Calorific value prediction for coals based on a certain algorithm

LIU Lin; WANG Peng; ZHAI Yongjie; ZHOU Jielian

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Calorific value prediction for coals based on a certain algorithm

更新时间：2025-09-28

- Calorific value prediction for coals based on a certain algorithm
- Thermal Power Generation Vol. 44, Issue 2, Pages: 47-51(2015)
- 作者机构：
  
  1. 广东电网有限责任公司佛山供电局
  2. 广东电网有限责任公司电力科学研究院
  3. 华北电力大学自动化系
- 作者简介：
- 基金信息：
- DOI：
  CLC： TQ533.4
- Published：2015
- 稿件说明：
移动端阅览
LIU Lin, WANG Peng, ZHAI Yongjie, et al. Calorific value prediction for coals based on a certain algorithm[J]. Thermal power generation, 2015, 44(2): 47-51.
DOI：

LIU Lin, WANG Peng, ZHAI Yongjie, et al. Calorific value prediction for coals based on a certain algorithm[J]. Thermal power generation, 2015, 44(2): 47-51. DOI：

摘要

为了提高煤质发热量的预测精度

提出了基于参数自适应粒子群(PSO)和最小二乘支持向量机(LSSVM)算法的煤质发热量预测方法:建立LSSVM煤质发热量预测模型

通过参数自适应PSO算法优化LSSVM模型参数。使用测试数据对煤质发热量的预测效果进行了仿真比较。结果表明

基于参数自适应PSO-LSSVM发热量预测模型能够准确预测发热量的变化

且较参数自适应PSO-BP神经网络、基本PSO-LSSVM以及GA-LSSVM模型运算速度快

预测精度高

利于工程应用。

Abstract

In order to improve the prediction precision of calorific value of coals

a forecasting method based on parameters adaptive particle swarm optimization(PSO)and least squares support vector machine(LSSVM)algorithm was presented.Through optimizing the parameters of LSSVM algorithm by parameters adaptive PSO algorithm

aprediction model for coals' calorific value was established.Moreover

the predicted calorific values were compared with the test data.The simulation results show that

the method based on the parameter adaptive PSO-LSSVM prediction model can accurately predict the change of calorific value

which has faster calculation speed

higher precision and is suitable for engineering application

compared with the parameter self-adaptive PSO-BP model

the PSO-LSSVM model and the genetic algorithm(GA)-LSSVM model.

关键词

Keywords

references

Views

188

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Monitoring model for fired coal quality in utility boilers based on flue gas components

Experimental study on effects of coal property on peak load regulation characteristics of a supercritical 600MW unit

Application of PSO algorithm-modified Elman neural network in vacuum prediction for dual-pressure condensers

Model for predicting SCR flue gas denitration efficiency based on particle swarm optimization and twin support vector machine

Design of flue gas denitration control system based on feedforward constrained dynamic matrix control

Related Author

LI Huijun

WANG Xiaofeng

ZHANG Yongsheng

LEI Lin1

JIAO Qingfeng1

ZHANG Dongliang2 1.Hunan Electric Power Corporation Research Institute

ZHANG Liping

CHEN Haotian

Related Institution

State Grid Electric Power Research Institute Hebei Electric Power Company

School of Energy and Power Engineering,North China Electric Power University

Hunan Electric Power Company Scientific Research Institute

Zhuzhou Huayin Thermal Power Generation Co., Ltd

College of Electric Power, North China University of Water Resources and Electric Power

AI问答

⁰