智能科学与技术学报 ›› 2020, Vol. 2 ›› Issue (2): 186-193.doi: 10.11959/j.issn.2096-6652.202021

• 学术论文 • 上一篇    下一篇

基于差分进化的水泥烧成系统动态优化算法

郝晓辰(),冀亚坤,郑立召,史鑫,赵彦涛   

  1. 燕山大学电气工程学院,河北 秦皇岛 066004
  • 修回日期:2020-04-14 出版日期:2020-06-20 发布日期:2020-07-14
  • 作者简介:郝晓辰(1980- ),男,博士,燕山大学电气工程学院教授,博士生导师,主要研究方向为智能控制技术与应用、物联网技术与应用|冀亚坤(1996- ),男,燕山大学电气工程学院硕士生,主要研究方向为水泥烧成过程多目标优化模型与优化决策算法|郑立召(1993- ),男,燕山大学电气工程学院硕士生,主要研究方向为水泥生产过程运行指标决策方法|史鑫(1984- ),男,燕山大学电气工程学院博士生,主要研究方向为水泥生产过程运行指标决策方法|赵彦涛(1979- ),男,博士,燕山大学电气工程学院副教授,主要研究方向为智能控制理论、信号处理等
  • 基金资助:
    河北省重点研发计划基金资助项目(19211602D);河北省自然科学基金资助项目(F2019203385);第二批河北省青年拔尖人才支持计划(5040050)

Dynamic optimization algorithm of cement firing system based on differential evolution

Xiaochen HAO(),Yakun JI,Lizhao ZHENG,Xin SHI,Yantao ZHAO   

  1. School of Electrical Engineering,Yanshan University,Qinhuangdao 066004,China
  • Revised:2020-04-14 Online:2020-06-20 Published:2020-07-14
  • Supported by:
    Key Research and Development Plan of Hebei Province(19211602D);Hebei Natural Science Foundation(F2019203385);The Second Batch of Hebei Province Youth Top Talent Support Program(5040050)

摘要:

针对水泥烧成过程的资源浪费以及难以建立有效数学机理模型的问题,提出一种基于水泥工业烧成系统动态能耗优化方法。该方法利用卷积神经网络构建了烧成系统电耗与煤耗的目标函数,利用差分进化算法对运行指标进行反向求解,得到符合当前工况的较优的运行指标。由于实际生产工况会随着时间变化,所以将未来时刻的运行指标与电耗、煤耗保存下来,再次输入神经网络中进行训练,并通过当前时刻的实际运行指标值确定运行指标的约束范围,使优化值可以满足实际运行指标的调整要求。该方法实现了水泥烧成过程动态能耗的目标优化,有效地降低了水泥烧成过程的能源消耗。

关键词: 卷积神经网络, 差分进化算法, 能耗优化, 能耗预测

Abstract:

Aiming at the problem of resource waste in the process of cement firing and the difficulty of establishing an effective mathematical mechanism model,a dynamic energy optimization method based on the cement industry firing system was proposed.The method used the convolutional neural network to construct the objective function of power consumption and coal consumption of the firing system.The differential evolution algorithm was used to solve the control parameters in reverse,and the better operating index was obtained according to the current working conditions.Since the actual production conditions will change with time,the operating indicators and power consumption and coal consumption in the future will be saved,and then input into the neural network for training,and the constraint range will be determined by the actual running index value at the current time.The optimization value can meet the actual operation index adjustment requirements.Furthermore,the goal optimization of the dynamic energy consumption state of the cement firing process was realized.It effectively reduces the energy consumption of cement firing process.

Key words: convolutional neural network, differential evolution algorithm, energy optimization, energy consumption forecast

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