基于改良的鸡群优化算法优化锅炉NO_x排放质量浓度

NOxEmission Reduction of a Boiler Based on Ameliorated Chicken Swarm Optimization

为达到锅炉NO_x排放质量浓度最优的目标,针对鸡群优化算法收敛速度慢、容易早熟等缺点,对算法中母鸡和小鸡的觅食行为分别进行了修改,得到改良的鸡群优化(A-CSO)算法,并通过测试函数验证了A-CSO算法性能优于粒子群算法、引力搜索算法、磷虾群算法和原鸡群优化算法.利用A-CSO算法以及快速学习网建立锅炉NO_x排放质量浓度预测模型,对锅炉运行时的可调参数进行优化,获得锅炉燃烧优化调整方式.结果表明:优化后所有工况的NO_x排放质量浓度明显下降,相对下降率优于文献[12]中的结果;由于锅炉燃烧中飞灰含碳量的影响,可适当调整优化后的氧量和一次风量,以达到锅炉高效低污染燃烧.

To reduce the NOx emission of a boiler, an ameliorated chicken swarm optimization （A-CSO） algorithm was proposed by respectively modifying the foraging behaviors of hens and chicks in the original chicken swarm optimization algorithm to overcome the shortcomings of lower convergent speed and premature convergence, of which the perforrrlance was proved to be better than the particle swarm optimization algorithm, gravitational search algorithm, krill swarm algorithm and the original chicken swarm optimiza- tion algorithm through test function verification. Meanwhile, a prediction model was established for boiler NOx emission on the basis of A-CSO algorithm and fast-learning network, so as to optimize the adjustable parameters in boiler operation and obtain the way of boiler combustion optimization. Results indicate that the NO~ emission in all cases is significantly superior to that in the literature. Consideri combustion, it is proposed to appropriately y lowered after optimization, and the relative decline rates are ng the influence of unburned carbon in the fly ash from boiler adjust the optimized oxygen content and primary air flow to achieve high efficiency and low NOx emission of the boiler.