STUDY ON SOFT-MEASUREMENT OF QUALITY FOR FURNACEENTERING COAL BASED ON GENETIC NEURAL NETWORK

JU Lincang; LI Lei; ZHAO Qiang College of Energy and Power Engineering

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STUDY ON SOFT-MEASUREMENT OF QUALITY FOR FURNACEENTERING COAL BASED ON GENETIC NEURAL NETWORK

Thermal Power Generation Vol. 40, Issue 3, Pages: 24-27(2011)

更新时间：2025-09-28

- STUDY ON SOFT-MEASUREMENT OF QUALITY FOR FURNACEENTERING COAL BASED ON GENETIC NEURAL NETWORK
- 作者机构：
  
  西安交通大学能源与动力工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183;TM621.2
- Published：2011
- 稿件说明：
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JU Lincang, LI Lei, ZHAO Qiang College of Energy and Power Engineering. STUDY ON SOFT-MEASUREMENT OF QUALITY FOR FURNACEENTERING COAL BASED ON GENETIC NEURAL NETWORK[J]. Thermal Power Generation, 2011, 40(3): 24-27.
DOI：

JU Lincang, LI Lei, ZHAO Qiang College of Energy and Power Engineering. STUDY ON SOFT-MEASUREMENT OF QUALITY FOR FURNACEENTERING COAL BASED ON GENETIC NEURAL NETWORK[J]. Thermal Power Generation, 2011, 40(3): 24-27. DOI：

摘要

基于软测量的非机理建模原理

利用遗传算法结合人工神经网络建立了煤质在线软测量模型。确定了BP网络与遗传算法(GA)两者结合的建模方式。分析了原煤从进入制粉系统到完全燃烧及排出的整个过程

得到BP网络模型的输入和输出节点参数集;通过对权参数初值进行实数编码

设计了基于实数编码的GA-BP算法流程

并在Visual Studio2005开发平台上进行了GA-BP算法程序编制及其调试。使用山西某电厂的200 MW机组实时运行数据进行模型训练和检测

结果表明煤质软测量模型可以较为准确地预测煤质参数。

Abstract

Based on the non-mechanism modelling principle for soft-measurement

an on-line soft-measuring model used for coal quality has been established by using genetic algorithm(GA) combined with artificial neural network.The BP neural network and GA have been presented

and the modelling mode of their combination being determined.The whole process

including the raw coal entering into the coal-pulverizing system to being fully burnt-up and discharged away has been analysed

obtaining a collection of parameters at the input and output nodal points

through actual number programming of initial values in weighing parameters

the process of GA-BP algorithm based on actual number programming has been designed

and the working-out of GA-BP algorithm programming and its debugging are completed on the developing platform Visual Studio 2005.By using real-time operation data of a 200 MW unit in one power plant of Shanxi Province

the model training and detection have been carried out

results show that the said model can more accurately predict the coal quality parameters.

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