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

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.

[1]  W. Marsden.  I and J , 2012 .

[2]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[3]  Mahdi Mahfouf,et al.  A Paradigm for the Scheduling of a Continuous Walking Beam Reheat Furnace Using a Modified Genetic Algorithm , 2007 .

[4]  Liu Quan-li.  An Optimal Scheduling Algorithm for Reheating Furnace in Steel Production , 2006 .

[5]  Cristóvão Nery Giacomin,et al.  SIMULAÇÃO COMPUTACIONAL DO PROCESSO DE REAQUECIMENTO DE PLACAS EM FORNOS DE VIGA MÓVEL , 2008 .

[6]  Andreas Kugi,et al.  A mathematical model of a direct-fired continuous strip annealing furnace , 2014 .

[7]  Fábio Gonçalves Jota,et al.  APLICAÇÃO DE CONTROLE AVANÇADO NOS FORNOS DE REAQUECIMENTO DE PLACAS DA LINHA DE TIRAS A QUENTE DA USIMINAS , 2007 .

[8]  Andrés Sanz-García,et al.  GA-PARSIMONY: A GA-SVR approach with feature selection and parameter optimization to obtain parsimonious solutions for predicting temperature settings in a continuous annealing furnace , 2015, Appl. Soft Comput..

[9]  Luciano Lellis Miranda,et al.  MODELAGEM MATEMÁTICA DO PROCESSO DO FORNO DE REAQUECIMENTO WB2 DA LTQ DA ACESITA , 2006 .

[10]  You Wen Chen,et al.  Modelling and prediction for steel billet temperature of heating furnace , 2010 .

[11]  Silvia Maria Zanoli,et al.  Two-Layer Linear MPC Approach Aimed at Walking Beam Billets Reheating Furnace Optimization , 2017, J. Control. Sci. Eng..

[12]  Chonghun Han,et al.  Application of neural network to the supervisory control of a reheating furnace in the steel industry , 1998 .

[13]  J. Miller.  Numerical Analysis , 1966, Nature.

[14]  Tae Woong Yoon,et al.  Modeling and predictive control of a reheating furnace , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[15]  Qi Zhang,et al.  NSGA-II for slab selecting and reheating furnace scheduling in hot rolling production , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[16]  T. Hughes,et al.  Signals and systems , 2006, Genome Biology.

[17]  Andreas Kugi,et al.  Nonlinear model predictive control of a continuous slab reheating furnace , 2013 .

[18]  Yong-zai Lu,et al.  Dynamic model based control for reheating , 1988 .

[19]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[20]  Jin-Hua She,et al.  Integrated Hybrid-PSO and Fuzzy-NN Decoupling Control for Temperature of Reheating Furnace , 2009, IEEE Transactions on Industrial Electronics.

[21]  Dennis C. Smolarski.  Book Review: Numerical Methods , 2013 .

[22]  Wei Hsin Chen,et al.  Analysis on energy consumption and performance of reheating furnaces in a hot strip mill , 2005 .

[23]  Dapeng Niu,et al.  Modeling, Prediction, and Control of Heating Temperature for Tube Billet , 2015 .