Nonlinear Prediction Model of Blast Furnace Operation Status
Originally presented at 30th European Symposium on Computer Aided Process Engineering (ESCAPE30). P. Azadi, S. Ahangari Minaabad, H. Bartusch, R. Klock, S. Engell.
Abstract:
The operation status of a process in the steel industry is mainly defined by three aspects, efficiency, productivity and safety. It provides guidance for the operators to make decisions on their future actions. The abrasive process environment inside a blast furnace (BF) makes it demanding to analyse the operation status by direct internal measurements. The blast furnace gas utilization factor (ETACO) is an essential indicator of the process efficiency. Besides efficiency, productivity and safety can, to some extent, be derived from the pressure drop (DP) and the top gas temperature (TG). This paper presents a nonlinear autoregressive network with exogenous inputs (NARX) model for the simultaneous multistep ahead prediction of ETACO, DP and TG, based upon a new set of fast and slow dynamic input attributes. Validation results using real industrial plant measurements show that this approach not only enables monitoring of the current operation status but also provides prediction capability by including the slow dynamics of the blast furnace into the model.