循环流化床锅炉燃烧系统优化控制的研究与实现

发布时间：2019-07-08 16:51

【摘要】：循环流化床(CFB)锅炉燃烧技术作为一种新型的燃烧方式,因为其燃烧效率高、燃料适应范围广、负荷调节性能好、炉内组织脱硫等诸多优点,在电力、供热、工业蒸汽生产中得到了广泛的应用。但是CFB锅炉燃烧系统又是一个分布参数、非线性、时变、大滞后、多变量耦合的被控对象。燃烧控制难,自动投运率低。因此对CFB锅炉燃烧系统优化控制的研究有很大的实际意义。本文首先对循环流化床锅炉燃烧系统的结构和燃烧特性进行了分析,其主要特点包括大惯性、强耦合、非线性等,在此基础上对燃烧系统主要热工参数的控制方法进行了研究。如今虽然机理建模技术已经非常成熟,但是机理建模得到的模型不适合于控制器优化。因此在设计优化控制策略前,利用机组历史数据和仿真机系统,对系统进行实验建模。鉴于燃烧控制系统的实验建模过程是一个需要辨识多维变量的复杂寻优问题,文章采取了蚁群优化算法,这为后面的实验建模奠定了良好的基础。在接下来的工作中,按照实验建模的一般方法,利用上述蚁群优化算法求得模型的传递函数并利用与辨识数据无关的其他数据来验证所求模型传递函数的有效性。最后通过利用一次风调节的快速性来控制床温,利用调节给煤机的供给速度来控制主汽压,以此对原有DCS中床温控制回路和主蒸汽压力控制回路的SAMA图进行优化,并将前馈补偿控制的原理运用在燃烧系统控制中。结合上述控制方案和辨识得到的对象模型,利用蚁群优化算法,对控制器参数进行了优化。通过对优化前后的控制效果图进行比较,优化后的控制系统不仅能提高锅炉控制系统的自动化水平,还对减排降耗提高能源利用率具有重要意义。
文内图片：

图片说明：主蒸汽压力前馈控制 SAMA 图
[Abstract]:As a new combustion mode, circulating fluidized bed (CFB) boiler combustion technology has been widely used in electric power, heating and industrial steam production because of its high combustion efficiency, wide range of fuel adaptation, good load regulation performance, desulphurization in furnace tissue and so on. However, the combustion system of CFB boiler is a controlled object with distributed parameters, nonlinear, time-varying, large lag and multivariable coupling. Combustion control is difficult and automatic operation rate is low. Therefore, it is of great practical significance to study the optimal control of CFB boiler combustion system. In this paper, the structure and combustion characteristics of circulating fluidized bed boiler combustion system are analyzed, its main characteristics include large inertia, strong coupling, nonlinear and so on. On this basis, the control method of main thermal parameters of combustion system is studied. Although the mechanism modeling technology has been very mature, the model obtained by mechanism modeling is not suitable for controller optimization. Therefore, before designing the optimal control strategy, the experimental modeling of the system is carried out by using the historical data of the unit and the simulator system. In view of the fact that the experimental modeling process of combustion control system is a complex optimization problem that needs to identify multidimensional variables, ant colony optimization algorithm is adopted in this paper, which lays a good foundation for the later experimental modeling. In the next work, according to the general method of experimental modeling, the transfer function of the model is obtained by using the above ant colony optimization algorithm, and other data independent of the identification data are used to verify the effectiveness of the transfer function of the model. Finally, the SAMA diagram of the bed temperature control loop and the main steam pressure control loop in the original DCS is optimized by using the rapidity of the primary air regulation to control the bed temperature and the main steam pressure control circuit by adjusting the supply speed of the coal feeder, and the principle of feedforward compensation control is applied to the combustion system control. Combined with the above control scheme and the identified object model, the ant colony optimization algorithm is used to optimize the controller parameters. By comparing the control effect diagram before and after optimization, the optimized control system can not only improve the automation level of boiler control system, but also play an important role in reducing emission and energy utilization.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP273;TM621.2

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