A dynamic subspace model for predicting burn-through point in iron sintering process

Abstract

This paper presents a dynamic modeling method for predicting the exhaust-gas temperature (EGT) of the burn-through point (BTP) in an iron sintering process. First, a subspace modeling method is used to build a steady-state subspace model (SSSM) for the EGT at a steady state. Then, a dynamic subspace model (DSM) that is driven by the errors of the SSSMs is developed to improve the accuracy of the EGT prediction in a continuous process. Finally, a grid search dynamic subspace model (GSDSM) is established to find the best parameters for each SSSM in the DSM. Verification results show that the GSDSM yields a predicted EGT with a high precision, which can be implemented in a predicting controller an actual sintering process.