基于最小二乘支持向量机的火电厂烟气含氧量预测模型优化研究
摘要：烟气含氧量是锅炉运行的重要监控参数，也是反映燃烧设备与锅炉运行完善程度的重要依据。根据运行工况快速、准确地测量烟气含氧量，对于优化锅炉燃烧过程具有重要指导意义。以某电站的1 000 MW超超临界锅炉的运行数据为基础，选取影响烟气排放的31个因素，分别采用交叉验证(cross validation，CV)、粒子群优化(particle swarm optimization，PSO)算法、遗传算法(genetic algorithm，GA)寻找最小二乘支持向量机(least squares support vector machine，LSSVM)模型的最佳参数，建立烟气含氧量预测模型。研究结果表明：相对于PSO-LSSVM和CV-LSSVM模型，GA-LSSVM预测模型对烟气含氧量具有更好的预测能力，具有预测精度高、泛化能力好、鲁棒性强等优点，拟合预测的相对误差、均方误差分别为0.54%、0.23%，泛化预测的相对误差、均方误差分别为1.66%、2.13%，能够比较准确地对火电厂锅炉烟气含氧量进行测量，为锅炉燃烧系统进一步的优化运行奠定了基础。
Abstract:The oxygen content in flue gas is an important monitoring parameter of boiler operation, and also an important basis that reflects the perfection of combustion equipment and boiler operation. Quickly and accurately measuring the oxygen content in flue gas according to operating conditions has important guiding significance for optimizing boiler combustion process. Based on the operation data of a 1 000 MW ultra-supercritical boiler in a power station, 31 factors affecting flue gas emission were selected, and cross validation (CV), particle swarm optimization (PSO) algorithm, and genetic algorithm (GA) were used to find the optimal parameters of the least squares support vector machine (LSSVM) model, and the prediction model of flue gas oxygen content was established. The research results show that, compared with PSO-LSSVM and CV-LSSVM models, GA-LSSVM prediction model has better prediction ability for flue gas oxygen content, and has the advantages of high prediction accuracy, good generalization ability, and strong robustness. The relative error and mean square error of fitting prediction are 0.54% and 0.23% respectively, and the relative error and mean square error of generalization prediction are 1.66% and 2.13% respectively. It can accurately measure the oxygen content of boiler flue gas in thermal power plants, which lays a foundation for further optimal operation of boiler combustion system.
标题：基于最小二乘支持向量机的火电厂烟气含氧量预测模型优化研究
title:Study on Optimization of Prediction Model of Flue Gas Oxygen Content in Thermal Power Plant Based on Least Squares Support Vector Machine
作者：赵国钦, 蓝茂蔚, 李杨, 周元祥, 江政纬, 甘云华
authors:Guoqin ZHAO, Maowei LAN, Yang LI, Yuanxiang ZHOU, Zhengwei JIANG, Yunhua GAN
关键词：火电厂,最小二乘支持向量机(LSSVM),粒子群优化(PSO)算法,遗传算法(GA),交叉验证(CV),
keywords:thermal power plant,least squares support vector machine (LSSVM),particle swarm optimization (PSO) algorithm,genetic algorithm (GA),cross validation (CV),
发表日期：2023-08-31
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