Real-time prediction model of feed coal quantity for boilers based on power to coal coefficient
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Real-time prediction model of feed coal quantity for boilers based on power to coal coefficient
Thermal Power GenerationVol. 45, Issue 7, Pages: 61-66(2016)
作者机构:
华北电力大学能源动力与机械工程学院
作者简介:
基金信息:
DOI:
CLC:TM621.2
Published:2016
稿件说明:
移动端阅览
GU Junjie, ZHANG Yan, BAI Zhizhong, et al. Real-time prediction model of feed coal quantity for boilers based on power to coal coefficient[J]. Thermal power generation, 2016, 45(7): 61-66.
DOI:
GU Junjie, ZHANG Yan, BAI Zhizhong, et al. Real-time prediction model of feed coal quantity for boilers based on power to coal coefficient[J]. Thermal power generation, 2016, 45(7): 61-66.DOI:
Real-time prediction model of feed coal quantity for boilers based on power to coal coefficient
摘要
锅炉入炉煤的投入量均按照电厂设计煤种进行计算
而电厂燃煤煤种较杂、煤热值波动较大
使得控制锅炉入炉煤量的准确性成为难题。对此
本文采用功煤系数法
依据组合预测模型与方差倒数理论建立了入炉煤量的预测模型
求取待预测时刻前1h功煤系数对当前时刻(待预测时刻)功煤系数的最优权重
实现了对当前时刻功煤系数以及入炉煤煤量的预测
并利用该预测模型对某660MW机组锅炉入炉煤量进行预测。结果表明
用实时入炉煤量的预测值作为实际给煤量
其平均准确度可达到97%以上
表明本文提出的基于功煤系数的入炉煤量预测模型具有较好的应用价值。
Abstract
The feed coal quantity of boiler is calculated according to the design coal.However
due to the complexity of feed coal type and the large fluctuations of coal calorific value in power plants
accurately controlling the amount of coal into the furnace has become a difficult problem.Thus
this paper designed the self-adaptive coal quality feedforward correction
and established a prediction model for feed coal quantity
by using the coefficient of power to coal as a starting point
on the basis of the combined prediction model and variance reciprocal method.The optimal weight coefficient of the power to coal coefficient in the last one hour to the current power to coal coefficient was calculated
so the coefficient of power to coal and the amount of feed coal into the furnace can be predicted.Moreover
taking No.4boiler of the 660 MW unit in Guohua Cangdong Power Plant as the example
the feed coal quantity was predicted using the above model.The results show that
using the real-time predicted value as the actual feed coal amount
the average accuracy can reach up to 97%
indicating the proposed prediction model has a good prospect.