摘要
在节能减排问题的研究中,电站锅炉燃烧优化控制问题的实质为在降低污染物排放的基础上提高锅炉效率的多目标优化问题。针对传统的加权法不能实现相互冲突的多个目标之间的协调问题,提出了采用多目标预测控制方法;并针对滚动优化关键问题的求解,提出了基于多目标蜂群算法的求解方法。首先说明了多目标蜂群算法的主要计算过程,然后详细阐述了滚动优化问题求解时的初始控制序列产生、约束条件处理与最优控制量选取方法。以NO x排放、锅炉效率及控制量的变化量作为优化目标,每步的控制量都是在综合考虑了多个目标的Pareto最优解集中按照某种准则选取的,从而可以实现各个目标之间的合理折衷。仿真结果表明,求解方法为锅炉燃烧优化控制提供了依据。
In the context of energy saving and emission reduction, the problem of boiler combustion optimization control is transformed into improving boiler efficiency and limiting pollutant emission simultaneously. In order to over- come the disadvantages of the traditional weighting method, the multiobjective predictive control method was intro- duced, and a new method based on multiobjective artificial bee colony （MABC） algorithm was proposed for solving the receding horizon optimization problem. The main procedure of the MABC was addressed first. After that, the ini- tialization method, constraint proposed method, and the selection of the optimized control sequence were all described in detail. By adopting the NOx emission, the boiler efficiency and the value of controlling variables as the optimiza- tion objectives, the valuesof controlling variables in each step were selected from the Pareto set according to a certain criterion. Simulation results show that the availability of the proposing method and a reasonable tradeoff among the muhiobjectives can be reached.
出处
《计算机仿真》
CSCD
北大核心
2013年第11期89-94,共6页
Computer Simulation
基金
金陵科技学院博士启动基金项目(jit-b-201218)
关键词
锅炉燃烧优化
多目标优化
人工蜂群算法
滚动优化
Boiler combustion optimization
Multiobjective optimization
Artificial bee colony algorithm
Recedinghorizon optimization
