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Iterative learning control in electric arc furnaces Disclosure Number: IPCOM000029476D
Publication Date: 2004-Jun-30
Document File: 12 page(s) / 60K

Publishing Venue

The Prior Art Database

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                                                                                                                                                                        Invention Disclosure Form


Applications: Steel Industries

Summary: (abstract 50-100 words)

The Electric Arc Furnace (EAF) is a batch process used to produce steel from several iron sources. The quality of the steel is determined with the steel composition and its temperature at the end of the heat. The quality of the raw materials varies and many of the variations cannot be measured, hence, reaching a final product quality at the end of the heat (batch) is not a straightforward task. The amount of measurements available in the EAF is very limited and the process is so complex that model based techniques for improving the performance of this operation are still scarce. Iterative Learning Control (ILC) is proposed in this invention disclosure to improve the regulatory control of the steel quality at the end of the heat. ILC uses the available off-line measurements and the results from previous heats to learn the behavior of a given EAF. The control equation utilizes an identification procedure to capture the unknown dynamics of the process. The ILC calculates the oxygen and carbon addition rates needed to achieve the desired quality specifications despite the process variations.

Prior Art and Problems:

The Electric Arc Furnace (EAF) operation has evolved over the years with significant improvements. One of the most important is the use of energy from chemical reactions to replace part of the electric power input and improve the overall energy consumption (Jones, 1995). The use of oxygen burners assists in the melting operation by transferring heat to the scrap. Oxygen is also used in jet burners to homogenize the temperature in the melt and oxidize combustion products.

The overall optimization of the EAF operation can be directed towards the electric (arc) system or to the chemical system. The electrode optimization can be as simple as using longer electrodes to maximize their lifetime (Carlisle, 1998) or an improved electric system to reduce flickering (Samuelsson, P. et al. 1995). Another approach to optimize the EAF operation is by maximizing the heat recovered from the chemical products. Oxy-fuel burners, carbon additions, foamy slag and post-combustion are different techniques that combine chemical reaction effects to obtain energy that is used for the melt, increase the EAF efficiency and increase the quality of the melt (Grant, M., et al, 2000; Vonesh, F.A., et. al., 1995). A different approach to optimize an EAF consists in feeding liquid hot metal continuously (Heard, 1998). A steady state model of mass and energy is used to assess the benefits of this modification.

The quality of the steel depends heavily on the properties of the raw materials and...