Air Preheater Monitoring And Economic Evaluation Of Coal-fired Power Station Air Preheater

Since entering the 21 st century,with the rapid population growth and rapid economic development in our country,the problems of large energy consumption,low utilization rate and excessive discharge of pollutants have become increasingly prominent.During the "12th Five-Year Plan" period,China has made some improvements in emissions-reducing and energy-saving.In the "13th Five-Year Plan",it put forward higher requirements on energy conservation and emission reduction tasks.Due to the energy structure of our country,coal-fired power generation will continue to assume the major power generation tasks as the main force in the power supply field for a long period of time in the future.As the core of thermal power generation,it is significant for coal-fired power plant boiler system to operate safely,stably and efficiently.However,the normal run of boiler system cannot be separated from the efficient operation of each auxiliary equipment.As an important equipment to preheat the air with flue gas energy and improve the thermal efficiency of the boiler at the rear of the boiler system,the efficiency of the air preheater greatly affects the efficiency of the unit.In this dissertation,coal-fired power plant boiler air preheater has been studied,and summary contents as follows:First,air leakage monitoring of air preheater has been research.Based on the analysis the mechanism model of air preheater under leaking condition,the selected air leakage rate,after comparing the two air leakage assessment models,is taken as the evaluation index of the leaking air condition of the air preheater.After selecting method of soft-sensing modeling and auxiliary optimization variables,and the air leakage forecasting model of air preheater is established by utilizing least-squares support vector machine theory.Furthermore,particle swarm optimization algorithm has been selected to optimize the model parameters.The simulation results,based on the actual operation data of coal-fired power plants,show that the proposed methods have good model fitting ability and the forecasting ability,which can meet the requirements of practical application.Then,the fouling monitoring of air preheaters has been researched.Based on the data obtained from the site of coal-fired power plant,the errors existing in the data were analyzed,and the solutions to the significant errors and random errors often found in thermal power data were proposed;A database of physical parameters of flue gas and working substance were established,and the physical parameter library through the established real-time component analysis module of flue gas were verified.According to the thermodynamic formula,the heat transfer model of the rotary air preheater is analyzed from the perspective of the reverse flue gas flow,and which construct the model of air preheater ash condition monitoring based on above work.Through the collected historical data of coal-fired power stations,the model was established and verified.The simulation results show that the model can effectively monitor the fouling of the heated surface of the air preheater.Finally,the economic evaluation of air preheater operating status has been investigated.Through the mechanism analysis of the real-time running status of the air preheater,the air preheater fouling,air leakage,thermal efficiency and power consumption have been monitored.An air preheater thermal efficiency model was established by artificial neural network algorithm.According to the power consumption of induced draft fan,blower fan and primary fan,an air preheater power consumption module was established.,Module evaluation of air preheater operational status was developed combined with the fouling and air leakage monitoring module described above.In the part of overall evaluation,the economic evaluation model of air preheater operating status was established based on the fuzzy neural network algorithm.The simulation results of the field data show that the model evaluation results are basically consistent with the actual situation,which proves the rationality of the evaluation model.