Optimization of Primary Steelmaking Purchasing and Operation under Raw Material Uncertainty

A centralized optimization strategy is proposed to determine optimal raw material purchasing and plant operation practices as applied to primary steelmaking in the steel processing industry. Raw materials are purchased on the open market and include coal, iron ore pellets, and scrap steel. There are many raw material vendors, providing products varying in quality and price. It is desired to determine the least costly method of both purchasing and processing the raw materials to make steel of acceptable quality. A model for primary steelmaking is developed using a combination of mass balances and empirical relationships. The model, in addition to process constraints, is combined with an economic objective function and the resulting optimization problem solved using a mixed-integer nonlinear programming (MINLP) solver. Case studies illustrate the strong connection between plant sections, and the significant impact that the carbon, volatile matter, and phosphorus content of the coals and pellets have on raw material selection. Raw material uncertainty is incorporated using two-stage stochastic programming. The results indicate that by making a slightly more expensive raw material purchase, the frequency of constraint violation during processing can be significantly reduced.