Prediction of dioxin emission concentration based on collaborative training decision tree

Abstract

Dioxin (DXN) is a kind of persistent organic pollutant with a cumulative effect. It is also one of the main reasons for "not in my back yard" effect in Municipal solid waste incineration (MSWI) plants. Real-time detection of DXN is helpful to realize emission reduction, optimize control, and eliminate oppose effect in MSWI process. However, there are very tiny label process data that can be used to construct data-driven prediction models due to the time and economic cost. In order to utilize the process data, this article presents a collaborative training decision trees (CTDTs) method for dioxin emission concentration prediction. First, the raw label process data is used to train the decision tree model, after that the process data is labeled. Second, the root mean square error of the labeled sample is calculated to select the optimal labeled and process data. Third, the DXN emission prediction model is constructed by cross-combination of the raw labels and labeled process data. Simulation results of the benchmark dataset and practical DXN data verify the effectiveness of the proposed method.