电站锅炉低氮燃烧优化控制基础研究

Combustion optimization plays an important role in improving the efficiency of existing boilers while reducing NOx emission. With the wide applications of low NOx and selective non-catalytic reduction (SNCR) or selective catalytic reduction (SCR) techniques, higher demands are raised for the adjustment and operation of the combustion system. This project proposes a new optimization control approach for low NOx combustion system by using multiobjective model predictive control, in which the economic and dynamic performance indices are integrated into one optimization problem to balance among the safety, costs and emissions, so that dynamic optimization of the combustion system is achieved by manipulating operational variables. The main contents of this project are as follows. Firstly, a grey-box modeling method is employed to establish the dynamic models of the low NOx combustion system; then the formulation of the multiobjective dynamic optimization problem is studied in detail, as well as its solving algorithms, and disturbance rejection method; at last, the temperature measurement based on an acoustic signal will be investigated to meet the stringent temperature limitations in the injection zone of SNCR.

燃烧优化是实现现役火电机组节能减排的重要手段。当前电站锅炉普遍采用了低氮燃烧和SNCR/SCR联合脱硝技术，因此对燃烧系统的运行和调整提出了更高要求。项目紧密结合实际脱硝方案和工艺要求，综合考虑系统整体的安全性、经济性和NOx排放，提出基于多目标预测控制的低氮燃烧优化控制方法，把经济性指标和动态性能指标统一在同一框架下进行滚动优化，从而实现低氮燃烧系统的动态优化控制。 项目首先利用灰箱建模方法建立低氮燃烧系统的动态数学模型；接着研究多目标经济性动态优化控制问题的构建、求解和扰动补偿等问题；最后为满足SNCR炉内反应温度限值的要求研究基于声波信号的炉膛温度测量方法。

我国以煤炭为主的资源禀赋，以及消纳间歇性可再生能源的需要，决定了燃煤发电在中长期内仍将保持主力电源地位。燃烧优化控制无需进行设备改造，仅通过对运行参数进行优化，达到提高锅炉效率，同时减少NOx等污染物排放的目的，是现役机组最直接的节能减排手段。. 项目围绕燃烧过程建模、闭环动态燃烧优化问题的构建和求解、炉膛温度场测量等关键科学问题开展了研究工作，建立了包含炉内一维烟气温度模型，以及一维NOx模型的、完整的燃烧系统灰箱模型，为燃烧优化控制系统的设计和验证打下了良好基础；提出了具有自学习功能的低氮燃烧多目标经济预测控制方法，克服了原有燃烧优化方法仅能实现开环控制和稳态优化，无法适应负荷和煤质变化的缺陷；开发了基于电动声源信号的炉膛温度测量系统，并改进了声波飞渡时间计算方法和温度场重建算法，为炉膛温度场监测提供了新的手段。. 项目部分成果已经在某1000MW超超临界锅炉获得了成功应用，取得了良好的经济效益和社会效益。