01 / Value
Value-based bottleneck analysis
Test whether a constraint that binds on tonnes still limits value once blending, product mix and sales are optimised together.
Customer case study · Coal
Bottleneck Optimisation Software for Mining
BlendOpt is a configurable optimisation platform for mining value chains. Configured for bottleneck analysis, it finds the constraint that actually limits value, not just tonnes, by starting from ROM and optimising stockpiles, processing, logistics and sales together.
Customer resultCentral Queensland coal
Powered by BlendOpt
Outcome
CHPP upgrade not needed
The bottleneck was eliminated on a value basis, saving tens of millions of dollars in capital expenditure.
Revenue~10% higher
Operation3 pits, 5 Mt a year
Forecast12 months
Coal · PCI & thermalCase study below
What BlendOpt optimises
What BlendOpt optimises in bottleneck analysis
That shows whether a capacity upgrade is really needed, or whether the same value can be unlocked by changing how material is blended, washed, stockpiled and sold.
02 / CHPP
CHPP and wash plant feed
Decide which material is washed, bypassed or stockpiled, including material such as oxidised coal that cannot go through the wash plant.
03 / Logistics
Haulage and logistics limits
Model limited haulage between pits, the plant and the rail stockyard, and include the extra cost of moving material in every decision.
04 / Stockpiles
Stockpiles and blending
Blend from many stockpiles of varying quality so plant capacity is spent on the material that adds the most value.
05 / Products
Product portfolio and sales mix
Balance fixed and spot market sales across products such as PCI and thermal coal while meeting operational constraints up- and downstream.
06 / Scenarios
What-if capacity scenarios
Compare a proposed capacity upgrade against operational alternatives and alternative product portfolios before committing capital.
Customer results
Bottleneck optimisation results
Results BlendOpt customers have measured when capacity, stockpiles and plants are optimised as one system.
$10s of millions
in CHPP upgrade capital avoided by eliminating the bottleneck on a value basis
Read the bottleneck case study below →
+18% NPV
over life of mine, like for like, against an existing Xpac plan across multiple processing plants
Multi-plant life-of-mine optimisation →
+$24M / yr
forecast revenue from new stockpile definitions and a moderate capacity change
Stockpile optimisation case study →
The whole value chain
Bottlenecks in the context of the whole value chain
A plant that looks full on tonnes is not always the constraint on value.
What the plant is fed, which stockpiles are used, how material is hauled and which products are sold all change where the real bottleneck sits. BlendOpt models these decisions together, so a capacity question is answered by its effect on the whole operation. See how this works for coal blending software, multiple processing plants, stockpile optimisation and integrated mine planning and scheduling, and read why local KPIs can hide the real constraint.
Bottleneck value chainTonnes vs value.
BlendOpt model
- ROMthree pits, many seams
- Stockscrushed ore
- CHPPwash or bypass
- Haulageto rail stockyard
- SalesPCI & thermal
Optimisation objective
Relieve the constraint on value, not tonnes
A capacity question is answered by its effect on the whole operation, across blending, processing, logistics and sales.
Customer case study · Coal
Case study
Value-chain bottleneck
CHPP bottleneck eliminated without capital investment
A three-pit Central Queensland coal operation used BlendOpt to build a 12 month forecast and found that its CHPP bottleneck could be eliminated on a value basis, avoiding a planned capacity upgrade.
- $10s of Mcapital expenditure avoided
- ~10%more revenue than benchmark
- 12 monthsforecast horizon
Customer outcomes
- Optimised CHPP utilisation
- ~10% increase in revenue
- Eliminated bottlenecks
01
The challenge
A mining operation in Central Queensland, Australia, containing three pits and multiple seams with varying quality and an annual production capacity of 5 million tonnes, wanted to test their planning assumptions. The main seam of the coal deposit was approximately 8 metres thick and split into an upper and lower seam. The operation crushed and screened ore before feeding it to the wash plant or placing it on crushed ore stockpiles. The wash plant had a maximum capacity of 275kT and was located at one of the pits. Washing material from other pits incurred additional costs because of haulage, which had limited capacity to move material between pits and to the rail stockyard for delivery onto trains.
Additional complexity resulted from many stockpiles with varying quality, and different handling of oxidised coal and fresh coal for product blending, as they have different quality characteristics; in the case of oxidised coal, material could not be sent through the wash plant. The product portfolio consisted of PCI and thermal coal products. Production targets for the operation were a combination of fixed and spot market sales. The client needed to develop a 12 month forecast in the presence of significant complexity surrounding the decision points in their value chain.
02
The solution
The complexity of the client’s operation was well suited to the BlendOpt value-chain optimiser, which is engineered to model and optimise operational complexity within mining value chains. The BlendOpt application was used to model numerous stockpiles, processing and logistical decisions, and product portfolio distributions. For our client this had been a very time consuming process: done manually, it was prone to mistakes and required expert knowledge of the operation. BlendOpt was used by the operation as a value chain solver, optimising the blending decisions while satisfying the product distribution and operational constraints up- and downstream.
03
The value unlocked
Using BlendOpt, our client identified that, on a volume basis, the CHPP was the bottleneck in the operation. However, on a value basis, BlendOpt showed that the CHPP bottleneck could be eliminated. Accordingly, the operation did not require capital investment to upgrade CHPP capacity (which was under consideration), saving the organisation tens of millions of dollars in additional capital expenditure. Using BlendOpt, our client also identified a ~10% increase in revenue compared to their benchmark, which was a simple Spry optimiser.
BlendOpt provided a user-friendly UI for rapid testing of “what if” scenarios and alternative product portfolios across 12 months to evaluate opportunities and risks. The mathematical optimisation process allowed BlendOpt users to focus on other important responsibilities in their day-to-day work. BlendOpt’s automated report generation and visual analytics allowed information to be shared rapidly with other stakeholders.
FAQ
Bottleneck optimisation FAQ
How BlendOpt tells a volume bottleneck from a value bottleneck, and tests a capacity upgrade before capital is committed.
What is bottleneck optimisation in mining?
Bottleneck optimisation is finding the part of the operation that limits value and deciding how to relieve it, whether by changing plans or by adding capacity. In mining the bottleneck is often a processing plant, haulage route or logistics link. BlendOpt finds it by optimising the whole value chain at once rather than looking at each step in isolation.
What is the difference between a volume bottleneck and a value bottleneck?
A volume bottleneck is the step that limits how many tonnes can move through the operation. A value bottleneck is the step that limits how much the operation earns. In the case study above the CHPP was the bottleneck on a volume basis, but BlendOpt showed that on a value basis the bottleneck could be eliminated by changing blending, processing and product decisions.
Can a processing bottleneck be removed without capital investment?
Sometimes it can. When plant capacity is spent on the material and products that add the most value, a plant that looks full may no longer limit value. In this case study the operation found it did not need the CHPP capacity upgrade it was considering, saving tens of millions of dollars in capital expenditure.
How does BlendOpt test a proposed plant upgrade?
BlendOpt runs what-if scenarios that compare the operation with and without the extra capacity, each fully optimised across stockpiles, processing, logistics and sales. The operation in this case study tested scenarios and alternative product portfolios across 12 months to evaluate opportunities and risks. Where a capacity change does add value, BlendOpt can show it, as in the stockpile case study where new definitions and a moderate capacity change added a forecast $24M a year.
Which parts of the operation does BlendOpt include in a bottleneck analysis?
Starting from the ROM each pit and seam delivers, BlendOpt can model crushing and screening, stockpiles, wash plants, haulage between pits, rail stockyards and the product portfolio in one model. Constraints such as material that cannot be washed, limited haulage capacity and fixed and spot sales targets are all included. BlendOpt also handles operations with multiple processing plants, where it delivered 18% higher NPV over life of mine against an existing Xpac plan.