(301h) Steel Scrap Purchasing Optimization and Supply Management

The production of steel is dominated by the use of iron ore and recycled steel scrap. Recycled steel scrap is available on the open market as a commodity. The scrap commodity is purchased periodically to ensure that overall production targets are met. Recycled steel scrap is used in both basic oxygen (BOF) and in electric arc (EAF) furnaces where it is blended with iron and melted, batch-wise, to produce steel that is cast into solid shapes that are then rolled, treated, and shipped to customers. Producing families of steel grades with recycled steel scrap to meet customer requirements in a timely fashion is a key aspect of furnace operation.

Purchased steel scrap is the most important feedstock material for an EAF, contributing significantly to production costs. Steel scrap can be used in different proportions to achieve desired physical and chemical properties of the finished product in order to meet customer requirements. The economical use of recycled steel scrap is governed by many factors including the prevailing market price and availability from each scrap supplier (e.g. cars vs. refrigerators) and the content of constituents such as copper, tin, sulfur, phosphorus. Limiting or controlling the level of these constituents is of primary concern to meet requirements such as hardness and weldability and to ensure that steel material properties are uniform across the cast piece.

The price and quality of the scrap fluctuate and therefore require the periodic determination of the relative usage rate for each batch of steel made in a particular period of time. The determination of the usage rate results in an optimization programming problem that seeks to minimize scrap purchase costs. The solution of the programming problem should indicate which scrap supplier to purchase from, what scrap type to use, and in what lot quantities, in order to fill customer orders and to maintain desired inventory levels for the steel producer.

A mixed integer optimization problem has been developed and was solved by using a commercially available tool. The model which includes about 600 real-valued variables, 200 integers, and over 800 possible constraint equations, uses industrial data and actual market prices and supplier information to perform calculations. Prices, quality, and supplier information are input or read into the model along with selections of constraints, and a production plan. To date, the case by case solution of the optimization problem has led to suggestions for improved blends and has indicated a potential for savings for a monthly scrap purchase. The model produces suggestions that can be implemented in a production environment. For example, savings are possible in changing the number of unique steel grades produced that allow a different quality standard, or in using cheaper scraps in meeting volume and furnace yield constraints.

Various aspects of the model will be discussed including ease of development and use, the solution method, formulation of constraints, objective function, energy usage predictions, productivity impact, supplier selection, and tiered pricing mechanisms that were used in the simulations. Future work will be discussed including challenges such as improving models for bulk scrap densities based on the packing/settling of scrap material in the furnace and use of related commodities such as electricity.