Online Soft Sensing of Dioxin Emission Based on Fast Tree BLS and Robust PCA

Municipal solid waste incineration (MSWI) is a crucial technology for waste treatment in densely populated cities. It plays a vital role in contributing to the hot concept of waste-to-energy. However, the effectively measuring of dioxins (DXN) emission from MSWI plants presents a complex challenge duo to its high economical cost and large lag time. To address the challenge, we propose a soft sensing method of DXN emission concentration based on the fast tree broad learning system (FTBLS) and the robust principal component analysis (RPCA). FTBLS can swiftly construct the DXN emission model with increment learning for obtaining accurate measuring results. RPCA is capable decomposing high-dimensional small sample data into low-rank and noise matrices, achieving robust operation condition drift detection in the presence of noise process data. The similarity estimation is used to aid the soft measuring value's obtainment for concept drift sample. The experiment and application results demonstrate the effectiveness of our proposed online soft sensing approach.