Neural Network Thermal Model of a Ladle Furnace

10th International Conference on Engineering Applications of Neural Networks

Patricia Teixeira Sampaio ; Antonio Padua Braga ; Takeshi Fujii

Others  :  http://CEUR-WS.org/Vol-284/page80.pdf
PID  :  21477

来源: CEUR

【 摘 要 】

Since the Brazilian inclusion in the global market, search for productivity and product quality improvement became essential for the companies to survive. However, due to energy costs rise, national steel industries are investing in electrical power generation in partnership with energy supply companies aiming at overall cost reduction. Therefore, actions that search for energy consumption reduction and productivity increase became priority for their research and development projects.The ladle furnace of V&M is one of the largest energy consuming units in the steel plant, consuming up to 2,400 MWh on average a month. Due to process complexity, system optimization became difficult to be implemented using conventional parametric approaches. However, applications of computational intelligence have been used as important alternative approaches to process modeling. Due to the little knowledge about the ladle furnace dynamics and the high variability of specific energy consumption, the use of neural networks was applied as a non parametric approach.This paper demonstrates the use of neural networks in complex industrial problems by applying it to the steel temperature prediction of the ladle furnace process. This paper shows that the neural network used yielded high generalization capability by obtaining smaller mean error on the test data than the expected error specified by the steel temperature measurement instrument. In addition, this paper shows that the use of this neural thermal model resulted in productivity increase, operational and energy cost reduction.