燃煤锅炉尾部灰污预测模型及吹灰策略优化

发布时间：2019-06-21 09:29

【摘要】：在锅炉运行过程中,煤粉燃烧使对流受热面灰污激增、热传递受阻,烟气余热不能及时再利用,导致过热烟气排入大气,使锅炉输入能损耗严重。为了改善热传递,实现锅炉高效运行,吹灰是必不可少的措施。传统的吹灰是根据锅炉条件的反应或某些时间表进行的。吹灰器的操作人员很少或者没有结垢状态等信息,往往只会根据经验进行判断或根据时间表对锅炉进行吹扫。因此,为了缓解灰污沉积造成的换热过程不均衡,消除热偏差及高、低温腐蚀等问题,势必要监测锅炉尾部金属管道表面污染情况和发展趋势,并采取行之有效的吹灰方式。针对锅炉尾部金属管道不可避免的积灰、结渣等污染现象,以贵州黔西电厂24h数据为对象,进行相关灰污软测量、灰污预测及优化软测量监测等相关研究,开发了一套基于污染率预测模型的智能燃气脉冲吹灰系统。首先建立省煤器等的灰污监测模型,用污染率表征各部件污染情况,将环境复杂的锅炉尾部间接变为可视图表;然后针对各部件污染状态,建立相关预测函数,从而方便运行人员对锅炉运行状态及效率的掌握;最后在实时软监测基础上,开发了一套智能吹灰控制系统,该系统采用以上模型计算平台结果作为智能判断依据,上位机作为中间介质,连接软测量模型和吹灰器,以爆燃波作为输出。本课题建立污染率预测模型并设计了智能吹灰系统,利用某300MW锅炉换热面数据进行仿真分析,解析模型结果,分析积灰客观形成规律,检验该预测机制的合理性。在机组运行稳定情况下,上位机软件中依照灰污预测模型计算结果实行决策判断,经过控制单元指导吹灰装置吹扫,以达到燃煤锅炉尾部烟道智能清洁的目的。仿真效果充分表明该脉冲吹灰系统可稳定合理运行。该吹灰系统能够正确指导燃气脉冲吹灰器,能够有效解决锅炉尾部烟道吹灰紊乱问题,进而提升燃煤机组的安全性、经济性、可持续性。
[Abstract]:In the process of boiler operation, pulverized coal combustion causes the ash pollution of convective heating surface to proliferate, the heat transfer is blocked, and the waste heat of flue gas can not be reused in time, which leads to the discharge of overheated flue gas into the atmosphere, and makes the input energy loss of boiler serious. Soot blowing is an indispensable measure to improve heat transfer and realize efficient operation of boiler. Traditional soot blowing is based on the reaction of boiler conditions or some timetables. The operator of the soot blower has little or no information such as scaling state, and often only judges the boiler according to experience or purges the boiler according to the timetable. Therefore, in order to alleviate the imbalance of heat transfer process caused by ash deposition, eliminate thermal deviation, high temperature corrosion and other problems, it is necessary to monitor the surface pollution and development trend of metal pipe at the end of boiler, and adopt effective soot blowing method. Aiming at the inevitable pollution phenomena such as ash accumulation and slagging in the tail metal pipeline of boiler, a set of intelligent gas pulse soot blowing system based on pollution rate prediction model is developed by taking the 24 h data of Guizhou Qianxi Power Plant as the object, such as soft sensing of ash pollution, prediction of ash pollution and optimization of soft sensing monitoring. Firstly, the ash pollution monitoring model of economizer is established, and the pollution rate is used to characterize the pollution of each component, and the boiler tail with complex environment is indirectly transformed into visual chart, and then the relevant prediction function is established according to the pollution state of each component, so as to facilitate the operators to master the operation state and efficiency of the boiler. Finally, on the basis of real-time soft monitoring, a set of intelligent soot blowing control system is developed. The system uses the results of the above model calculation platform as the intelligent judgment basis, the upper computer as the intermediate medium, the soft sensing model and the soot blower, and the explosion wave as the output. In this paper, the pollution rate prediction model is established and the intelligent soot blowing system is designed. The heat transfer surface data of a 300MW boiler are used for simulation analysis, the results of the model are analyzed, the objective formation law of ash accumulation is analyzed, and the rationality of the prediction mechanism is tested. Under the condition of stable operation of the unit, the upper computer software carries out the decision judgment according to the calculation results of the ash pollution prediction model, and the ash blowing device is guided by the control unit in order to achieve the purpose of intelligent cleaning of the tail flue of the coal-fired boiler. The simulation results fully show that the pulse soot blowing system can run stably and reasonably. The soot blowing system can correctly guide the gas pulse soot blower, and can effectively solve the problem of soot blowing disorder in the tail flue of the boiler, and then improve the safety, economy and sustainability of the coal-fired unit.
【学位授予单位】：东北电力大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM621.2

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