Optimal compositing in bedded deposits

Many mining enterprises are working deposits with multiple thin plies.  Mostly the decision about when to combine these plies and when to mine them separately is based on rules of thumb.  This paper will show that often the application of these rules of thumb results in sub-optimal outcomes.

For any set of plies in a bedded deposit there is a combination which will maximize project value.

The Problem

The decision about what should or should not be composited starts in the field with the geologist supervising the drilling program making decisions based on rules of thumb.  These decisions arise because of a general failure to recognize that there is a problem, with the result that there is almost always a loss of data resolution.

To illustrate the problem Figure 1 shows a sample hole from a coal exploration program.

Figure 1 – Sample hole

The usual rule of thumb which is likely to be applied in this case would be to waste ply EC as shown in Figure 2.

Figure 2 – Ply EC wasted by application of rule of thumb

Using the same hole there are several alternative possibilities as presented in Figure 3.

Figure 3 – Some alternative possibilities

In the example given there are nine layers and 45 possible combinations.  Using a common rule of thumb there would be only one result, however considering that each layer may be mined as coal or waste there are in fact 90 possible combinations.  The simplest way of visualising the combinations is by way of a Pascal’s triangle as shown in Figure 4.  In this figure each possible combination is evaluated on the basis of being mined as ore or waste.

 

Figure 4 – Pascal’s triangle of possible combinations

In Figure 4 each layer is suffixed with a “C” for “Coal” and “IB” for “Interburden”.  When considering the monetary value of each combination the following factors must be considered:

• Loss of ore at each interface
• Dilution at each interface
• Product yield or plant recovery
• Cost of mining as a function of thickness for both ore and waste
• Cost of processing including the handling of extra rejects or tailings

The monetary value for each possible combination is presented in Figure 5.

Figure 5 – Monetary value of each possible combination

In Figure 5 the value of mining all the layers individually is the sum of the lowest level of values, i.e. $180 - $2 + $155 - $2 + $121 - $2 + $76 - $2 - $1 = $523.  Applying the rule of thumb in which the lowest two layers would be wasted would give a value of $526, but there are a number of combinations which would result in higher values than this.  An example is presented in Figure 6.  For this example, the total value would be $348 - $2 + $214 = $560 which is approximately 6.5% better than result using the rule of thumb.  A quick examination of Figure 6 shows that there are two results which are even better than this, one of these being the value three steps down on the left-hand side which shows a total value of $562.  The best result however is where all the layers are mined in a single pass.  In this case the total value is $587 which is 11.6% better than the rule of thumb result.

Figure 6 – A selected possible combination

It is important to realize that this method cannot be properly applied to the drillhole data.  The reason for this is shown in Figure 7.

Figure 7 – Method cannot be properly applied on a drillhole basis

Instead the method should be applied on a model basis as shown in Figure 8.

Figure 8 – Method applied on a model basis

Following the compositing of plies, it will usually be found that various areas of the model have different composites.  This is illustrated in Figure 9.  In this example three sets of composites were created.

Figure 9 – Geological composite model

In Figure 9 there are some areas within each area which are smaller than the minimum selective mining unit.  This means that it would be impractical to change the composite within this area.  Each of these smaller areas can either be:

1. Expanded to make it large enough to be mined separately or,
2. Removed and mined as per the surrounding composite.

It may be considered that a solution to this problem would be to simply use a model cell size equal to the size of the selective mining unit.  This solution however relies on predefining the origin and extent of each selective mining unit which would almost certainly produce a sub-optimal result.

ASEAMCO’s solution to this problem has been to list all solutions at each node as cumulative values and to subsequently use the floating cone optimization method with a cone truncated to the size of the selective mining unit to produce a set of seam composites each of which covers a practical mining area.

An example of the result following post-processing is presented in Figure 10.

Figure 10 – Result after post-processing

Real world results

ASEAMCO developed this application and used it to evaluate alternative strategies for a coal project in the Hunter Valley of New South Wales, Australia.  The final outcome resulted in:

• An increase in total coal recovery from 90.5% to 95.5%
• An increase in run-of-mine tonnage of 14.2%
• An increase of 5.9% in coal product tonnage
• A reduction of 9.1% in total operating cost per product tonne
• An increase in annual EBITDA of more than 7% without any change in equipment or manning

Although the coal processing plant yield reduced from 66.5% to 61.7%, the plant had excess throughput capacity and the additional plant operating costs were more than offset by the increased product output and reduced mining costs.

Other Applications

This method can be applied to any bedded deposit and can also be applied to underground mining where alternative roof and floor positions need to be considered.

Conclusions

• Data must be collected at sufficient resolution to allow compositing
• Rules of thumb are unreliable
• Simple compositing tends to produce unusable results
• Improvement in annual cash flow for mines with multiple seams or multiple coal plies that require compositing can be significant
• Can be applied to underground mining

It is an interesting fact that many strategic mining decisions are made without properly assessing the consequences of those decisions.  Below are some examples of the experiences of ASEAMCO staff in assisting mining operations to overcome the problems which resulted from poor strategic decisions.

The common thread throughout was a failure, at least initially, to adequately assess the consequences of the decisions.

Below are some specific examples from projects involving ASEAMCO staff.

A – Coal Projects

1. A coal mine in Australia

Scenario

A long term mine plan had been prepared for each of two open cuts.  Open Cut 1, which was currently being operated, had a strip ratio of 8 bank cubic metres per tonne of coal (bcm/t).  Open Cut 2,  which was planned to be developed with the next 2 years, had an initial strip ratio of 2 bcm/t.  The economic limit was 10bcm/t.

The available equipment could meet the increased production requirements at the average strip ratio of 5 bcm/t.

However mining in Open Cut 2 was not yet approved but because the average production rate over the next 4 years called for a strip ratio of 5 bcm/t it was assumed that the existing equipment would be adequate.

Unfortunately when a mine has sufficient equipment to mine at a strip ratio of 5 bcm/t but is faced with a block that has a strip ratio of 8 bcm/t, it is immediately apparent that production targets will not be met.

Fortunately in this case the dragline had excess stripping capacity, while the shovel used to mine coal had a very low utilisation.

Main Issue

Using average values assumed that a large waste volume could be pushed back in time.

Advice

A new mine plan was created in which the shovel used to mine coal was fitted with a rock bucket and deployed to augment the other equipment mining prestrip waste .  The waste haulage trucks, instead of hauling waste around the pit into the dragline spoil dump, dumped the prestrip waste into the dragline strip.  This ensured that there were enough trucks to handle the increased prestrip waste loading capacity.  The dragline rehandled the additional material on its second pass.  This strategy increased prestrip capacity while maintaining the same coal output.

Management Decision

Implement the new mine plan.

Result

The mine was able to deliver product at the contracted rate without having to engage external contractors.

2. A coal mine in Indonesia

Scenario

Two open cuts were operating both with similar “reserves”.  Pit 1 was being mined at a strip ratio which produced a margin of $10/tonne over the life of the mine.  Pit 2 was being mined at a strip ratio which produced a margin of -$5/tonne over the life of the mine.

Main Issue

Using average values.

Advice

Only mine Pit 1 as long as coal prices prevent coal from Pit 2 from making a profit.

Management Decision

Mine both pits because “on average” we still make money.  As the saying goes: “you can lead a horse to water, but you can’t make it drink”.

Result

The company made about half as much profit as it could have done, had it followed the advice.

3. A coal mine in Indonesia

Scenario

Coal prices fell to an extent which made the mining of several pits uneconomic.  A management directive was made to reduce the average strip ratio but to dump waste so that future potentially recoverable resources would not be sterilized.  This was interpreted by both the in-house technical services department and an external consultant as “do not dump against the highwall in the pits which are not currently being mined”.  Consequently the waste haulage distance for the remaining operating pits increased from 1000m to 2900m which increased costs by more than $3 per tonne.

Main Issue

Assuming that waste dumped against the highwall in the currently uneconomic pits would sterilize future potentially recoverable resources.

Advice

Reconsider the decision to not dump against the highwall in the currently uneconomic pits.  This was because if waste were to be dumped against the highwall in the currently uneconomic pits the rehandling of this waste would only cost about $1 per tonne.  This would save more than $2 per tonne if sale prices did improve and $3 per tonne if sale prices did not improve.

Management Decision

Continued with the advice given by the external consultant.

Result

The mine was sold at a fire sale price.

4. A coal mine in Australia

Scenario

A mine was being offered for sale.  A member of ASEAMCO’s staff had been engaged to provide technical advice to one of the parties interested in making a bid for the mine.  During the site visit it was observed that the pit was being accessed by a central ramp; the basal seam was dipping and the topography ahead of mining was rising; and the pit was becoming wider.

Questioning of a member of the technical services staff revealed that the waste balance had only been scheduled for two years out and that the dumps were already at environmental limits in terms of height and lateral extents.

Main Issue

It appeared to ASEAMCO’s staff member that the mine would run out of waste dumping space in a little over two years.

Advice

After consultation with the due diligence team the question regarding dump space was asked at the bidders’ forum.  The suggestion was rejected by the vendor.

Management Decision

A lower price was offered by the client after taking into account the looming problem with waste dump space.

Result

The mine was subsequently sold to a different bidder.  Almost two years to the day later, ASEAMCO received a call from the successful bidder for the mine requesting assistance with solving the problem of lack of dump space.  ASEAMCO provided assistance with solving the problem.

5. A coal mine in Australia

Scenario

ASEAMCO was commissioned to undertake a dragline study for a project to determine the most efficient means of mining through a substantial hill which existed at one end of the current strip layout.  Initial analysis by ASEAMCO showed that it was not economically viable to mine through the hill.  When ASEAMCO reported this, mine management stated that they believed that it would cost less to mine through the hill than to extend the strip by opening a new boxcut on the other side of the hill and that “on average” the mine would be more profitable by mining through the hill.

Main Issue

The assumption that, being a large corporation, it was acceptable to lose money for a time because “on average” the mine would still be profitable.

Advice

ASEAMCO suggested a change to the scope of work to make the priority an assessment of the relative merits of the two approaches.

Management Decision

The change of scope was accepted.

Result

Mining did not proceed through the hill.  Instead, after the pit had progressed to a point beyond the hill, the mining strip was extended by opening a new boxcut.

6. A coal mine in Australia

Scenario

While undertaking other planning work at a mine, a member of ASEAMCO’s staff noticed that many thin seams were being removed in individual slices.  As each slice was being removed there was unavoidable loss and dilution at each coal/rock interface.  This raised the question of whether it would be more economical to bulk mine combinations of these seams in at least some locations.

A preliminary analysis was undertaken, at no cost to the client, to investigate this possibility.  The analysis examined mining costs as a function of thickness, recovery, dilution, coal haulage, coal processing, waste dumping and rejects disposal.  This analysis showed that in certain situations it was more economical to bulk mine rather than to mine individual seams.  Many mines operate using a rule of thumb to determine the minimum separable coal and parting thicknesses.  What was surprising was that the analysis showed that applying the accepted rules of thumb would so often result in substantially sub-optimal outcomes for the mine.

Main Issue

The assumption that the rules of thumb regarding the minimum separable coal and parting thicknesses were correct.

Advice

It was suggested to the mine that applying a computer software solution to the problem of minimum separable coal and parting thicknesses would likely provide a substantial improvement to the profitability of the mine.

Management Decision

Management commissioned a detailed study to determine how and where the various seams in the pit should be composited.

Result

• Total coal recovery improved from 90.5% to 95.5%
• ROM tonnes increased by 14.2% due to additional dilution
• Product yield reduced from 66.5% to 61.7%
• Product tonnes increased by 5.9% due to improved seam recovery
• Cost per product tonne reduced by 9% due to reduced mining costs and increased seam recovery
• EBITDA increased by 7.1% with no changes to mining equipment or manning

ASEAMCO now applies optimal seam compositing software techniques on client projects.

B – Metals Projects

1. An underground gold project in Indonesia

Scenario

The project consisted of two underground mines.  Both of these were acessed by declines.  ASEAMCO was commissioned to assess the alternatives to determine the best approach to increase production.

Main Issue

Total production was reducing due to limited haulage capacity and poor equipment allocation.

Advice

Both short-term and long-term situations were considered which resulted in the assessment of five scenarios.  Linear programming was applied to determine the best solution to the problem.

Management Decision

Management followed ASEAMCO’s advice and, in the short term, redeployed equipment while for the longer term additional trucks were purchased.

Result

Production output increased immediately and, following the introduction of the additional trucking capacity, record production was achieved.

2. A graphite project in Indonesia

Scenario

One of ASEAMCO’s clients was approached by the owner of a tenement containing graphite with a view to selling an interest in the deposit.  The client engaged ASEAMCO to assess the deposit to determine its value.

Main Issue

The main isuues were that the prefeasibility study provided by the vendor had unrealistically low operating costs and unrealistically high sale prices which led to the asking price being an order of magnitude greater than the estimated value.

Advice

Do not invest in this project.

Management Decision

Management followed ASEAMCO’s advice and decided not to invest in the project.

Result

The client avoided a poor investment.

3. An open cut silica project in Australia

Scenario

An overseas investor was interested in exploring the potential for developing a vertically integrated business for the production of solar panels.  ASEAMCO was commissioned to locate suitable deposits of silicon dioxide in Australia on either an acquisition or farm-in basis.

Main Issue

Finding suitable deposits of silica which met the required specifications and which where either available for pegging or for farm-in.

Advice

The major portion of value adding in the production of solar panels is between silicon metal and the finished product rather than between silica and silicon metal.  Consequently, after consultation, it was recommended to buy silicon metal rather than mining silica and producing silicon metal.

Management Decision

Follow ASEAMCO’s advice and pursue the production of solar panels using purchased silicon metal.

Result

The substantial capital investment required to convert silica to silicon metal was avoided.

4. A potential porphyry copper project in Indonesia

Scenario

A potential porphyry copper deposit had been identified and partially explored.  ASEAMCO was commissioned to assess the mining potential for the deposit.

Main Issue

Relatively low grade and tonnage meant that the project was only viable if the bullish trends for gold and copper were maintained.

Advice

A risk analysis involving capital and operating costs vs metal prices showed that the project was only viable if the bullish trends for gold and copper were maintained.

Management Decision

Follow ASEAMCO’s advice and rank this project against other projects in the portfolio before committing to any further expenditure.

Result

Capital expenditure was allocated in the most efficient manner while minimizing risk.

Pit optimization has been widely used in the minerals industry for many decades.  However, for bedded deposits there is an inherent problem with the existing technique.  This paper will firstly describe the methodology in current use, then present the inherent problem with this method, and finally explain the solution to the problem.

Current Methodology

Optimization is based on building a pattern of 3D blocks which are then valued.  Values and costs are assigned to each individual block as shown in Figure 1.  Ore is assigned a value calculated on quality/grade, mining cost, haulage cost, processing cost and recovery.  Waste is assigned a mining cost and haulage cost.  The sum of the values in a block is the total value of that block.

Figure 1 – Assignment of value to each block

Figure 2 shows a vertical cross-section through an example deposit with a seam or vein dipping to the right.

Figure 2 – Vertical cross-section showing seam or vein

Figure 3 shows the vertical cross-section after the assignment of values to each block. In this figure the costs of mining waste is taken as $4.  The ore blocks will have a net positive value.  The first row consists of what is termed “air blocks”.

Figure 3 – Vertical cross-section showing assignment of values

The next step in pit optimization is to sum the blocks in each column vertically.  This results in the values presented in Figure 4.

Figure 4 – Blocks after vertical summation

In an open cut mine, mining can progress in one of three directions as shown in Figure 5.  In this case it is assumed that the stable wall angle is 45°.

 

 Figure 5 – Mining directions

Starting in the top left corner the next step is to sum the blocks along the mining directions shown in Figure 5 in the direction which will give the maximum cumulative value and mark the direction from which the summation was made.  This results in the values presented in Figure 6.

Figure 6 – Blocks after summing for maximum value & marking source

The next step is to trace the arrows back along the mining directions starting in the top right corner as shown in Figure 7.  The resultant trace is the base of the optimum pit.

Figure 7 – The highlighted trace of the optimum pit floor

The inherent problem for bedded deposits

Because the blocks are an approximation to the floor/footwall  of the vein or seam some material below the seam or vein will be included in the assessment of the optimum pit as shown in Figure 8.  This material is called underburden.

Figure 8 – Section highlighting the underburden problem

For any set of costs, including the underburden causes the optimum pit to be pessimistic because, except in cases where the the stable pit wall angle is shallower than the dip of the seam or vein, the underburden is included in the assessment of the mining economics even though it will never be mined.

Creating smaller blocks reduces the problem but does not remove the problem.  This staircase effect is illustrated in the three-dimensional display of part of an optimum pit presented in Figure 9.

Figure 9 – Three-dimensional view highlighting staircase effect

Solution to the problem

It has already been shown that using blocks with fixed vertical dimensions does not adequately model a bedded deposit or flat-dipping vein due to the underburden problem.  This is the reason that bedded deposits are typically modelled using two-dimensional grids rather than three-dimensional blocks.  In two-dimensional grids each XY grid cell contains the actual vertical elevaton of the surface being modelled rather than relying on the approximation offered by three-dimensional blocks.  This means that the upper and lower surfaces of the seam or vein are accurately modelled.  As a result, instead of a block containing a mix of waste and ore each mining block contains only ore or waste.  Noting also that the thinner the seams or veins the greater the problem created by using blocks with fixed vertical dimensions.

Figure 10 compares the optimum pit obtained using a 3D block model with the optimum pit obtained using a 2D gridded model.

Figure 10 – Comparison between optimum pit using 3D block model (left) and 2D grid model (right)

The advantage of using optimization based directly on a 2D grid model for a seam or vein rather than using a 3D block model becomes more pronounced as the dip angle of the seam or vein reduces.  This is highlighted in Figure 11 which shows that, in this instance, the optimum pit created using the 2D grid model optimization technique recovers approximately 25% more ore than the optimum pit based on a 3D block model for the same economic inputs.

Figure 11 – Increased advantage of optimum pit based on 2D grid model with reduction in dip angle

It is clear that applying 2D grid pit optimization to a set of volumes modelled using two-dimensional grids produces an optimum pit shell which follows the bedded structure and gives a superior result to optimization based on three-dimensional blocks.  An example of the output is illustrated in Figure 12 where it can be seen how the pit floor follows the basal bedded structure.

Figure 12 – Optimization with pit floor following basal bed structure

This approach has all of the advantages of current commercially available pit optimizers, with the added benefits of:

1. The lower limits of the resultant optimum pit follows the floor, or footwall, of the seam or vein, i.e. there is no underburden, and;
2. The optimization result more accuractely reflects the specified economics.

To the best of our knowledge this technique is available exclusively through ASEAMCO.

ASEAMCO principals have undertaken consulting assignments for the following coal projects/companies:

New South Wales - Hunter Valley

Ashton	Bayswater	Bengalla	Bulga
Camberwell	Cumnock	Donaldson	Drayton
Glennies Creek	Howick	Hunter Valley No. 2	Lemington
Liddell	Mitchell Carrington	Moolarben	Mount Arthur North
Mt Owen	Mt Pleasant	Mt Thorley	Narama
Ravensworth	Ravensworth East	Ravensworth West	Rixs Creek
Saddlers Creek	South Lemington	Ulan	Wambo
Warkworth	Westside	Wilpinjong

New South Wales - Gloucester Basin

Duralie	Stratford

New South Wales - Gunnedah Basin

Belmont	Boggabri	Sunnyside	Werris Creek
Whitehaven

New South Wales - Western Coalfields

Baal Bone	Charbon	Ivanhoe	Neubecks Creek
Springvale

Queensland

Brigalow	Broadlea North	Broadlea	Callide
Carrinyah	Collinsville	Coppabella	Daunia
Diamond Creek	Foxleigh	German Creek	Goonyella
Harrybrandt	Humboldt	Isaac Plains	Kennedy
Lake Vermont	Moorvale	Mt Cooper	Nebo
North Collinsville	South Newlands	Oaky Creek	Pentland
Poitrel	Riverside	Millennium	South Blackwater
Suttor Creek	Theodore	Togara North	Togara South
Wandoan	Wilton

South Australia

Leigh Creek

Western Australia (Collie Coalfield)

Chicken Creek	Ewington	Muja	Premier

Indonesia

Adaro	Arutmin	Baramulti	Bayan Resources
Berau	Cokal	Genesis Sumber Energi	Ilthabi Bara Utama
Indotan	Kaltim Prima Coal	Kangaroo Resources	Mahakam Sumber Jaya
Separi	Tanito Harum

Other International

Benga Coal Mine - Mozambique	Big Brown – Texas USA
Captain Mine – Illinois USA	Carter Rawhide - Wyoming USA
Depot Creek – PNG	El Cerrejon – Colombia
Jacobs Ranch – Wyoming USA	Julia Creek – Alberta Canada
Neyveli Lignite – Tamil Nadu, India	Pike River – South Island, New Zealand
Piparwar – India	Semirara – Philippines