Quality-related process monitoring of ironmaking blast furnace based on improved kernel orthogonal projection to latent structures

Abstract

This paper proposes a novel improved kernel orthogonal projection to latent structures (KOPLS) for quality-related process monitoring of ironmaking blast furnace (BF). Firstly, the KOPLS algorithm is used as a preprocessing tool to remove the components which are not related to the molten iron quality (MIQ) in the process variable space. Then, the remaining process variable space is further orthogonally decomposed into the quality-related subspace and the quality-unrelated subspace by the post-processing method. Afterwards, the complete decomposition of the process variable space is achieved and each subspace has obvious correlation or irrelevance with MIQ. By designing appropriate monitoring statistical indicators in each subspace, it can clearly detect whether the fault is related to the MIQ. Aiming at the problem of fault identification based on the improved KOPLS, this paper further proposes a fault identification method based on robust reconstruction error. This method reversely estimates the normal values of the process variables in the original variable space based on the improved KOPLS model, and uses the reconstruction error of the process variables to construct the fault identification index. Numerical experiments and industrial experiments all verify the effectiveness and practicability of the proposed method.