A CEEMDAN-based Stacking Ensemble Learning Method for SO2 Emission Forecast in a Wet FGD Process

Abstract

There has recently been increasing attention paid to sulphur dioxide (SO₂) pollution owing to its hazardous effect on both human health and atmospheric environment. To handle this problem, the wet flue gas desulphurization (FGD) system has found wide applications in SO₂ emitting industries. Accurate prediction of SO₂ emissions in treated flue gas serves the purpose of providing timely operating guidance for the FGD system. However, the wet FGD process is characterized by highly nonlinear dynamics and non-stationarity, which poses significant difficulties and limitations for traditional modeling methods. To address above issues, in this article, an integrated model is proposed to perform SO₂ emission forecasting for an FGD process. Our integrated model comprises a multiplicity of techniques, including complete ensemble empirical mode decomposition  with adaptive noise stacking ensemble learning (SEL) and permutation-based entropy (PEN). The serves as decomposing SO₂ emission signal, then the complexity of each decomposed sub-series is analyzed by PEN and ones with similar scores are combined, finally a stacking-based ensemble learning model which incorporates different types of member models are developed for modeling purposes. The proposed method was validated and evaluated by measurements of a real FGD system in a 600MW coal-fired unit, and experimental results illustrate the superiority of our method.