摘要
制浆造纸产业是典型的高能耗、高污染产业。针对当前制浆造纸企业能耗管理不集中、能源利用率低等问题,基于遗传数学算法对传统的BP神经网络能耗预测模型进行优化,以EMS架构下的制浆造纸企业为例对优化后的能耗预测数学模型能耗预测速度、精度等进行测试,认为该数学模型能够很好地保证制浆造纸企业获得理想的能耗管理和节能效果,值得广大制浆造纸企业借鉴。
Pulp and paper industry is a typical industry with high energy consumption and high pollution. Aiming at the problems of uncentralized energy management and low energy utilization in pulp and paper enterprises, this paper optimized the traditional BP neural network energy consumption prediction model based on genetic mathematical algorithm. Taking the pulp and paper enterprises under EMS framework as an example, the energy consumption prediction speed and accuracy of the optimized energy consumption prediction mathematical model were tested. It is concluded that the mathematical model can guarantee the pulp and paper enterprises to obtain the ideal energy consumption management and energy-saving effect, which is worthy of reference for many pulp and paper enterprises.
作者
段瑞
DUAN Rui(Shaanxi Polytechnic Institute,Xianyang 712000,China)
出处
《造纸科学与技术》
2022年第5期29-32,共4页
Paper Science & Technology
关键词
EMS架构
制浆造纸企业
能耗预测
数学模型
EMS architecture
pulp and paper enterprises
energy consumption prediction
mathematical model
