Optimising value chains from Mine-to-Market

Stockpile
Management

BlendOpt Stockpile Modelling

 

Key Features

  • Model cargo assembly or dedicated stockpiles.
  • Model material aging such as oxidation.
  • Define minimum and maximum stock capacities.

Commodities

  • Coal
  • Iron Ore
  • Graphite
  • Mineral Sands
 
 
 

Description

BlendOpt.SM stockpile modelling enables you to enhance the supportive role that stockpiling can have on product delivery while balancing this with the costs of unsold inventory.

Time-based models of stockpile quality deterioration, e.g. oxidation, can be created to directly influence planning decisions in BlendOpt’s mathematical optimisation algorithm.

Stockpile minimum capacities can be modelled to change with time when it’s important for BlendOpt plans to build up stocks.

Conversely, stockpile maximum capacities can also be modelled to change with time, enabling studies into the forced depletion of selected stock.

BlendOpt Stockpile rule configuration
 

Constrain stockpiling decisions by location,
quality, processing, or product

Stockpile Models with BlendOpt.SM

If short-term plans are being generated with daily or weekly time intervals, it could be important to model stockpiles due to their impact on blending options and ship loading. With shorter term plans there may also be an option as to whether coal should be stockpiled or directly fed to the CHPP which may also need to be optimised. Stockpiling is relevant to the integrated mine planning problem because it introduces a rehandle cost and influences material qualities as a result of the blending of material onto the stockpile. In some cases rehandling coal influences particle size distributions and coal liberation with a corresponding influence on yield and clean coal quality attributes.

The need to model stockpiles at different locations in the supply chain will depend on context and may require models for ROM stocks, processed coal stocks, and port stocks. Initial inventory levels may need to be modelled due to their direct influence on physically accurate plans.

Minimum and maximum stockpile capacities can also be important model constraints. Allowing modelled stockpile capacities to change over time can enable a plan to account for scenarios involving forced stock depletion as well as enable the build-up of stock eg for a rainy season.

If there is any likelihood of coking coals remaining on a stockpile for a significant period of time then it may become necessary to model changes to coal quality that take place as a result of oxidative ‘aging’ eg loss of fluidity. In these circumstances, it may also be necessary to include constraints on the maximum time that coal is permitted to remain on a stockpile before it is reclaimed.

Blending models for stockpiles can also be important to plan realism. Common models include weight averaging, first-in-first-out, and last-in-first-out models as well as combinations of the above. Assuming the availability of data, more complex bench models or 3D models might also be useful for short-term scheduling.

How BlendOpt helps you define the “Right Problem”