煤炭工程 ›› 2013, Vol. 45 ›› Issue (12): 112-115.doi: doi:10.11799/ce201312039

• 研究探讨 • 上一篇    下一篇

基于参数优化支持向量机的煤泥输送管道压力预测

杨学存   

  1. 西安科技大学
  • 收稿日期:2013-02-19 修回日期:2013-03-01 出版日期:2013-12-10 发布日期:2013-12-10
  • 通讯作者: 杨学存 E-mail:xkdyangxuecun@163.com

The pressure prediction of coal slurry pipeline based on the parameter optimization of support vector machine

  • Received:2013-02-19 Revised:2013-03-01 Online:2013-12-10 Published:2013-12-10

摘要:

针对煤矸石热电厂煤泥输送管道堵塞问题,通过对实际现场的分析,确定煤泥输送系统浓料泵主缸压力预测是进行管道堵塞预测的必要前提,提出了基于网格法的支持向量机(GSVM)的浓料泵主缸压力预测模型,将网格搜索法用于支持向量机的参数优化。仿真结果表明,该GSVM预测模型参数寻优时间为6.55s,预测模型稳定,且相对误差在3%以内,能满足实际工程要求。与基于遗传算法的支持向量机(GA-SVM)预测模型相比较,GSVM预测模型在寻优时间和搜索稳定性方面优于GA-SVM预测模型,能够应用于煤泥输送系统实时压力预测系统。

关键词: 煤泥输送管道, 堵塞, 参数优化, 支持向量机

Abstract:

According to the coal slime pipeline blockage problem of coal gangue thermal power plant, after the analysis of the actual scene, it is sure that thick slurry pump master cylinder pressure prediction is the necessary premise of blockage prediction. The thick slurry pump master cylinder pressure prediction model is proposed, which is GSVM (support vector machine with grid method), grid search method is used to parameters optimization of SVM. The simulation results show that parameter optimization time of this GSVM forecast model is 6.55 seconds, its prediction model is stable, and the relative error is less than 3%, which can satisfy the engineering requirement .And compared with prediction model based on GA-SVM(The genetic algorithm support vector machine), The GSVM prediction model is better than GA-SVM one in the optimization time and stability, and can be used in real time pressure prediction of the coal conveying system.

Key words: coal slurry pipeline, blockage, parameter optimization, support vector machine