CO Emission Prediction Based on Kernel Feature Space Semi-Supervised Concept Drift Detection in Municipal Solid Waste Incineration Process

Carbon monoxide (CO) is a toxic pollutant emitted by municipal solid waste incineration (MSWI), which has a strong correlation with dioxins. In terms of the sustainable development of an ecological environment, CO emission concentration is strictly controlled by the environmental departments of various countries in the world. The construction of its prediction model is conducive to pollution reduction control. The MSWI process is affected by multi-factors such as MSW component fluctuation, equipment wear and maintenance, and seasonal change, and has complex nonlinear and time-varying characteristics, which makes it difficult for the CO prediction model based on offline historical data to adapt to the above changes. In addition, the continuous emission monitoring system (CEMS) used for conventional pollutant detection has unavoidable misalignment and failure problems. In this article, a novel prediction model of CO emission from the MSWI process based on semi-supervised concept drift (CD) detection in kernel feature space is proposed. Firstly, the CO emission deep prediction model and the kernel feature space detection model are constructed based on offline batched historical data, and the historical data set for the real-time construction of the pseudo-labeling model is obtained. Secondly, the drift detection for the CO emission prediction model is carried out based on real-time data by using unsupervised kernel principal component analysis (KPCA) in terms of feature space. If CD occurs, the pseudo-label model is constructed, the pseudo-truth value is obtained, and the drift sample is confirmed and selected based on the Page–Hinkley (PH) test. If no CD occurs, the CO emission concentration is predicted based on the historical prediction model. Then, the updated data set of the CO emission prediction model and kernel feature space detection is obtained by combining historical samples and drift samples. Finally, the offline history model is updated with a new data set when the preset conditions are met. Based on the real data set of an MSWI power plant in Beijing, the validity of the proposed method is verified.