Coal blending explained

Coal blending is the process of mixing coals after coal has been mined to achieve quality attributes that are desirable for the coal’s intended application (e.g. steam generation, coking).[1] The quality attributes that are most important in blending will differ from one mine site to another and also depend on how the coal seams vary in quality and their final intended use. In thermal coals, quality attributes of interest often include ash, volatile matter, total Sulfur, and gross calorific value. For coking coals, additional attributes are sometimes considered including crucible swelling number, fluidity, and RoMax.

Blending methodology

Blending is typically achieved through the stacking of different materials on a stockpile or within a vessel’s hatch during ship loading. Stacking methodology (e.g. Chevron, Windrow, Cone Shell, Strata) can also impact the homogeneity of the final blended material.Blending sometimes will take place prior to the Coal Handling and Processing Plant (CHPP) in order to achieve attributes (e.g. feed ash levels) that can improve CHPP production rates. Blending may take place in several locations within the demand chain including:

Decision support software

Blending decisions impact the total tonnes of each product that a mine site is able to sell. In addition, the quality attributes of a product can impact the final sale value of the product. Because blending has a significant impact on mine site revenue, several decision support systems have been developed with the aim of improving product reliability and profitability.

Blend optimization

Blend optimization is a nonlinear combinatorial optimization problem where the objective is typically to maximize revenue, Net Present Value (NPV), or monthly product tonnage targets.[2] Important features of the blending problem include:

Several constraints must also be taken into account including:

Blend Analysis

Blend analysis is the process of understanding what blending options exist within a specified schedule and how these options impact product quality, projected revenue, and scheduled mining decisions. Because mathematical optimization algorithms only solve a mathematical model of the physical blending problem, it is typically necessary to make manual adjustments to blends in order to achieve practical/desirable business outcomes. Analytics software tools facilitate this process by enabling a mine planner to easily see what options exist and the consequences of these options.

External links

Notes and References

  1. Chirons, Nicholas P. Coal Age Handbook of Coal Surface Mining
  2. Whitacre, J., Iorio, A., Schellenberg, S. “Coal Blending: Business Value, Analysis, and Optimization” https://arxiv.org/abs/1405.0276