周鑫,吴佳
ZHOU Xin,WU Jia

• 1:浙江浙能台州第二发电有限责任公司

摘要(Abstract)：

SCR(选择性催化还原)是发电厂目前普遍采用的烟气脱硝方法,脱硝系统的喷氨量不仅影响烟气脱硝的效率,过量喷氨也会造成氨逃逸率升高,导致环境的二次污染。SCR系统反应机理复杂,具有非线性、大惯性等特点,传统PID控制方法无法实现喷氨量的精确控制。将KPLS(核偏最小二乘方法)与GA(遗传算法)结合,提出了GA-KPLS建模方法,并建立了SCR系统模型。仿真结果表明,模型具有较好的学习及泛化能力。将SCR模型应用于模型预测控制方法中,实时计算最优喷氨量,实现了对喷氨量的精确控制。实验结果表明,此方法与传统PID控制方法相比,显著提高了脱硝率,同时降低了氨逃逸率。
Selective catalytic reduction(SCR) method is usually used for flue gas denitration in thermal power plant. Spraying ammonia flow of denitration system can affect the efficiency of flue gas denitration And excess ammonia spraying results in higher rates of ammonia escape which cause secondary pollution of the environment. Reaction mechanism of SCR system is very complex and it has the characteristics of nonlinearity and large inertia. Therefore, it is difficult for the traditional PID control methods to achieve precise control of the amount of ammonia injection. Combining the kernel partial least squares(KPLS) and genetic algorithm(GA), GA-KPLS modeling method is proposed and the SCR system model is established. The simulation results show that the learning and generalization abilities of the model are both better. In order to precisely control the amount of ammonia spraying, model predictive control method is used to calculate the real-time optimal amount of ammonia spraying. Experimental results show that compared with traditional PID control this method significantly improves the denitration rate as well as reduces the ammonia escape rate.

关键词(KeyWords)： 选择性催化还原;烟气脱硝;核偏最小二乘;遗传算法;最优控制
selective catalytic reduction;flue gas denitration;kernel partial least squares;genetic algorithm;optimal control

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 周鑫,吴佳
ZHOU Xin,WU Jia

DOI: 10.19585/j.zjdl.2015.11.005

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