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Prediction of Typical Flue Gas Pollutants from Municipal Solid Waste Incineration Plants

With rapid population growth and urbanization, municipal solid waste (MSW) generation rates are rising around the world. Incineration is considered as an effective way to deal with the growing demand for MSW disposal. The prediction of concentrations of pollutants generated from MSW incineration plants is a powerful support for waste incineration process control, which aims to reduce pollutant emissions. This paper proposes a two-stage prediction method based on the long short-term memory (LSTM) network to forecast typical flue gas pollutants of an MSW incineration plant in South China. In the first stage, an LSTM-based classification model is utilized to determine whether the pollutants are at a low level, and over-sampling strategy is applied to deal with the class-imbalance problem. At the second stage, an LSTM-based prediction model is built to further estimate the amounts of pollutants. Besides, the sliding average method is used to process the multi-scale raw data. Experiment results proved the effectiveness of the proposed method and indicated how different inputs affect the forecasting of pollutant emissions.