Dynamic optimization algorithm of cement firing system based on differential evolution

Xiaochen HAO; Yakun JI; Lizhao ZHENG; Xin SHI; Yantao ZHAO

doi:10.11959/j.issn.2096-6652.202021

您当前的位置：

首页 >

文章列表页 >

Dynamic optimization algorithm of cement firing system based on differential evolution

Regular Papers | 更新时间：2024-06-05

- Dynamic optimization algorithm of cement firing system based on differential evolution
- Chinese Journal of Intelligent Science and Technology Vol. 2, Issue 2, Pages: 186-193(2020)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202021
  CLC： TB973
- Published Online：2020-06，
  
  Published：20 June 2020
- 稿件说明：
移动端阅览
Xiaochen HAO, Yakun JI, Lizhao ZHENG, et al. Dynamic optimization algorithm of cement firing system based on differential evolution[J]. Chinese journal of intelligent science and technology, 2020, 2(2): 186-193.
DOI：

Xiaochen HAO, Yakun JI, Lizhao ZHENG, et al. Dynamic optimization algorithm of cement firing system based on differential evolution[J]. Chinese journal of intelligent science and technology, 2020, 2(2): 186-193. DOI： 10.11959/j.issn.2096-6652.202021.

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。

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.

关键词

Keywords

references

周国治 , 彭宝利 . 水泥生产工艺概论 [M ] . 武汉 : 武汉理工大学出版社 , 2005 .

ZHOU G Z , PENG B L . Introduction to cement production technology [M ] . Wuhan : Wuhan University of Technology PressPress , 2005 .

常建峰 . 水泥回转窑烧成过程控制系统研究与应用 [D ] . 秦皇岛:燕山大学 , 2016 .

CHANG J F . The research and application of kiln calcination process control system [D ] . Qinhuangdao:Yanshan University , 2016 .

周剑平 . 水泥生产工艺 [M ] . 西安 : 西北大学出版社 , 2008 .

ZHOU J P . Cement production technology [M ] . Xi’an : Northwestern University PressPress , 2018 .

SONG Y , CHEN Z Q , YUAN Z Z . Neural network nonlinear predictive control based ontent-map chaos optimization [J ] . Chinese Journal of Chemical Engineering , 2013 , 15 ( 4 ): 539 - 544 .

朱志斌 , 王岩 , 陈兴林 . 分段状态约束非线性预测控制数值算法研究 [J ] . 系统工程与电子技术 , 2014 , 31 ( 6 ): 1436 - 1440 .

ZHU Z B , WANG Y , CHEN X L . Research on numerical algorithms for nonlinear predictive control problems based on segmented state constraints [J ] . Systems Engineering and Electronics , 2014 , 31 ( 6 ): 1436 - 1440 .

MUJUNDAR K S , GANESH K V , KULKARNI S B , et al . Rotary cement kiln simulator (RoCKS):integrated modeling of pre-heater,calciner,kiln and clinker cooler [J ] . Chemical Engineering Science , 2007 , 62 ( 9 ): 2590 - 2607 .

BIANCO V , MANCA D , NARDINI S . Electricity consumption forecasting in Italy using linear regression models [J ] . Energy , 2009 , 34 ( 9 ): 1413 - 1421 .

CUI Z , SHAO W , CHEN Z Y , et al . Mathematical model and numerical solutions for the coupled gas-solid heat transfer process in moving packed beds [J ] . Applied Energy , 2017 , 206 ( 15 ): 1297 - 1308 .

牛培峰 , 马云鹏 , 张欣欣 , 等 . 基于人工智能技术的火电厂燃煤锅炉智能燃烧优化研究及应用 [J ] . 智能科学与技术学报 , 2019 , 1 ( 2 ): 163 - 170 .

NIU P F , MA Y P , ZHANG X X , et al . Research and application on combustion optimization of coal-fired boiler in thermal power plant based on artificial intelligence technology [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 2 ): 163 - 170 .

陈妮亚 , 阮佳阳 , 黄金苗 , 等 . 结合深度学习与生物特征识别在冷链拣选中的算法研究 [J ] . 智能科学与技术学报 , 2019 , 1 ( 1 ): 88 - 95 .

CHEN N Y , RUAN J Y , HUANG J M , et al . Algorithm design for food-picking combining deep learning and biometrics recognition [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 1 ): 88 - 95 .

KAYA S , KÜÇÜKADA K , MANCUHAN E . Model-based optimization of heat recovery in the cooling zone of a tunnel kiln [J ] . Applied Thermal Engineering , 2008 , 28 ( 5-6 ): 633 - 641 .

高永岩 . 工程型预测控制软件的开发与应用 [D ] . 南宁:广西大学 , 2005 .

GAO Y Y . Development and application of predictive control software for engineering [D ] . Nanning:Guangxi University , 2005 .

王昭旭 . 基于深度信念网络的水泥生产能耗时间序列预测模型 [C ] // 第 37 届中国控制会议论文集 . 北京:中国自动化学会控制理论专业委员会 , 2018 : 1161 - 1166 .

WANG Z X , . Prediction model of time series of energy consumption in cement production based on deep belief network [C ] // The 37th China Control Conference . Beijing:Technical Committee on Control Theory,Chinese Association of Automation , 2018 : 1161 - 1166 .

Views

269

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Diagnostic of breast tumors based on improved EfficientNet

Application of Fit CutMix data augmentation algorithm based on saliency information in medical images

Research on data-driven modeling for photovoltaic characteristics based on hybrid neural network

Related Author

FANG Zhenqi

LI Xue

MO Hong

Xinhuan LUO

Yixuan WANG

Wei LI

Xi CHEN

Guobin ZHANG

Related Institution

School of Electrical & Information Engineering, Changsha University of Science & Technology

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology

Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology

Postal code：100079
Tel：（010）53879206 Email：cjist@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：35685 Visits Today：160

⁰