Prediction of NOx Emissions from Coal-Fired Boilers Based on Support Vector Machines and BP Neural Networks

摘要

The BP neural network and support vector machine (SVM) are respectively employed using operationtest data to establish models describing the NOx emission characteristics of a coal-fired boiler with theassistance of the intelligent MATLAB toolbox. The momentum method is employed to improve existingproblems within the BP neural network, and to choose the optimal kernel function of the SVM predictionmodel and the corresponding parameters c and g. The maximum error of the prediction model of theimproved BP neural network is 9.85% with an average error of 4.2%; the maximum error of the SVMprediction model after parameter optimization simulation is 4.57% with an average error of 2.15%.Results indicate that both modelling methods demonstrate improved accuracy and generalization.Finally, quantitative comparison analysis of the simulation and prediction results of the two modelsindicate that the supporting vector machine model is greatly superior to the neural network model interms of computing speed, fit and generalizability while requiring fewer thermal state data samplesfrom boiler operation.