基于知识库的制造业能耗优化平台技术研究

doi:10.11959/j.issn.1000-0801.2022142

电信科学 ›› 2022, Vol. 38 ›› Issue (8): 178-185.doi: 10.11959/j.issn.1000-0801.2022142

• 工程与应用 • 上一篇

基于知识库的制造业能耗优化平台技术研究

杨伟伟¹, 王思宁², 郑贵德³, 宋亚琼²

¹ 南大通用数据技术股份有限公司，天津 200384
² 北京中电普华信息技术有限公司，北京 100192
³ 中国电机工程学会，北京 100761

修回日期:2022-04-28 出版日期:2022-08-20 发布日期:2022-08-01
作者简介:杨伟伟（1979- ），男，南大通用数据技术股份有限公司技术总监、高级工程师，主要从事大数据的研究、开发与应用等工作
王思宁（1978- ），女，北京中电普华信息技术有限公司副总工程师、正高级工程师、能源物联及区块链事业部总经理，主要从事电力信息化项目管理及标准体系构建等工作
郑贵德（1964- ），男，博士，中国电机工程学会电力信息化专委会委员，主要从事人工智能、大数据分析等工作
宋亚琼（1990- ），女，现就职于北京中电普华信息技术有限公司，主要研究方向为电力信息化项目管理及标准制修订管理

Research on manufacturing energy consumption optimization platform technology based on knowledge-base

Weiwei YANG¹, Sining WANG², Guide ZHENG³, Yaqiong SONG²

¹ General Data Technology Co., Ltd., Tianjin 200384,China
² Beijing China-Power Information Technology Co., Ltd., Beijing 100192,China
³ Chinese Society for Electrical Engineering, Beijing 100761,China

Revised:2022-04-28 Online:2022-08-20 Published:2022-08-01

摘要/Abstract

摘要：

摘要：我国制造业在经济比重、规模巨大，企业通过数字化技术逐步实现制造企业能耗优化具有现实意义。以汽车制造为例，提出了基于知识库实现能耗优化的方案，包括制造业能耗的特点分析、能耗优化的关键技术及应用实践呈现。从企业生产设备入手，通过企业制造执行系统信息及设备运行状态寻找影响能耗的规律、构建知识库，并通过神经网络、关联分析等机器学习算法，实现能耗优化。

关键词: 能耗优化, 知识库, 节能智慧平台

Abstract:

China’s manufacturing industry accounts for the largest proportion of the economy and involves a huge scale.It is of practical significance for enterprises to gradually realize energy consumption optimization through digital technology.Taking automobile manufacturing as an example, a scheme of energy consumption optimization based on knowledge-base was proposed, including the analysis of the characteristics of energy consumption in manufacturing industry, the key technologies of energy consumption optimization and the presentation of application practice.Starting with the production equipment of the enterprise, the law affecting energy consumption was found and a knowledge-base was build through the information of manufacturing execution system and equipment operation status of the enterprise, and the optimization of energy consumption was realized through machine learning algorithms such as neural network and correlation analysis.

Key words: energy consumption optimization, knowledge-base, energy saving intelligent platform

中图分类号:

TP393.08

杨伟伟, 王思宁, 郑贵德, 宋亚琼. 基于知识库的制造业能耗优化平台技术研究[J]. 电信科学, 2022, 38(8): 178-185.

Weiwei YANG, Sining WANG, Guide ZHENG, Yaqiong SONG. Research on manufacturing energy consumption optimization platform technology based on knowledge-base[J]. Telecommunications Science, 2022, 38(8): 178-185.

图/表 7

图1

图2

图3

表1

图4

图5

图6

参考文献 19

[1]	胡鞍钢 . 中国实现2030年前碳达峰目标及主要途径[J]. 北京工业大学学报(社会科学版), 2021,21(3): 1-15.
	HU A G . China's goal of achieving carbon peak by 2030 and its main approaches[J]. Journal of Beijing University of Technology (Social Sciences Edition), 2021,21(3): 1-15.
[2]	尹瑞雪 . 基于碳排放评估的低碳制造工艺规划决策模型及应用研究[D]. 重庆:重庆大学, 2014.
	YIN R X . The study and application of process planning decision model for low carbon manufacturing based on carbon emissions evaluation[D]. Chongqing:Chongqing University, 2014.
[3]	李聪波, 沈欢, 李玲玲 ,等. 面向能耗的多工艺路线柔性作业车间分批优化调度模型[J]. 机械工程学报, 2017,53(5): 12-23.
	LI C B , SHEN H , LI L L ,et al. A batch splitting flexible job shop scheduling model for energy saving under alternative process plans[J]. Journal of Mechanical Engineering, 2017,53(5): 12-23.
[4]	赵振家, 张鹏, 赵明楠 ,等. 汽车制造过程的能耗及碳排放分析[J]. 中国人口·资源与环境, 2017,27(S1): 186-190.
	ZHAO Z J , ZHANG P , ZHAO M N ,et al. Analysis of energy consumption and carbon emission for automobile manufacturing process[J]. China Population,Resources and Environment, 2017,27(S1): 186-190.
[5]	何彦, 林申龙, 王禹林 ,等. 数控机床多能量源的动态能耗建模与仿真方法[J]. 机械工程学报, 2015,51(11): 123-132.
	HE Y , LIN S L , WANG Y L ,et al. Method for modeling the dynamic energy characteristics of multi-energy sources in CNC machine tool[J]. Journal of Mechanical Engineering, 2015,51(11): 123-132.
[6]	孔露露 . 数控机床能耗建模与参数影响分析[D]. 大连:大连理工大学, 2014.
	KONG L L . Research on CNC machine tool energy modeling and analysis of its influential parameters[D]. Dalian:Dalian University of Technology, 2014.
[7]	高宗江 . 典型工业涂装行业 VOCs 排放特征研究[D]. 广州:华南理工大学, 2015.
	GAO Z J . Source characteristics of VOC emissions from typical industrial painting sources[D]. Guangzhou:South China University of Technology, 2015.
[8]	邹冰倩 . 汽车涂装车间能耗分析与节能技术研究及应用[D]. 重庆:重庆理工大学, 2016.
	ZOU B Q . Energy consumption analysis of car coating workshop and the research and application of energy-saving technology[D]. Chongqing:Chongqing University of Technology, 2016.
[9]	罗亮, 吴文峻, 张飞 . 面向云计算数据中心的能耗建模方法[J]. 软件学报, 2014,25(7): 1371-1387.
	LUO L , WU W J , ZHANG F . Energy modeling based on cloud data center[J]. Journal of Software, 2014,25(7): 1371-1387.
[10]	张青 . 中央空调系统节能运行控制方法研究[D]. 南京:东南大学, 2016.
	ZHANG Q . Study on energy saving control strategies of central air conditioning system[D]. Nanjing:Southeast University, 2016.
[11]	李忠, 安建琴, 刘海军 ,等. 关联挖掘算法及发展趋势[J]. 智能计算机与应用, 2017,7(5): 22-25.
	LI Z , AN J Q , LIU H J ,et al. Association mining algorithm and its development trend[J]. Intelligent Computer and Applications, 2017,7(5): 22-25.
[12]	张蕾, 章毅 . 大数据分析的无限深度神经网络方法[J]. 计算机研究与发展, 2016,53(1): 68-79.
	ZHANG L , ZHANG Y . Big data analysis by infinite deep neural networks[J]. Journal of Computer Research and Development, 2016,53(1): 68-79.
[13]	李巍巍 . 数据中台技术在业务系统中的应用研究[J]. 现代信息科技, 2019,3(21): 108-110.
	LI W W . Research on the application of DPaaS technology in business system[J]. Modern Information Technology, 2019,3(21): 108-110.
[14]	数据中台技术相关进展及发展趋势[EB]. 2019.
	Data middle office technology related progress and development trends[EB]. 2019.
[15]	基于遗传算法的燃气锅炉供热系统优化策略研究[EB]. 2016.
	Research on optimization strategy of gas boiler heating system based on genetic algorithm[EB]. 2016.
[16]	李键 . 燃气锅炉燃烧控制器优化算法研究与设计[D]. 天津:天津理工大学, 2015.
	LI J . Gas boiler burning controller optimization algorithm research and design[D]. Tianjin:Tianjin University of Technology, 2015.
[17]	李昕, 钱旭, 王自强 . 一种高效的高维异常数据挖掘算法[J]. 计算机工程, 2010,36(21): 34-36.
	LI X , QIAN X , WANG Z Q . Efficient data mining algorithm for high-dimensional outlier data[J]. Computer Engineering, 2010,36(21): 34-36.
[18]	张荣昌 . 基于数据挖掘的用电数据异常的分析与研究[D]. 北京:北京交通大学, 2017.
	ZHANG R C . Analysis of abnormal electro-data based on data mining[D]. Beijing:Beijing Jiaotong University, 2017.
[19]	沈花玉, 王兆霞, 高成耀 ,等. BP 神经网络隐含层单元数的确定[J]. 天津理工大学学报, 2008,24(5): 13-15.
	SHEN H Y , WANG Z X , GAO C Y ,et al. Determining the number of BP neural network hidden layer units[J]. Journal of Tianjin University of Technology, 2008,24(5): 13-15.

指标名称	目标值	实际值	偏差值	对燃气影响
锅炉排烟温度	119.5102℃	140.281 8℃	20.771 6℃	1.147 8℃
出口含氧量	4.459 8%	1.992 5%	-2.467 4%	-2.402 3%
主汽温度	268℃	265.431 6℃	-2.568 4℃	0.294 8℃
主汽压力/MPa	16.67	16.319 1	-0.350 9	0.479 3
水槽温度/kPa	15	11.826 1	-3.173 9	-6.360 9
蒸汽量/（kg·h^-1）	1 200	1010	190	-2.402 3

基于知识库的制造业能耗优化平台技术研究

Research on manufacturing energy consumption optimization platform technology based on knowledge-base

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 19

相关文章 4

Metrics

推荐阅读 0

[1]	刘飞扬, 李坤, 宋飞, 周华春. DDoS攻击恶意行为知识库构建[J]. 电信科学, 2021, 37(11): 17-32.
[2]	邢宇恒. 基于知识库的系统安全评估方法[J]. 电信科学, 2016, 32(4): 192-196.
[3]	蔡鑫,阮宜龙,石屹嵘. 基于图数据库的IPTV内容知识库设计和实现[J]. 电信科学, 2016, 32(12): 32-36.
[4]	孟建忠,杜少全. 装维知识库的实现及应用研究[J]. 电信科学, 2014, 30(Z1): 200-205.