SCR脱硝系统多目标优化控制研究

Study on Multi-objective Optimal Control of SCR Denitrification System

燃煤机组面临着灵活运行和超低排放的双重压力,机组快速深度变负荷对选择性催化还原(selective catalytic reduction,SCR)脱硝系统的控制提出了更高要求。提出一种兼顾超低排放和经济成本的多目标优化控制方法,将脱硝成本加入优化目标函数,采用预测控制结构,结合神经网络和遗传算法进行模型建立和控制量寻优,实现了喷氨量的优化控制。仿真结果表明,该方法在满足排放标准的同时降低了脱硝成本,并能适应锅炉大范围变工况运行。

Coal-fired units are faced with the dual pressures of flexible operation and ultra-low emissions. The rapid and deep load change of the units puts forward higher requirements for the control of the SCR(Selective Catalytic Reduction) denitrification system. This paper proposed a multi-objective optimization control method that takes into account ultra-low emissions and economic costs. The cost of denitration was added to the optimization objective function, and a predictive control structure was adopted. The neural network and genetic algorithm were also separately used to establish the model and optimize the control value to achieve the ammonia injection optimization. The simulation results show that the method reduces the cost of denitration while ensuring the emission standards, and can adapt to the large-scale variable operating conditions of the boiler.