论文标题:锅炉灰污监测和吹灰优化系统的研究与开发
Study on Fouling Monitoring and Sootblowing Optimization System for Power Plant
论文作者
论文导师 徐治皋,论文学位 硕士,论文专业 热能工程
论文单位 东南大学,点击次数 618,论文页数 68页File Size13795K
2006-03-01论文网 http://www.lw23.com/lunwen_12912597/
coal-fired boiler; fouling; cleanliness factor; real-time monitoring; Neural network; sootblowing schedule; sootblowing optimization
国内电站锅炉主要以煤作为燃料,燃煤锅炉在运行过程中,各受热面不可避免地会出现灰污现象。灰污的形成对机组的安全性和经济性均会产生不利影响。吹灰是清除灰污和保持锅炉受热面传热性能的一种有效手段,但目前电厂中,吹灰器的运作大多是由运行人员根据经验进行控制,定时将各受热面全部吹扫一遍,很难确定出吹灰效益。本文针对现状,从经济角度考虑,深入研究建立了各受热面的灰污状态监测模型,开发出吹灰优化软件。主要内容如下: 本文基于电厂DCS系统的监测数据,提出了一套完整的电站锅炉受热面结渣积灰监测方法,用以在线监测锅炉结渣和积灰的程度。根据各受热面传热方式的差异建立不同的在线监测模型,并统一用清洁因子来描述各受热面的灰污程度。 在对流受热面灰污监测方面,建立了适用于各不同工况的基于热平衡原理的受热面积灰监测模型;对于尾部受热面,建立了基于烟气压差法的积灰监测模型;对于辐射受热面,采用人工神经网络法对锅炉炉膛的污染监测问题进行了研究,采用BP算法对锅炉受热面灰污监测模型进行训练,实现了炉膛辐射受热面结渣的整体监测。 基于锅炉各受热面的实时监测模型,本文在保证设备安全运行的前提下,从经济性出发,提出了基于临界清洁因子确定最佳吹灰时机和根据吹灰收益最大原则确定最佳吹灰周期,并给出基于吹灰最佳频率确定临界清洁因子的方法,给出制定合理的吹灰方案的步骤。 在理论研究的基础上,作者开发出电站锅炉吹灰优化系统,并实际应用于大唐洛河发电厂#1炉。大量试验表明,本文所建的模型能够准确地监测锅炉灰污的发展状况,并能起到指导和优化吹灰的作用。
Domestic power stations mostly take coal as their fuel. During the operation of coal-fried boiler, the interior surfaces inevitably encounter fouling problems. Fouling can has an adverse effect on the security, reliability and economy of units. Sootblowing is an effective method to clean fouling and enhance boiler heating surfaces performance. But operations of the sootblowers are based on people’s experice. It is hard to ensure the benefit by sootblowing every interior surfaces time.Base on Economics, this papers lucubrate the application of fouling monitoring and sootblowing optimization. In this paper a slagging and fouling monitoring system is put forward to monitor the degree of boiler. According to the data of the DCS System, different models are constituted because of the different heating-fashions. To make things comprehensible,use CF to monitor all the parts"s fouling in application. As to the fouling monitoring of convective surfaces, a fouling monitoring model is developed based on the heat balance theory. A model based on the difference of flue gas pressure is developed to monitor air-heater"s fouling. The focus of this thesis is on investigating the application of neural network approaches to monitoring ash fouling. The monitoring ash fouling model is trained through a Back Propagation (BP) algorithm. Therefore, the arithmetic is greatly improved and more suitable for the learning of neural networks by improved BP networks. By this arithmetic radiate heating surface is monitored. Based on fouling monitoring of boiler,this papers studied on method based on minimal cleanliness factor to determine sootblowing opportunity, method based on best frequency to calculate critical cleanliness,method base on the principle of maximal heat income caused by sootblowing to determine sootblowing period, method based on the principle of safety first to determine sootblowing schedule. All the above methods together can determine the correct schedule to sootblowing system. Based on these theories,this paper design the software of sootblowing optimization system of First Boiler of LuoHe Power Plant. A large number of soot-blowing experiments are taken. The results show that the models could correctly reflect the alteration of the smudginess coefficiency and give the advice of on-line soot-blowing to the operators.
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