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Prediction of NOx emissions based on CSO-RBF Neural Network for Municipal Solid Waste Incineration

Accurate prediction of nitrogen oxide (NOx) concentration is crucial in controlling NOx emissions in municipal solid waste incineration (MSWI) processes. In this study, a model based on radial basis function (RBF) neural network and competitive swarm optimization (CSO) algorithm is proposed to predict the NOx concentration ahead of time with high accuracy. First, by considering the continuous change of NOx concentration, the embedding dimension of NOx for model inputs is determined. Then, the RBF neural network whose parameters are optimized by the CSO algorithm is utilized to establish the prediction model, aiming to improve the prediction accuracy. Finally, simulation experiments are carried out on the benchmark data and real industrial data. The comparison results illustrate that the prediction accuracy of the model proposed in this study is higher than basic RBF model and PSO-RBF model.