摘要
以某燃煤电厂水务系统为研究对象,对机组运行参数和水量历史数据进行筛选和关联性分析,根据前期水平衡测试结果,结合响应面分析验证,发现机组负荷、蒸发损失系数、浓缩倍率和循环水温升这四个因素能够对全厂供水量产生关键性影响。基于灰色理论和多元非线性回归分析,分别建立各因素的灰色预测模型GM(1, 1),再将灰色模型预测值作为自变量输入到多元非线性回归方程中,得到了改进灰色-多元非线性回归组合的供水量预测模型,其模型拟合度R2为0.913且与真实值的平均相对误差为6.9%左右,实现了灰色模型和回归模型优势互补,有效地预测该电厂供水量未来变化;而供水量预测是智慧水务建设的关键所在,是水务管理和智能调度的主要依据,也是实现供水管网漏损和仪表故障报警的重要途径。
The water affairs of a power plant has been taken as a research subject,its operation parameters associated with water history datas is analyzed and screened out,according to the previous water balance test results and validation with response surface analysis method,it is found that the generation load,coefficient of water evaporation loss,concentration ratio and rise of circulating water inlet and outlet temperature can make important influence on water supply demand.Based on the classical grey theory and multivariate nonlinear regression analysis,grey models GM(1,1)are firstly used to forecast four impact factors,its predictive values are used as independent variable into equation,then the improved combination prediction model of grey system and multivariate nonlinear regression of water supply is obtained.The regression model of fitting accuracy(R2)come up to 0.913,the mean relative error between the predicted value and the truth is about 6.9%.That model can achieve complementary advantages of grey model and regression model,and effectively forecast the future tendency of water supplies.Water supply prediction is the key part of the construction of smart water affair of coal-fired power plant,which is the major basis for water management and intelligent distribution,also is an important way to achieve the detection of pipeline leakage and warning of monitoring instrument failure.
作者
熊远南
XIONG Yuannan(Datang Central-China Electric Power Test Research Institute,Zhengzhou 450000,Henan,China)
出处
《化工进展》
EI
CAS
CSCD
北大核心
2020年第S02期393-400,共8页
Chemical Industry and Engineering Progress
关键词
灰色模型
多元非线性回归
供水量预测
管网漏损
智慧水务
grey model
multivariate nonlinear regression
water supply prediction
pipeline leakage
smart water affair
