基于最小二乘支持向量机的电站锅炉高效率低NO x 的多目标优化研究
摘要：针对锅炉燃烧系统的多目标优化，在所建立的锅炉燃烧系统预测模型的基础上，分别采用加权-粒子群算法和多目标粒子群优化(multi-objective particle swarm optimization，MOPSO)算法优化锅炉系统的可调整运行参数，以实现锅炉高效率低NO x 排放。分析表明，2种优化算法所得的运行参数相近，趋势与燃烧特性分析和燃烧调整试验结果相符合，说明智能算法优化电站锅炉燃烧系统有效可行。但是加权-粒子群优化算法主观依赖性严重，难以选取合适的权值，优化时间长且结果少；而MOPSO算法优化时间远远小于加权-粒子群算法优化时间，并且优化结果更多，优化效率更高，更有利于指导锅炉的实际运行。
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.
标题：基于最小二乘支持向量机的电站锅炉高效率低NO x 的多目标优化研究
title:Study on Multi-Objective Optimization of High-Efficiency and Low-NO x Emissions of Power Station Boilers Based on Least Squares Support Vector Machines
作者：梁中荣, 蓝茂蔚, 郑国, 何荣强, 屈可扬, 甘云华
authors:Zhongrong LIANG, Maowei LAN, Guo ZHENG, Rongqiang HE, Keyang QU, Yunhua GAN
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),
keywords:power station boiler,multi-objective optimization,weighted-particle swarm optimization,multi-objective particle swarm optimization (MOPSO),
发表日期：2023-12-31
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