Canadian Malartic mine and processing plant are located in Malartic, Quebec, in Canada, 25 km west of Val-d'Or, Quebec. In partnership with Yamana Gold, Agnico-Eagle operates an open pit gold mine with 55,000 t/day and an average of 1.2 g/t that produces approximatively 580,000 ounces of gold and 600,000 ounces of silver per year. The equipment of the grinding circuit forming part of the control volume is illustrated in Figure 20. The control volume includes: a) a semi-autogenous mill (SAG 30 x 7 m, 14.5 MW), and b) three ball mills configured in two stages, namely: two secondary grinding mills in parallel (7.3 x 22 m, 11 MW) followed by a tertiary grinding mill (7.3 x 22 m, 11 MW). A full description of the grinding circuit of the Canadian Malartic mine is available in the following publication: Technical Report on the mineral resource and mineral reserve estimates for the Canadian Malartic property, August 2014.

Figure 20: Grinding circuit - Canadian Malartic mine

Thermal energy losses

A summary of the distribution of the energy losses is presented in Figure 21. The total electrical energy supplied to the system is 37,882 kilowatts, or 14,882 kW to the primary grinding mill and 11,500 kW to each of the two secondary grinding mills. Because of the simplicity of the Excel tool developed as part of this project, the two secondary grinding mills operating in parallel were treated as one.

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Also, data from the tertiary grinding mill are available but have not been included in the calculation tool. The primary grinding mill has a direct drive set-up with no speed reducer. The Q4 value is therefore 0 kW. The two secondary grinding mills have a similar drive set-up, i.e. they have no speed controller (Q2 = 0 kW). Total energy losses from the drive set-up, components Q1 to Q4, are 2,811 kilowatts or 7.4% of the total electrical energy supplied to the system. This low temperature energy is dissipated into the ambient air of the plant and provides a passive source for heating the building in winter. Note that the estimate of the heating load of the concentrator was not made for this plant.

Figure 21: Energy distribution for the Canadian Malartic mine grinding mills

Energy losses at the trunnions of the two grinding mills (Q5) account for just over 1% of the total electrical energy supplied to the system, or 443 kilowatts. This is the source of thermal energy with the highest temperature at 48oC suggesting potential for recovery. Energy losses through the grinding mills’ shell caused by convection and radiation into the plant’s ambient air (Q6) are not very high at 207 kW. However, this energy loss is not significant, as it accounts for less than 1% of the total electrical energy supplied to the system. Evaporation (Q7) was not easy to measure. We have set this energy loss at 1% of the total electrical energy supplied to the system. We made the same assumption as for the Goldex mine. Nonetheless, this energy loss is not significant. The energy transmitted to the slurry (Q8) is the most significant of all types of energy in the study for both the primary grinding mill and the secondary grinding mills, respectively representing 86% and 78% of the total energy supplied to the system. The temperatures of the slurry at the outlet of the primary and secondary grinding mills are respectively 48oC and 36oC, slightly warmer than at the Goldex mine. The thermodynamic efficiency for the primary grinding mill is 4% and 12% for the secondary grinding mill. Efficiency for the primary grinding mill is particularly weak. We infer that it would be appropriate to

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review the ore, water, and slurry temperatures. The performance of the secondary grinding mills is acceptable according to the values presented in Schellinger (1951).

New Gold, New Afton Mine

Description of the plant

The New Afton Mine and processing plant are located 10 km south of Kamloops, BC, in Canada. New Gold Inc operates an underground copper and gold mine with an output of 15,000 t/day. They produce over 85,000 ounces of gold and 75x106 pounds of copper per year. The equipment of the grinding circuit forming part of the control volume is illustrated in Figure 22. The control volume includes: a) a semi-autogenous mill (SAG 8.5 x 4 m, 5 220 kW) and b) a ball mill (5.5 x 10 m, 5 220 kW).

Figure 22: Grinding circuit - New Afton Mine

Thermal energy losses

A summary of the distribution of the energy losses is presented in Figure 23. The total electrical energy supplied to the system is 10,987 kilowatts, i.e., 5,431 kW to the primary grinding mill and 5,556 kW to the secondary grinding mill. Both grinding mills have conventional drive systems. Energy losses from the

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drive system, components Q1 to Q4, total 883 kW or 8.0% of the total electrical energy supplied to the system. This low temperature energy is dissipated into the plant’s ambient air and is used to heat the building passively in winter. Note that there is no estimate of the concentrator’s heating load for this plant.

Figure 23: Energy distribution at the New Afton mine grinding mills

Energy losses at the trunnions (Q5) of the two grinding mills need to be revised because the differences in the temperature of oil at the inlet and at the outlet appear low compared with data from the other mines. However, calculating these energy losses gives a result of the same order of magnitude as the other mines in the study, i.e., around 1% of the total electrical energy supplied to the system (129 kW). The maximum oil temperature is 37oC. Energy losses through the shell of the grinding mills caused by convection and radiation into the plant’s ambient air (Q6) and energy losses through evaporation (Q7) are the lowest of the three grinding circuits studied. The total is only 39 kW. Ore, water and slurry temperatures are significantly lower at the New Afton Mine. The parameter with the greatest impact is the temperature of water entering the grinding mills. The process water comes from an outside source with a temperature of 5oC, while the process water at Goldex comes from a tank with a temperature of 23oC. Since energy losses identified by Q6 and Q7 are minimal and the principle of conservation of energy must be applied, this energy must be transferred to the slurry. The energy transmitted to the slurry (Q8) is the most significant of all types of energy in the study for both the primary grinding mill and the secondary grinding mills. Respectively, it represents 80% and 81% of the total energy supplied to the system. As with the other three grinding circuits, the energy transmitted to the slurry is the most significant.

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Energy sources Q1 to Q8 at the New Afton mine concentrator are low temperature thermal energy losses. Water, slurry, oil and air temperatures vary from 11oC to 43oC. The recovery of some of these energy forms is technically possible, but it is recommended that a techno-economic assessment of such projects be evaluated. The thermodynamic efficiency of the primary and secondary grinding mills is 9% and 10% respectively. The efficiency of the grinding mills is comparable with the values reported by Schellinger (1951). Note that chemical additives are used at the New Afton grinding circuit. The term 𝑊𝑝𝑟𝑜𝑑 includes

approximately 200 kW from the exothermic reaction of lime with water.

Identifying energy recovery potential Theoretically, all the thermal energy losses identified in this project can be recovered and used as thermal energy or even to produce electricity. The energy recovered and reused as thermal energy generally has efficiencies of around 75%. This would be for heat exchanger applications. Table 7 summarizes the available energy and the slurry and water temperatures. However, the low temperatures involved will have a significant impact on the techno-economic feasibility of these solutions. Another approach, namely, generating electricity, is not simple. The Carnot efficiency calculation (Table 7), assuming a cold water source of 10oC, provides a good illustration of this. The efficiencies are very low for the temperature ranges encountered in the grinding circuits included in the study. Technological solutions must be found to improve the Carnot efficiency, which is far from acceptable. Radziszewski (2013) presents some ideas leading to improved Carnot efficiency. These include dry milling technology, which could be an avenue to explore since it involves temperatures of around 85oC, which are, therefore, more favourable for energy recovery. Table 7: Recovery potential

Heat losses Q1 to Q8 (kW)

Energy transmitted to the slurry Q8 (kW)

Maximum temperatures (oC)

Carnot efficiency

Water Slurry

Goldex 5,072 4,116 20 38 9.00%

Malartic 34,623 30,739 26 36 8.41%

New Afton 9,917 8,865 6 18 2.75%

Total 49,611 43,720

Limitations of the study Study of the SAG and ball mills Given the variety of configurations among the 62 plants in Canada, the model was deliberately limited to the study of primary and secondary grinding mills to maintain an acceptable level of simplicity. These two types of equipment, usually SAG mills and ball mills, process more than 83% of the ore in Canada (source: Geological Survey of Canada, 2014).

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Acquiring temperature data Ore, water and slurry temperatures at the inlet and outlet of the grinding mills are not included among the parameters taken into account by the plants’ control systems. The acquisition of these additional data required a lot of effort. Analyzing the grinding circuits in conjunction with the operators was essential to determine the location of the test points. We soon realized it was impossible to put the temperature sensors where we wanted. They had to be installed as close as possible to the target test points. There were additional calculations and assumptions made to evaluate some parameters required by the modelling tool and to get a balanced energy distribution. For example, we added 1.5oC to the water inlet temperature at the primary grinding mill and increased the difference in temperature of the slurry in the secondary grinding mill by 2.3oC at the New Afton mine to achieve an energy balance. These temperature adjustments were required since, after analyzing the situation, we realized that the water temperature reading at the entrance of the main grinding mill had been taken 2 km upstream of the plant and the slurry temperature probes at the secondary grinding mill seemed to have been affected by a cool air stream. These temperature adjustments have helped decrease the percentage of energy transmitted to the slurry from 88% to 80%, demonstrating at the same time how sensitive the energy distribution is to temperature measurements. Intermediate calculations The modelling tool developed as part of this project represents a simplified version of a grinding circuit. The identification of energy losses requires several parameters from data archived by the plants’ control systems. Nevertheless, some of these parameters are not available due to the complexity of the grinding process. According to available data, calculations about slurry density, mass flow or even unit conversions may be required. The user of the modelling tool will need to make intermediate calculations to provide the required parameters for the model. An in-depth understanding of the grinding circuit is an asset. Non-recoverable work The modelling tool cannot discern the source of work performed within the control volume. Thus, the energy for grinding the ore, wearing down the balls and the lifter plates and for the plastic deformation of grinding mill shell is all included in the term 𝑊𝑝𝑟𝑜𝑑.

Convection, radiation and evaporation Calculating convection and radiation only takes into consideration the cylindrical portion of the grinding mills and assumes that they have a uniform temperature that is equal to that of the slurry. While modelling could be improved, the contribution of these energy losses is low compared with the increased complexity for whoever is using the tool. Evaporation is difficult to measure. The calculation tool has the option of ignoring this loss of energy. In studying the three concentrators, we deliberately set the calculation parameters at 1% evaporation, except for the primary grinding mill at the Goldex Mine, to illustrate the effect of this loss of energy on the global distribution. To improve the estimate of evaporative losses, the measurement technology would need to be reviewed. The case study of the Goldex Mine and the Canadian Malartic Mine shows circuits in which the slurry temperature is relatively warm. The New Afton Mine is an example in which the temperature of the slurry is cooler. These observations show that:

water temperature significantly influences the slurry temperature range at the outlet of the grinding mills;

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energy recovery does not change the thermodynamic efficiency of the grinding equipment. The percentage of energy transmitted to the slurry remains high in all three cases;

the more the recovery of the energy transmitted to the slurry is significant, the more the grinding cycle temperatures should stabilize within a temperature range similar to that of the New Afton Mine.

energy recovery will lower the operating temperatures of the grinding mills. This drop in temperature could have a positive or a negative impact on ball and lifter plate wear and on recovery following flotation. Since the grinding processes have unique characteristics, the impact of such an action must be evaluated in advance (Radziszewski, 2013).

General energy distribution Considering the following:

the difficulty in obtaining reliable measurements

the sensitivity of the temperature data

the complexity and variety of the processes

the productivity range (5,000 t/d to 55,000 t/d for the cases studied in this project)

the varied mineral compositions

the amount of work required in order to integrate all of the parameters into the calculation tool

the fact that all seven grinding mills have a substantially similar energy distribution

CMIN suggests applying an overall energy distribution as an initial approximation of energy losses. This is the simplest way to express energy losses since it only requires one piece of information about the

grinding process, i.e., the total power supplied to the system (𝑊𝑒𝑙𝑒𝑐) . This value then simply has to be multiplied by the average distribution percentages obtained in the course of the project, namely:

1. 8% for energy losses related to the drive system 2. 5% for energy losses related to the grinding mill 3. 77% for energy transmitted to the slurry

The sum of these three categories of thermal energy represents 90% of the total electrical energy supplied to the system. The remaining 10% of the energy is associated with system-related non-recoverable work (𝑊𝑝𝑟𝑜𝑑).

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Conclusions CMIN, in collaboration with CMIC, Metso, Laval University, Agnico-Eagle and New Gold, has developed an analytical tool to identify thermal energy losses from a wet grinding circuit. The tool was developed using Excel software. Initially, energy losses related to the drive system are characterized by the efficiency of the equipment. Then, energy losses from convection, radiation, evaporation and energy transmitted to the slurry are calculated using a thermodynamic model. The remaining energy is assigned to the grinding work itself, providing the last element of the grinding circuit’s energy balance. Using this calculation tool requires in-depth knowledge of the grinding circuit, having access to production data, measuring slurry and process water temperatures, and the ability to perform intermediate calculations to populate the spreadsheets with essential parameters. To validate the modelling of energy losses, CMIN collected production and temperature data for the concentrators of the three mining companies involved in the project. The mines in question are Agnico-Eagle’s Goldex Mine and Canadian Malartic, both in Quebec, and New Gold’s New Afton Mine in British Columbia. A total of seven grinding mills were studied. The study shows that, on average, 77% of the electrical energy supplied to the grinding circuit is transmitted to the slurry, 10% is used for the grinding work, 8% is lost through the drive system and about 5 % of the energy is transmitted to the ambient air around the grinding mills. Therefore, about 90% of the thermal energy is potentially recoverable. CMIN suggests using this distribution model as a simple way to estimate energy losses. Only one production datum is required: the electric power of the grinding mills. Recovering thermal energy is not simple since the temperatures of the 7 grinding mills studied range from 18oC to 38oC. This is a difficult energy source to recover because of the low temperatures involved. Trying to generate electrical energy is not worthwhile with a Carnot thermal efficiency of barely 5%. At this stage in the process of identifying a grinding circuit’s energy losses, research should continue into technical solutions for low grade heat recovery. Initially, we suggest a literature survey to take stock of the current situation. The impact of energy recovery must be analyzed carefully before any action is taken in this respect. For example, what effect will lowering the grinding mills’ operating temperature have on ball and lifter plate wear? The impact could be positive if it increases the life of the consumables or negative if it accelerates corrosion degradation. We can ask the same question about the performance of the flotation process, in which chemicals react differently depending on the temperature. Since grinding processes have unique characteristics, the impact of such an action must be evaluated in advance (Radziszewski, 2013). Note that CMIN also assessed sound and vibrational energy losses. A series of measurements taken at the concentrator at the Goldex Mine led to the conclusion that these two forms of energy losses were negligible. The modelling tool developed for the project does not allow one to distinguish between the types of the work carried out within the study’s control volume. The work entailed in grinding, grinding media destruction and the plastic deformation of structural elements forms a single parameter, i.e., work produced.

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In most concentrators in Canada, thermal energy losses are used to passively heat buildings at least six months a year. Any energy recovery strategy must consider this critical need. The experience we acquired in the course of this case study leads us to suggest that work that focuses on energy recovery would support what is a priority issue for the industry and CMIN. Having identified the energy balance of two types of grinding mills, one possibility would be to focus efforts on identifying cutting-edge technologies capable of efficiently recovering energy at low temperatures. This is an important step for validating the impact of energy recovery. At the same time, a predictive modelling analysis could attempt to estimate the operating temperature in an energy recovery situation so as to estimate what impact reducing the grinding mills’ operating temperature would have on the rest of the mineral process. It might also be worthwhile examining the energy efficiency of different types of grinding mills. CMIC has already identified five grinding technologies that merit particular attention. These are:

a. HPGR in closed-circuit with air classifiers.

b. Vertical roller mills

c. Conjugate Anvil Hammer Mill (CAHM)

d. HPGR-stirred mill combined

e. Selfrag

It would also be interesting to explore new grinding process control strategies in collaboration with Dr. J. Bouchard from Laval University, where they are in the process of developing a grinding simulator with predictive controls. The approach is entirely new and would benefit from the contribution of the industry and the expertise of CMIN.

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Appendix 1: Literature Review

Energy recovery potential in comminution processes

Radziszewski, P. 2013, "Energy recovery potential in comminution processes", Minerals Engineering, vol.

46-47, pp. 83-88.

Summary The energy required to create new surfaces corresponds to less than 1% of the energy consumed by a

mill. Although comminution is a very inefficient process in the creation of new surfaces, it is very

effective at producing heat. This article aims to examine the distribution of thermal energy in the

grinding circuit to quantify the amount that could be recovered in order to increase the efficiency of the

grinding process.

It has been shown that 56% of the energy used in the control volume of a grinding circuit is lost to the

environment as heat, 43% becomes trapped in the slurry (as heat), while 1 % of the energy is used in the

production of new surfaces.

The efficiency of energy recovery defined by the Carnot cycle is proportional to the temperature

difference between the hot source (the mill discharge) and the cool sink. Thus three methods have been

suggested to improve the energy recovery by increasing the mill discharge temperature, either by: 1)

reducing heat losses to the environment through insulation and evaporation reduction, 2) increasing the

control volume to include all grinding stages in the circuit, and 3) a combination of both techniques. The

latter has increased the energy efficiency of the grinding circuit to 11%, the largest increase of all the

proposed techniques.

However, there are technical and / or operational limitations that could present obstacles to the

realization of this work.

Energy transfer and conversion during comminution and mechanical activation

Tkáčová, K., Heegn, H. & Števulová, N. 1993, "Energy transfer and conversion during comminution and

mechanical activation", International Journal of Mineral Processing, vol. 40, no. 1, pp. 17-31.

Summary The article provides a summary of research aimed at understanding the principles governing structural

and energetic changes in fine grinding processes.

It was stated that Rumpf (1973) determined that ~50% of the work performed during single particle

breakage could be retained in the form of strain energy. Alternatively, Schellinger (1952) used a

calorimetric mill to assess the energy conversion during grinding. The energy retained in the particles

29

and in the grinding tools corresponded to 10 to 20% of the work utilized and was determined by the

difference between the work supplied and the heat released during grinding. Thus it was shown that 80

to 90% of the work supplied in a grinding process is converted to heat.

Energy transfer in two groups of mills was also investigated: i) direct particle impact mills, and ii) loose

media mills. The work of Heegn (1989) showed that 3 to 7 cycles were required to transfer a given

amount of energy to particles in direct impact mills whereas 102 to 104 cycles were needed in those with

loose media. Thus the rate of stress was greater in impact mills. It was also stated that the amount of

energy accumulated in the particles is influenced by the rate of stress and the efficiency of energy

transfer in comminution processes.

Rumpf, H., 1973. Physical aspects of comminution - a new formulation of a law of comminution . Powder

Technol., 7: 148-159.

Schellinger, A.K., 1952. Surface energy of solids an its calorimetric determination in some minerals. Min.

Eng., 4: 369-374

Heegn, H., 1989. On the connection between ultrafine grinding and mechanical activation of minerals.

Aufbereitungs-Technik, 30: 635-642

Chemical engineering analysis of fine grinding phenomenon and process

Jimbo, G. 1992, "Chemical Engineering Analysis of Fine Grinding Phenomenon and Process", Journal of

Chemical Engineering of Japan, vol. 25, no. 2, pp. 117-127.

Summary An energy transfer model for a grinding process was presented whereby energy was transmitted in five

consecutive steps as follows: 1) to the motor, 2) to the mill, 3) to the grinding media, 4) to the powder

bed, and 5) to the individual particles. The overall efficiency of the energy transfer mechanism was

presented as a function of the efficiency of each individual step. The overall energy efficiency of the

grinding process cannot be measured experimentally because of the difficulty in determining i) the

energy absorbed by each particle, and ii) the efficiency of energy transfer from the grinding media to the

powder bed. However, the total energy efficiency of the grinding process can be estimated with the use

of an energy balance and theoretical calculations to assess the energy required to fracture particles

based on the increase in surface area.

The model was expanded, based on information from other publications (Rumpf, 1974; Bown, 1966), to

illustrate the energy flows in the grinding process and it was shown that heat was produced at each of

the five energy transfer steps. The model showed that inefficiencies in the grinding process resulted in

heat produced from:

mechanical losses,

grinding media impact with other media or with mill wall,

30

elastic and plastic deformation,

changes in crystalline structure of particles, and

chemical, mechanochemical, physiochemical, physical phenomena.

It was stated that at least 90% of the energy input in a grinding process is converted to heat and that

95% or more of the energy consumed is wasted.

Rumpf, H., J. soc: Powder Technology (in Japanese), 11(1), 18 (1974). Bown, R. W.: Trans. Instn. Min. Metall., Sect C: Mineral Porcess Extr. Metall., 75, C173-180 (1966).

Modeling of energy loss to the environment from a grinding mill. Part I: Motivation, literature

survey and pilot plant measurements

Kapakyulu, E. & Moys, M.H. 2007, "Modeling of energy loss to the environment from a grinding mill.

Part I: Motivation, literature survey and pilot plant measurements", Minerals Engineering, vol. 20, no. 7,

pp. 646-652.

Modeling of energy loss to the environment from a grinding mill. Part II: Modeling the overall

heat transfer coefficient

Kapakyulu, E. & Moys, M.H. 2007, "Modeling of energy loss to the environment from a grinding mill.

Part II: Modeling the overall heat transfer coefficient", Minerals Engineering, vol. 20, no. 7, pp. 653-661.

Summary A laboratory scale batch mill of 0.54 m internal diameter and 0.4 m internal length was used to

determine the energy loss to the environment. The load inside the mill consisted of steel balls only; the

introduction of slurry to the mill load would have reduced the energy transfer rate in the mill due to

energy used to evaporate water.

Temperature measurements of the load, the air above the load, the liner, the mill shell, and ambient air

were obtained with precision thermistors. A calibrated torque load beam was used to measure the mill

power draw, whereas sound energy was measured using a sound and vibration analyzer.

Experiments were conducted at various mill speeds ranging from 30% to 105% of critical speed, as well

as various mill load volumes from 20% to 40%. The observed temperatures and power were higher for

the trials with higher mill speeds for a given mill load volume. It was also observed that the

temperatures increased with increasing mill load volume. Furthermore, a temperature profile revealed

that the highest temperature corresponded to that of the load, followed by: the air above the load, the

liner, the shell, then the ambient temperature. The data also revealed that proportion of energy

allocated to sound was negligible.

Experiments were also conducted to determine the heat transfer coefficient of the steel balls with air, as

well as with the mill liner, which were subsequently used to establish the overall heat transfer

coefficient of the shell.

31

Parallel paths for heat transfer from the mill load to the environment were presented; heat could be

transferred directly from the load to the liner or from the load to the air above the load then to the

liner. Subsequently heat transfer occurred from the liner to the shell, then to the environment. A

statistical analysis was used with the data presented in Part I of the article, to develop a model for the

overall heat transfer coefficient for the mill as a function of mill speed and load volume for the different

paths through which thermal energy is transferred from the mill to the environment.

The results showed that the greatest heat transfer coefficient corresponded to that of the load to air

above the load, followed by that of the air to liner, then the load to liner. It was also shown that all heat

transfer coefficients increased with load volume and mill speed. The overall heat transfer coefficient of

the mill also increased with mill speed and load volume, and ranged from 14.4 to 21 W/m2K. The work

also determined that 55 to 65% of the energy transferred from the load to the environment followed

the path via the air above the load.

Measurement of load behaviour in an industrial grinding mill

Van Nierop, M.A. and Moys, M.H., 1997. Measurement of load behaviour in an industrial grinding mill.

Control Engineering Practice, 5(2), pp.257-262.

Summary In an effort to enhance understanding of load behaviour in mills to improve control, probes were

installed in mill liner bolts to measure conductivity, temperature and movement. The probes were

installed at three locations along the mill: at the feed end, the middle, and at the discharge end.

The conductivity data gathered allowed calculating the angle of orientation of the load as well as the

load volume. The temperature data showed that there was an increase of 6°C from the feed end to the

discharge end of the mill. This information indicated that the load in the mill was not perfectly mixed.

Furthermore, it was shown that when the fresh feed to the mill was stopped the temperature at all

measurement locations first decreased in the absence of cooler material, but then increased.

It was also shown that the power consumption from the mill decreased when the mass was lower.

Conversely, when the feed was restarted the power increased but all the temperature values decreased

initially but slightly increased until a plateau was reached indicating a steady state. Subsequently the mill

was shut down and it was stated that all the temperatures decreased slowly but that the ends of the mill

cooled quicker than the middle, indicating that there is a greater heat transfer at the ends due to the

larger surface area.

32

The energy efficiency of ball milling in comminution

Fuerstenau, D.W. and Abouzeid, A.Z., 2002. The energy efficiency of ball milling in comminution.

International Journal of Mineral Processing, 67(1), pp.161-185.

Summary Several methods used for estimating comminution energy efficiency were presented. The methods were

classified within two categories: i) energy efficiency based on surface area, and ii) energy efficiency

based on particle size distribution.

One of the methods presented amongst those based on surface area consisted of the thermodynamic

efficiency, whereby the heat lost from a mill and the energy input to mill were measured; it was

assumed that the difference between these values corresponded to the amount of energy used to

create new surface. The thermodynamic efficiency of the grinding process was then defined as the ratio

of the energy used to create new surface, to the energy input to the mill. Experiments were conducted

using quartz and the thermodynamic efficiency values ranged between 10 and 19% for various mill

loading. Conversion of these values in terms of the surface tension of quartz showed that the efficiency

of milling was less than 0.5%. Efficiency values relating to the surface tension of quartz from various

studies were also compared and ranged from 0.15 to 1.7%.

A second definition of energy efficiency for milling was presented based on fracture physics, where the

efficiency of a ball mill ranged from 1.5 to 12%.

Energy efficiency based on surface area was also presented as the ratio of specific energy used in single-

particle breakage to that used by a comminution device to produce the same surface in the product. The

efficiency for grinding quartz using this approach corresponded to 15%, thus 85% of the energy input to

the mill is lost.

Tests were conducted to measure the energy required to crush various materials of different feed and

product particle size. Energy curves were developed from crushing tests conducted with uniform-size

feed particles to produce a product with: i) a natural particle size distribution, and ii) a uniform particle

size. Actual energy consumption of various comminution equipment was compared to these energy

curves to assess their energy efficiency, which ranged from ~1% to 88%. The highest efficiency value

corresponded to that of a roll crusher whereas those for commonly used mills ranged from 7 to 32%

based on this method.

Results from another study were presented whereby the energy efficiency of ball mills was determined

based on the energy used to crush single particles to the same size distribution as in the mill. The energy

efficiency of ball mills was estimated at 10 to 20% using this approach. Conversely, an alternative

method for defining energy efficiency was proposed based on interparticle crushing where the efficiency

of balls mills corresponded to 40 to 60%.

Another approach was presented where the energy consumed by a ball mill was compared to that

required for single particle crushing based on the same median particle size of the feed and product

33

material for both methods. It was determined that the energy efficiency of grinding quartz in a ball mill

was 22% based on this definition.

The impact of the design of comminution devices on energy efficiency was also investigated. This

included a comparison of breaking particles in a steel enclosure and another with gelatin. The basis for

this work was established on findings that showed that 45% of the strain energy to initiate fractures is

converted to kinetic energy. Thus the additional breakage resulting from the impact of fractured

particles on solid surfaces can be used to increase the efficiency of the comminution process. It was

shown that the efficiency of the breakage in the steel enclosure was twice that in the gelatin breakage

test.

Further studies using simulations showed that the efficiency of ball milling based on particle size could

be increased by 36% from increasing the circulating load in a closed-circuit grinding process from 50% to

500%. This increase in efficiency was due the removal of fine particles thus the energy supplied to the

mill was effectively used for breakage of coarse particles.

Approximation of the Energy Efficiencies of Commercial Ball Mills by the Energy Balance

Method

Schellinger, A.K. and Lalkaka, R.D., 1951. Approximation of the Energy Efficiencies of Commercial Ball

Mills by the Energy Balance Method. Mining Engineering, pp.523-524.

Summary An energy balance was used to estimate the thermodynamic efficiency of two ball mills whereby cement

raw materials were ground. The amount of energy converted to heat during the grinding process was

calculated from material and water flowrate values, material and water specific heat capacity, and

temperature measurements of the material, water and slurry flows. The surface energy was defined as

that used for grinding the material and was determined by the difference between the amount of

energy supplied to the mill and that converted to heat. The thermodynamic efficiency was defined as

the ratio between the energy consumed for creating surface area to that supplied to the mill. Other

energy conversions such as noise, phase changes, oxidation, radiation and convection were not

considered since it was assumed that these were negligible.

The results showed that the thermodynamic efficiency of the ball mills examined in this study ranged

from 10.2 to 19.9%. It was stated that these values corresponded well to those presented in other

studies which ranged from 10 to 19% from a grinding calorimeter, and from 7 to 19% for a laboratory

scale ball mill.

34

Appendix 2: Mathematical models for estimating vibrational energy

Model 1: Theory of linear elasticity (Hooke) This approximate model is based on Hooke's generalized theory in one dimension. It can be used to determine the average power transmitted to a structure for a given frequency. The total energy is obtained by calculating the total amount of energy from all frequency bands.

𝑃 = 𝜔𝑛

2

𝐸𝑆0

𝑙0𝑋𝑚𝑎𝑥

2 where 𝑃: vibration power (W)

𝜔𝑛: natural frequency (rad/s) E: Young's modulus S0: surface (m2) L0: length (m) X: movement (m) The simplifying assumption this model makes lies principally in the fact that the vibrating mass undergoes uniform deformation over its entire length, which is not the case in reality.

Model 2: Instantaneous power of a vibrating mass This model assumes that the vibrating mass is a rigid body moving as a block. The vibration power is thus defined as the product of inertial forces and velocity. 𝑃 = 𝑚���� where 𝑃: vibration power (W) m: mass (kg) ��: acceleration (m/s2) ��: velocity (m/s) The simplifying assumption of this model overestimates the vibration power which will be interpreted as an upper limit.

Model 3: Statistical Energy Analysis (SEA) The method of statistical energy approximation (SEA) is a model that estimates energy flow through a structure. 𝑃=𝜔𝑚0𝑆⟨𝑣2⟩𝜂 where 𝑃 = power (W) 𝜔: Angular frequency (rad/s) S: surface (m2) mo: surface density (kg/m2) η: damping loss factor (dimensionless) v2: mean quadratic velocity along the measured surface (m2/s2) The simplifying assumption of this model estimates power by considering a uniform velocity along the surface that the vibrational energy flows through.

35

Bibliography

Ballantyne, G.R., 2012. “Proportion of energy attributable to comminution.” 11th AusIMM Mill Operator’s Conference 2012.

Bouchard, J. 2015, “The CMIC / CanmetMINES Comminution Energy Recovery Potential Initiative – The Agnico Eagle Goldex Division Case.” International Mineral, Processing Congress, 2016.

Gérard, A 2015. “Mesure de la puissance acoustique et vibratoire d’équipements miniers” [Measuring sound and vibration power for mining equipment]. Report 15-05-12-AG, SoftdB Inc, pp 31.

Fuerstenau, D.W. and Abouzeid, A.Z., 2002. “The energy efficiency of ball milling in comminution.” International Journal of Mineral Processing, 67(1), pp.161-185.

Jimbo, G. 1992, "Chemical Engineering Analysis of Fine Grinding Phenomenon and Process.” Journal of Chemical Engineering of Japan, vol. 25, no. 2, pp. 117-127.

Kapakyulu, E. & Moys, M.H. 2007, "Modeling of energy loss to the environment from a grinding mill. Part I: Motivation, literature survey and pilot plant measurements.” Minerals Engineering, vol. 20, no. 7, pp. 646-652.

Kapakyulu, E. & Moys, M.H. 2007, "Modeling of energy loss to the environment from a grinding mill. Part II: Modeling the overall heat transfer coefficient.” Minerals Engineering, vol. 20, no. 7, pp. 653-661.

Lowrinson, G.C., 1974. “Crushing and Grinding.” Butterworths, London.

MNP. (2013, April 19, 2013). “Documentation of Key Findings of the Workshop on Barriers to Green Mining Innovation in Canada.” from http://www.nrcan.gc.ca/mining-materials/publications/11792.

Radziszewski, P. 2013, "Energy recovery potential in comminution processes.” Minerals Engineering, vol. 46-47, pp. 83-88.

Schellinger, A.K. and Lalkaka, R.D., 1951. “Approximation of the Energy Efficiencies of Commercial Ball Mills by the Energy Balance Method.” Mining Engineering, pp.523-524.

Tkáčová, K., Heegn, H. & Števulová, N. 1993, "Energy transfer and conversion during comminution and mechanical activation.” International Journal of Mineral Processing, vol. 40, no. 1, pp. 17-31.

Tromans, D. (2008). “Mineral comminution: Energy efficiency considerations.” Minerals Engineering, 21(8), 613-620. doi: 10.1016/j.mineng.2007.12.003.

Van Nierop, M.A. and Moys, M.H., 1997.”Measurement of load behaviour in an industrial grinding mill.” Control Engineering Practice, 5(2), pp.257-262.