Statistical Analysis and Optimization Of EAF Operations

摘要

The largest material cost, for an electric arc furnace steelmaker, is the cost of scrap. Analysis of operating data (kWh, burner programs, carbon practice) has revealed that yield and electrical energy consumption is strongly related to scrap mix. Electrical energy consumption is a strong indicator of the power-on-time and consequently tap-to-tap time. These in turn are related to oxygen and natural gas consumptions and productivity. Typically, scrap purchasing decisions are based on residual metals requirements, market conditions and the cost of scrap. Decisions on charge mix to the furnace are made, in part, based on inventory. Oxy-fuel burners, oxygen lance and carbon practice decisions are made with little consideration of how to take advantage of the properties of the differing scrap types and mixes. The ability to predict how scrap mix, in relation to these other parameters, helps to determine the operating efficiency of the furnace would allow operators and purchasing agents to make informed decisions as to the ideal scrap mix given a set of economic conditions. This paper represents a summary of a component of a work in progress with the goal of developing an overall optimization strategy for furnace operation. Ultimately, this strategy will include the definition of the interrelationships between scrap mix, electrical energy consumption, oxy-fuel burner programs, productivity and carbon practice. This particular study focused on the development of a model, for the prediction of kWh per charge ton, as a function of scrap mix. The goal of optimization was to minimize electrical costs, scrap costs and to maximize productivity benefits as determined by scrap mix charged to the furnace per heat. Consideration was given to limitations on the scrap mix in order to provide appropriate grade specifications in terms of residual metals.