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基于沙地猫群优化-最小二乘支持向量机的动态NOx排放预测

金秀章 史德金 乔鹏

中国电机工程学报2024,Vol.44Issue(1):182-190,中插15,10.
中国电机工程学报2024,Vol.44Issue(1):182-190,中插15,10.DOI:10.13334/j.0258-8013.pcsee.222144

基于沙地猫群优化-最小二乘支持向量机的动态NOx排放预测

Dynamic NOx Emission Prediction Based on Sandcat Swarm Optimization-least Squares Support Vector Machine

金秀章 1史德金 1乔鹏1

作者信息

  • 1. 华北电力大学控制与计算机工程学院,河北省保定市 071003
  • 折叠

摘要

Abstract

Aiming at the problem that the frequent peak shaving of thermal power units leads to the unstable combustion state of the unit,causing a large fluctuation range of NOx generation at the boiler outlet,a dynamic NOx emission model based on Sand Cat Swarm Optimization(SC SO)and Least Squares Support Vector Machine is proposed.First,the k-nearest neighbor mutual information is used to calculate the time delay and filter auxiliary variables.Then,based on SCSO algorithm,the order of input variable is selected.Using the information including the time delay and order of auxiliary variables as the input of the model,the SCSO algorithm optimizes the parameters of the least squares support vector machine and establishes the least squares support vector machine prediction model of dynamic NOx emission(SCSO-LSSVM dynamic soft sensing model).Finally,the model is compared with the LSSVM model without delay,the LSSVM model with delay and the dynamic soft sensor model with Particle Swarm Optimization algorithm to optimize the parameters of least squares support vector machine.The results show that,compared with other models,the SCSO-LSSVM dynamic soft sensor model established in this paper has the smallest root mean square error,the smallest mean absolute error and the smallest mean absolute error,while it has the highest prediction accuracy.Moreover,it can also predict the NOxproduction well when the NOx production fluctuates sharply,and has good dynamic characteristics.

关键词

NOx浓度/k近邻互信息/沙地猫群优化算法/最小二乘支持向量机/软测量模型

Key words

NOx concentration/k nearest neighbor mutual information/sandcat swarm optimization algorithm/least squares support vector machine/soft sensor model

分类

能源科技

引用本文复制引用

金秀章,史德金,乔鹏..基于沙地猫群优化-最小二乘支持向量机的动态NOx排放预测[J].中国电机工程学报,2024,44(1):182-190,中插15,10.

中国电机工程学报

OA北大核心CSTPCD

0258-8013

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