Soft Measuring Method of Dioxin Emission Concentration for MSWI Process Based on RF and GBDT

Abstract

Dioxin (DXN) is a highly toxic pollutant emitted during municipal solid waste incinerator (MSWI) process. In the actual industrial process, DXN emission concentration is measured through offline experiment analysis, which has shortcomings such as long time and high cost. In this study, a soft-sensing model of DXN emission concentration was established by using MSWI process variables. Random forest (RF) and gradient boosting decision tree algorithms are used to construct ensemble learning-based DXN model. First, RF tree sub-models are constructed base on random sampling and CART regression tree. Then, Gradient boosting decision tree (GBDT) method is used to each RF sub-model, in which one gradient iteration is performed to reduce the prediction error. Finally, a simple average combination strategy is performed on these RF and GBDT based sub-models. Thus, the soft measuring model of DXN emission concentration based on small samples and high-dimensional MSWI process data is obtained. The proposed method can both reduce model variance and eliminate prediction bias. The experimental results show that the proposed method can further improve the prediction performance and generalization ability.