Neural Network Thermal Model of a Ladle Furnace

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 ap-