Fuel Costs Minimization on a Steel Billet Reheating Furnace Using Genetic Algorithms

Abstract

Metallurgy industries often use steel billets, at a proper temperature, to achieve the desired metallurgical, mechanical, and dimensional properties of manufactured products. Optimal operation of steel billet reheating furnaces requires the minimization of fuel consumption while maintaining a homogeneous material thermal soak. In this study, the operation of a reheating furnace is modeled as a nonlinear optimization problem with the goal of minimizing fuel cost while satisfying a desired discharge temperature. For this purpose, a genetic algorithms approach is developed. Computational simulation results show that it is possible to minimize costs for different charge temperatures and production rates using the implemented method. Additionally, practical results are validated with actual data, in a specific scenario, showing a reduction of 3.36% of fuel consumption.