Research on furnace explosion protection strategy of efficient pulverized coal fired boiler based on SVM
Pan Hao
Abstract：In order to improve the combustion stability of pulverized coal fired boiler,a furnace explosion protection strategy based on Support Vector Machine (SVM) was proposed in the paper.The state vector was constructed with key parameters of the boiler;afterwards a system state classifier was generated by training off-line history data of boiler with SVM,applying radial basis function and grid search algorithm.Besides,the oxygen content factor was introduced to regulate the training model.During running time of boiler,the classifier predicted pre-furnace state from on-line data then executed protection program through PLC (Programmable Logic Controller) module.Results show that when the oxygen content factor takes value of O.4,the maximum cross validation matching rate of the classifier is over 97％,the maximum predicting accuracy is over 95％,and mismatching rate is less than 10％.The protection strategy is able to identify the pre-furnace explosion state of boiler effectively as well as keeping low error judging rate under normal working state,and enhance the robustness of the system
Keywords：pulverized coal fired boilerfurnace explosion protectionsupport vector machinemachine learning
Publication Date：2017-01-01