Study On Soft Sensor Method Of Oxygen Content In Flue Gas Of Coal-fired Power Plant

Electricity is a vital strategic resource,which is related to the lifeline of national economy and national energy security.As the main body of thermal power generation,improving quality and efficiency to reduce pollution is an important measure of supply-side structural reform.The oxygen content of flue gas is an important parameter reflecting the ratio of air to coal,an important index for boiler thermal efficiency calculation and system optimization,and a prerequisite for ensuring the economy and safety of boiler system.However,the measurement of oxygen content in flue gas has many problems,such as high cost,complex process,easy damage to sensor,low timeliness and decreasing precision.Based on a coal-fired power plant in Yulin,Shaanxi province,this paper builds a database based on boiler technology and makes an in-depth study on soft measurement modeling methods of machine learning and deep learning.The specific research content is as follows:1)The chemical principle of oxygen content in flue gas and boiler technology were analyzed and field investigation was carried out.The oxygen content of flue gas was taken as the dominant variable and a reasonable auxiliary variable was selected.At the same time,the actual production data of unit 1 of subcritical natural circulation boiler of 200MW power plant were collected.All the sample data were preprocessed,and the grey relational analysis(GRA)model was introduced to select the auxiliary variables,and the model database was constructed by dividing the data sets.2)Aiming at the traditional machine learning modeling method,the support vector machine(SVM)model is firstly established.Secondly,for the SVM parameter optimization problem,the particle swarm optimization(PSO)is fused and the adaptive weight,asynchronous learning and compression factor are added to improve the PSO.Then embedded in the SVM to construct improved PSO-SVM soft sensor model to optimize the penalty factor quickly.And can effectively avoid the problem of particle swarm falling into local optimal solution or falling into stagnation.3)Aiming at the modeling method of deep learning,a soft sensor model based on long short-term memory(LSTM)is proposed.Firstly,to solve the problem of network model optimization,a hyper-parameter j oint optimization strategy was proposed.Based on global and local optimization of units,time step and LSTM layers,an improved PSO-LSTM soft sensor model with better performance was constructed.Secondly,simulation experiments and comparative analysis show that this model has higher accuracy,simple optimization and better generalization performance than the improved PSO-SVM model.Finally,the model is integrated into the practical application of coal-fired power plant.Based on PyQt5 design,a soft sensor system based on LSTM is developed.The method of soft sensor of oxygen content in flue gas has important practical significance.The method presented in this paper meets the demand of high precision monitoring of oxygen content in flue gas of coal-fired power plants,and can be used as an effective method to replace the zirconia sensor.