Optimum Distribution of Raw Materials and Fuel/Energy Resources Across Multiple Blast Furnaces

Abstract

Metallurgical companies are currently facing an unstable market for iron ore and fuel/energy, which means that it is important to optimize the distribution of raw materials across multiple blast furnaces and determine the optimum composition of the blast-furnace charge for each furnace. Our analysis of current mathematical modeling approaches for determining the optimum distribution of iron-ore raw material across multiple blast furnaces indicated that the most promising approach involves full-scale modeling and the concept of perturbed object trajectories relative to a reference trajectory. The paper describes algorithm design, mathematical tools, and software for a system to determine the optimum distribution of raw materials and fuel/energy resources across multiple blast furnaces. The optimum raw-material resource distribution scheme implemented in the blast-furnace management system must support integration of the physical and chemical laws governing basic control-target and translator-model processes. The problem was solved by assuming small deviations of parameters from baseline conditions for the perturbed trajectory relative to a reference trajectory. We illustrate the blast-furnace operations planning capabilities of this software and methodology with respect to the cast-iron smelting process and improvement of performance statistics using the Magnitogorsk Metallurgical Complex PJSC (MMC PJSC) as an example.