Coal blending models for optimum cokemaking and blast furnace operation

An important problem at an integrated steel-producing plant is the blending of different types of coals to make coke for the blast furnace operation. Historically, linear blending models were not appropriate because coal properties important for both optimum cokemaking and blast furnace operation do not combine linearly and are not completely understood. In this paper, a solution methodology is developed that utilizes two techniques: (1) a mixed integer linear programming model for blending the candidate coals to produce coke at a minimum cost and (2) binary decision tree analyses and results that are converted into model constraints to ensure the production of high-quality coke. Subsequently, the model results are used at the pilot-scale oven for testing and for validating the new, improved blend(s) that have been recommended by the model. This is an on-going need that is dictated by changing availabilities in both coal prices and sources. These steps reduce costs by both minimizing the number of blends to be tested at the pilot-scale facility and ensuring a minimum cost coal blend that is useable for the operating facilities. Hypothetical, but realistic, data are used to illustrate how the model performs.