发电技术2023，Vol.44Issue(6)：809-816,8.DOI:10.12096/j.2096-4528.pgt.22108
基于最小二乘支持向量机的电站锅炉高效率低NOx的多目标优化研究
Study on Multi-Objective Optimization of High-Efficiency and Low-NOx Emissions of Power Station Boilers Based on Least Squares Support Vector Machines
摘要
Abstract
Aiming at the multi-objective optimization of boiler combustion system,on the basis of the established prediction model of boiler combustion system,the weighted-particle swarm algorithm and the multi-objective particle swarm optimization(MOPSO)algorithm were used to optimize the adjustable operating parameters of the boiler,which can realize the operating state of the boiler with high efficiency and low NOx emission.The analysis shows that the operating parameters obtained by the two optimization algorithms are similar,and the trend is consistent with the combustion characteristics analysis and combustion adjustment test results.It indicates that the intelligent algorithm is effective and feasible to optimize the combustion system of the power plant boiler.However,the weighted-particle swarm optimization algorithm has serious subjective dependence.It is difficult to select appropriate weights,and the optimization time is long and the results are few.However,the optimization time of the MOPSO algorithm is far less than the optimization time of the weighted-particle swarm optimization algorithm,the optimization results are more,and the optimization efficiency is higher.Therefore,the MOPSO algorithm is more beneficial to guide the actual operation of the boiler.关键词
电站锅炉/多目标优化/加权-粒子群算法/多目标粒子群优化(MOPSO)Key words
power station boiler/multi-objective optimization/weighted-particle swarm optimization/multi-objective particle swarm optimization(MOPSO)分类
能源科技引用本文复制引用
梁中荣,蓝茂蔚,郑国,何荣强,屈可扬,甘云华..基于最小二乘支持向量机的电站锅炉高效率低NOx的多目标优化研究[J].发电技术,2023,44(6):809-816,8.基金项目
国家自然科学基金项目(52376108) （52376108）
广东省基础与应用基础研究基金项目(2020B1515020040). Project Supported by National Natural Science Foundation of China(52376108) （2020B1515020040）
Basic and Applied Basic Research Foundation of Guangdong Province(2020B1515020040). （2020B1515020040）
