东北电力技术2025，Vol.46Issue(9)：56-62,7.
基于PrePSO-LSSVM模型的SCR系统NOx出口浓度预测
Prediction of NOx Concentration in SCR System Based on PrePSO-LSSVM Model
摘要
Abstract
According to the issues of low accuracy and insufficient generalization ability of NOx concentration prediction models in se-lective catalytic reduction(SCR)systems,it establishes a least squares support vector machine(LSSVM)prediction model based on the progressive particle swarm optimization algorithm(PrePSO).Through dual innovation enhancing predictive performance,firstly,it uses the optimal Latin hypercube sampling for particle initialization,utilizing its spatial filling properties to evenly cover the design domain,effectively avoiding local aggregation caused by traditional random initialization.Secondly,it dynamically adjusts the inertia weights and learning factors to construct a nonlinear parameter update mechanism that balances global exploration and local development capabilities.Finally,based on the operating data of a 660 MW unit in a power plant,it constructs a PrePSO-LSSVM model.Through three error indicators,it compares and analyzes the predictive performance of the PrePSO-LSSVM model with LSSVM and PSO-LSSVM models.The results show that the improved model improves the prediction accuracy by more than 30%compared to the basic model.This method provides an efficient predictive tool for precise control of NOx in SCR systems and has important engineering application value in reducing ammonia escape rate.关键词
粒子群优化算法/最小二乘支持向量机/预测模型/NOx浓度Key words
particle swarm optimization algorithm/least squares support vector machine/prediction model/NOx concentration分类
信息技术与安全科学引用本文复制引用
崔哲,马铭宏,薛永锋,张海涛,于强..基于PrePSO-LSSVM模型的SCR系统NOx出口浓度预测[J].东北电力技术,2025,46(9):56-62,7.基金项目
国家能源集团科学技术研究院有限公司科技资助项目(SY2025Y001) （SY2025Y001）
