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Lyceum 2021 | Together Towards Tomorrow

Susan’s presentation focuses on how to break silos between data and disciplines, and use geospatial visualisation to promote transparency of material properties in real-time, both on-site and remotely.

Discover how to take a whole-system approach to the mine value chain and bring decision-making into focus with proactive and dynamic influence characteristics. Find out how the Stockpile Management application can deliver value to your operations and increase production.



Susan Kennedy
Global Field Engineering Lead,


30 min

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Lyceum 2021

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Video Transcript

(upbeat music)

<v ->Hello, my name is Susan Kennedy.</v>

I’m here from

We are an industrial AI company

focusing on the mining sector with a mission

to make mining operations, efficient, sustainable, and safe,

through trusted and transparent AI solutions.

We optimize every step of the mining value chain.

Today, I’m here to talk about stabilizing mine production

with end-to-end material tracking.

Within your mine,

high resolution visibility of your material

means predictable and stable material input

into your plant.

In your plant, the foresight into the incoming material

allows optimization of the plant controls and decisions.

End to end material tracking breaks down silos

between different disciplines and data onsite

and aids the realization of value of mining operations.

On your screen, you can see

our intelligent mine experience center,

which we have developed with our partners BSF.

Which is as you can probably make out a digital mine.

Now we’re here to talk about end to end material tracking.

So the obvious place to start this story

is in the resource model, underneath the pit.

Within your resource model,

you have information on your material properties

and where these are spatially located.

Once you blast, you may have a model

of how this material is expected to have spread.

And from there you will go to either your plant

or a stockpile.

So in this case, I’ve got open cut mine,

and I’ve got trucks that are hauling the material up

to a jaw crusher here and onto a conveyor belt

into the remainder of the plant.

But it’s actually the stockpiles

that I want to talk more carefully about.

So if we’re not going straight to the plant,

then we’re probably going to a stockpile.

And this is where information,

a lot of information is traditionally lost

or heavily diluted.

So I’m going to talk to you about the hurdles

of material tracking in traditional stockpile management

and how we have overcome this with a spatially aware model.

I’ll take you through a tour of the model

and how it integrates with other software.

And then I’ll take you into IntelliSense’s platform

Brains app, where I’ll talk further about the value.

After we talk about stockpiles, we’ll go into the plant.

To talk about the value of more stable

and predictable material information within the plant.

I’m going to talk specifically about heap leaching

as an example, but the concepts

that I’m going to talk about apply to other areas

of the plant as well.

So to talk about stockpiles, on the left of my screen

is a typical model of a stockpile,

a Weighted Average Model.

In a model such as this,

all material information is averaged.

And during planning and reclaiming

material variation cannot be distinguished.

Are the models currently in use include first in first out

or last in first out,

but all of these models are quite simple,

with the result being,

not having an understanding of material distribution

and potentially unstable and unpredictable feed

into the plant.

A spatially aware model is far more representative

of reality and that’s what we have developed

and what you can see on the right of my screen.

Using a model like this,

you can manage both dumps and reclaims better.

This model can be used to either plan and track blending

within your stockpile,

or strategically plan reclaims to suit your mine plan

and achieve more stable results

as the blend goes into your plant.

To show you a real life example of results achieved,

on the screen is a mine plan versus plant actuals

at a copper mine.

The red and blue bars are percent difference

and standard deviation of versus actuals

over a four month period prior to the introduction

of our Spatial Model,

when a Weighted Average Model was used.

And then the green and yellow lines

are after our Spatial Model was introduced,

which we call GRIDS, also over a four month period.

All sample days or data points were taken

from when there was over 60% plant feed from stockpiles.

So these days we’re heavily reliant

on stockpile information accuracy.

And as you can see using the Spatial Model,

they have been able to get much, much closer to their plan.

So I’ve just jumped into Seequent Leapfrog.

Now, as I said earlier,

I was going to talk about integration.

Our application is agnostic to systems

and can use data from various sources.

Typically FMS if it’s available, mine block models,

topographical scans and SCADA systems.

We’re also using machine learning and AI

to improve anomalous data.

Outputs are also integratable with other applications.

And our 3D block model can be opened up into any geological

modeling or visualization tool.

And as I mentioned,

I’ve actually got one of our models open now

within Seequent Leapfrog.

So this is a copper stockpile I’m displaying

and at the moment I’ve got total copper on display.

So you can see the spatial distribution of the copper.

Now I can change this to any other material property

that I’m tracking and I can track whatever

material properties that I have information on.

So for example, I’ve just changed to arsenic

and I can also see the spatial distribution

of arsenic within the model.

Another thing I can see within the model

is how long the material has been sitting on the stockpile.

So as you can see, this stockpile was created

from the Southeast and then went Northwest.

And this shows me the age in weeks of the material.

And this can be particularly important

for any tufts of material where oxidation is important,

for example, in a coal mine.

Now the model behaves realistically,

it’s not simply discrete blocks.

Within the model, the material blends and moves.

It flows downhill just as it would in real life.

If there are dozers on the stockpile,

either trying to blend or simply manage the stockpile,

the material movement and blending is also taken to account.

And ultimately information is retained.

You are no longer diluting information you had

from your Institute model.

And as I said, from here, you can start making decisions

on the information that you have.

So now I’ve jumped into the Brains app,

which is our platform where I’m displaying

a little more information about the stock piles.

So I’ve got open, a material reclaimed screen

about the Stockpile Calipso.

And on this screen,

you can see a lot of information about the material

that has been reclaimed from the stockpile.

If I show you this graph, you can see how the model

has spatial understanding.

So instead of being a flat line of your reclaims

from a stockpile, you can now see

that the material information that I’ve got displayed,

and I’ve got total copper recovery

and concentrate displaying on my screen,

how they vary depending on exactly where

the material was reclaimed and the material properties

at that location within the stockpile.

Traditionally in a Weighted Average Model,

it would look more like this red line.

It might be just completely flat or maybe slightly sloping.

And the difference between the red line and the blue line,

blue line representing what’s actually happening

within the stockpile,

is the difference between what you think you’re getting,

with a Weighted Average Model

and what you’re actually getting.

So in this example, we’ve got 2.05% copper,

is what you would think you’d be getting.

Whereas you might actually be getting 2.2% or 1.85%.

And so with this,

you’re not only not reaching your feed plan,

but if you’ve got levers to pull in your plant

for recovery or throughput like additive

in your concentrator, you will not only,

you’re also not optimizing your output

and there’s opportunity cost there.

So it’s not only about reclaiming from your stockpile,

but there’s also the opportunity to manage

the build of your stockpiles and actively manage them

in real time.

So I want to show you a couple of interesting cases

that we’ve seen.

In the first one, there was over a hundred thousand tons

of waste, unknowingly dumped onto a stockpile

when some road widening activity was being carried out.

This was clearly seen in the 3D block model,

as represented by the blue area in the model

and the alerts in the application as shown

on the bottom in the, as shown as out of range dumps.

So this means it was out of range

for what was expected for that stockpile.

If this has entered the plant,

the opportunity costs of not processing ore instead of waste

was over 5 million US dollars.

So instead the material was isolated from the stockpile.

In the next example, 20,000 tons of ore

was used to create a platform in a pit, again unknowingly.

The material was labeled to go to a stockpile,

which you can see within the red boundary on the screen,

but was actually dumped in a pit.

And they found out was used to build a platform.

The data quality screen that you can see

on the bottom right, was able to highlight the anomaly.

And a plan was put in place to reclaim the material

once the platform was no longer required.

And this ore was valued over US 1 million.

So to continue talking about stockpiles.

Now, once the material comes from a stockpile

and goes to the plant, what you can see on my screen

at the moment,

is the combination of various different sources.

So in the green here,

I’ve got 52% of material is coming from my pit,

going into my plant.

And the remainder, 48%,

is coming from three different stockpiles.

And you can see in this graph,

the combination of all those different material sources

and the ultimate material properties entering the plant

over time.

I’ve got the average over that time,

down the bottom in these text boxes.

I have 11 days filtered, but you might want to filter

for a few hours, for six hours, for a shift, for a day,

so you can see exactly what is coming

into the plant in near real time.

So it will hit your crusher and then that will be,

then the people in the plant will have an understanding

about what is coming in very soon into their plant.

So this gives them more information

about what is entering the plant

then what was just in the mine plan.

So this will allow decisions to be made

to optimize recovery or plant output.

So generally the various solution panels available,

such as the two that I’ve shown you,

are useful by geologists and grade controllers

for reconciliation,

my mine plan is to check compliance to plan

and the plant team to understand

what material is entering the plant.

So if we take this and move to the plants now,

as I mentioned, I’m going to talk about heap leaching,

but the concepts of what I’m talking about

can be applied to other areas of the plant.

I’m going to talk about two challenges within heap leaching

and how material tracking can improve the outcomes.

The first challenge is the presence of highfine material

causing poor leaching kinetics,

and excessive consumption of asset or cyanide.

Secondly, the presence of moisture in the tailing

causes the material to behave with greater fluidity,

which can prevent the construction of a stable heap,

reduce the rate a rotary shovel can reclaim

and result in increased storage requirements

and thus required environmental permits.

We use machine learning to model the influence

of the different material properties

on process performance metrics,

giving the marginal contribution of each factor

to every prediction made.

And this is what we call the Material Influence model

in the screen have now open.

So by knowing the planned and actual material input

to input to your plants,

you can the fine fraction and residual moisture.

So on my screen here,

these limits here in red are what we want to stay under

and this is our prediction of fine fraction.

So you can see, we want to keep the green and blue lines

underneath this red line.

And the same can be said

for this residual moisture prediction.

So we want to keep our residual moisture

underneath this red line.

Now, how do we do that?

So we can look at the material influence.

So once we know when the fine fraction

or residuals might likely to exceed the limit,

this is where we go.

For the fines, some of the influencing factors

are linked to the material properties

and some linked to the crushing circuit.

And it is a similar story for residual moisture.

Adjustments can then be made when high fines

or high moisture is predicted.

This can be adjustments around the crusher blending ore,

or adjustments to your agglomeration variables,

such as adding more binder.

For moisture content, blending is also a possibility

or adding salt.

And down here, I can see the opportunity cost of action

is not taken.

So for this particular timeframe that I have filtered,

which is a month, which I can see up here,

if I don’t take actions,

when the predictions are above this red line,

then there’s a potential to, there’s opportunity costs

of over a hundred thousand US dollars.

So this is just one example in the plant

of how effective material tracking

can not only stabilize your product,

but also increase the value of production.

And as you can probably imagine, from what you’ve seen,

end to end material tracking helps break down silos

between disciplines and data and facilitate constructive

conversations between teams.

And that is all from me today.

Again, my name is Susan Kennedy from

I hope you found that interesting and useful.

(upbeat music)