Heat-loss cycle prediction in steelmaking plants through artificial neural network

Abstract

A critical factor in steelworks concerns setting the steel release temperature from the ladle furnace. The challenge resides in estimating in advance the reduction the steel temperature will undergo during its non-processing time until the subsequent casting process. A poor estimation results in productivity and yield losses in casting and unnecessary energy consumption in the ladle. Given process complexity, a pure mathematical description is not available. This work develops a predictive neural model for the reduction in steel temperature between the ladle and the caster considering the main sources of heat losses. The case study refers to a steelmaking plant in Brazil. After model identification and validation, and a sensitivity analysis study, thirty troublesome steel runs that resulted in unplanned shutdowns during casting were investigated. The neural approach provided a correlation between factory-collected values and model estimates of 0.895, with a satisfactory Mean Absolute Error (MAE) of 3.03 °C , against 0.308 and 4.97 °C, respectively, given by the experimental plant model used by the process team, and ‒0.087 and 8.53 °C, respectively, obtained with a linear regression analysis used for comparison purposes. More reliable estimation of the reduction in steel temperature leads to more efficient and economic operations.