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Mining Technology and Mine Management

Research on Energy Consumption Prediction of Steel Industry Production Based on Improved Grey Models

  • Gao DENG ,
  • Qi LI
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  • Hunan Iron and Steel Group Co. ,Ltd. ,Changsha 410004,Hunan,China

Received date: 2023-08-21

  Revised date: 2024-04-19

  Online published: 2024-07-05

Abstract

Under the background of double carbon target,the research on energy consumption prediction of steel industry plays an important role in reducing production efficiency and improving efficiency of steel industry.In order to scientifically predict the energy consumption of steel industry production,based on the data of iron and steel energy consumption from 2010 to 2022,three improved grey prediction models of DNGM(1,1),IDGM (1,1) and DDGM(1,1) were established to predict the comprehensive energy consumption per ton of steel and the comparable energy consumption per ton of steel.The data prediction and error comparison analysis were carried out to select the optimal model and obtain the prediction results from 2023 to 2025.The results show that the grey prediction model is feasible and adaptable in the prediction of steel energy consumption.The DNGM(1,1) model has the best overall simulation performance in the prediction of energy consumption in steel industry production.The comprehensive energy consumption per ton of steel and the comparable energy consumption per ton of steel will continue to decline from 2023 to 2025.

Cite this article

Gao DENG , Qi LI . Research on Energy Consumption Prediction of Steel Industry Production Based on Improved Grey Models[J]. Gold Science and Technology, 2024 , 32(3) : 548 -558 . DOI: 10.11872/j.issn.1005-2518.2024.03.118

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我国管辖海域1∶100万区域地质调查重要成果发布

5月28日,全面反映我国管辖海域地质资源环境全貌的“1∶100万海洋区域地质调查系统性成果”在青岛正式发布,填补了我国海洋区域地质系统国情调查的空白,开启了海洋区域地质调查的新征程,在我国海洋地质调查进程中具有里程碑式意义。

当天,第一届海洋区域地质调查大会在青岛召开。本次会议由自然资源部中国地质调查局青岛海洋地质研究所主办,主题是“凝心聚力,守正创新,奋力推动海洋区域地质调查高质量发展”,来自自然资源部、中国地质调查局和有关高校、科研院所等60多家单位的院士、专家学者及青年科技人员参加会议。

据介绍,自1999年以来,在自然资源部(原国土资源部)的直接领导下,在财政部、外交部等相关部门的大力支持下,中国地质调查局精心组织60多家单位、千余名海洋地质工作者,历时20余载,开创性地实现了我国管辖海域20个国际标准图幅1∶100万海洋区域地质调查的全覆盖,取得了基于海量实测数据的“图、书、库”系统性成果,实现了数据集聚、原创发现、理论认知、技术进步等多方面的创新,为支撑能源资源安全保障,服务生态文明建设与自然资源管理等提供重要的基础资料,系统化、规律化、理论化的重大成果有效服务于海洋强国战略。

1∶100万海洋区域地质调查主要取得5项代表性成果:(1)有效提升了海洋资源环境国情认知水平。(2)取得了系列原创性科学认识。(3)积极开展成果应用服务。(4)实现了调查装备技术的整体跃升。(5)建成了我国海洋区域地质大数据中心。

大众日报)

http://www.goldsci.ac.cn/article/2024/1005-2518/1005-2518-2024-32-3-548.shtml

Chao Yinkang Gong Lixiong Huang Xiao,et al,2023.Power consumption prediction and application of GM(1,1)-MEA-BP combined model[J].Journal of Chongqing University of Technology(Natural Science)37(7):306-314.

Cheng Guang Li Lingyun Ding Yi,et al,2014.Influencing factors of energy of consumption for iron steel enterprise systems[J].Iron and Steel49(4):86-89.

Deng Gao Yang Shan2017.Analysis of mineral resources utilization based on grey cluster and grey prediction combined model[J].Gold Science and Technology25(5):85-92.

Deng Junlong1984.The diferential grey model(GM) and its implement in long period forecasting of grain[J].Discovery of Nature,(3):37-43.

Duan H M Wang D Pang X Y,et al,2020.A novel forecasting approach based on multi-kernel nonlinear multivariable grey model:A case report[J].Journal of Cleaner Production,260:120929.

Fan Zhongzhou Zhao Yi Zhou Ning,et al,2020.Integrated model for forecasting waterway traffic accidents based on the grey-BP neural network[J].Journal of Safety and Environment20(8):857-861.

Feng Zhengyuan1992.Direct grey model[J].Acta Mathematicae Applicatae Sinica15(3):345-354.

Guo S D Jing Y Q Li B J2022.Matrix form of interval multivariable gray model and its application[J].Grey Systems:Theory and Application12(2):318-338.

Guo Xiaojun Liu Sifeng Fang Zhigeng2014.Discrete DDGM prediction model of development belt based on the standard interval grey number[J].Mathematics in Practice and Theory44(6):19-25.

He Chengxiang Zeng Bo Yang Lebin2021.Prediction comparative analysis of PM2.5 in Chongqing based on parameter combination optimization of grey models[J].Journal of Systems Science and Mathematical Sciences41(10):2855-2867.

He Kun Wang Li2021.Development and status of production energy consumption of China’s iron and steel industry[J].China Metallurgy31(9):26-35.

Intharathirat R Salam P A Kumar S,et al,2015.Forecasting of municipal solid waste quantity in a developing country using multivariate grey models[J].Waste Management,39:3-14.

Lin B Q Wang X L2015.Carbon emissions from energy intensive industry in China:Evidence from the iron & steel industry[J].Renewable and Sustainable Energy Reviews,47:746-754.

Liu C Lao T F Wu W Z,et al,2022.An optimized nonlinear grey Bernoulli prediction model and its application in natural gas production[J].Expert Systems with Applications,194:116448.

Liu Qiang Yan Xiu Lu Yu,et al,2022.A grey relation projection-random forest prediction model of energy consumption for electric buses considering driving style[J].Journal of Transport Information and Safety40(5):129-138.

Ma Hongyan Cui Jie Wang Yu2019.Parameter properties of DDGM(1,1) model under scalar multiplication transformation of modeling sequence[J].Statistics and Decision35(7):60-62.

Tong Mingyu Zhou Xiaohua Zeng Bo2015.Expand of NGM(1,1) model based on the direct estimation method[J].Control and Decision30(10):1841-1846.

Wang Jiangrong Liu Shuo Jin Cuncheng2020.Application of grey G(1,1) model based on variable weight buffer operator in metro energy consumption prediction[J].Mathematics in Practice and Theory50(7):90-96.

Wang Q Li S Y Li R R,et al,2018.Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model[J].Energy,160:378-387.

Wang Weixing2017.The status of energy consumption of China and analysis of energy saving potential in iron and steel industry[J].China Steel Focus,(8):50-58.

Wang Xinpu Zhou Xiangling Xing Jie,et al,2016.A prediction of PV output power based on the combination of improved grey propagation neural network[J].Power System Protection and Control44(18):81-87.

Wang Z X Hipel K W Wang Q,et al,2011.An optimized NGBM(1,1) model for forecasting the qualified discharge rate of industrial wastewater in China[J].Applied Mathematical Modelling35(12):5524-5532.

World Steel Association,2023.World Steel in Figures 2023[OB/OL].

Xie N M2022.A summary of grey forecasting models[J].Grey Systems:Theory and Application12(4):703-722.

Xie Naiming Liu Sifeng2005.Discrete GM(1,1) and mechanism of grey forecasting model[J].Systems Engineering-Theory & Practice,(1):93-99.

Xiong P P Zou X Yang Y J2021.The nonlinear time lag multivariable grey prediction model based on interval grey numbers and its application[J].Natural Hazards107(3):2517-2531.

Xu Hailun Pan Guoyou Shao Yuanjing,et al,2017.Analysis of energy consumption evaluation indication for iron and steel production[J].Energy for Metallurgical Industry36(2):3-7,56.

Xu Lisong Zhang Qi2021.Analysis on energy consumption and CO2 emission trend of China’s iron and steel industry in key regions[J].China Metallurgy31(9):36-45.

Yang Hualong Liu Jinxia Zheng Bin2011.Improvement and application of grey prediction GM(1,1) model[J].Mathematics in Practice and Theory41(23):39-46.

Zeng B Duan H M Zhou Y F2019.A new multivariable grey prediction model with structure compatibility[J]Applied Mathematical Modelling,75:385-397.

Zeng B Li H Ma X,et al,2020.A novel multi-variable grey forecasting model and its application in forecasting the grain production in China[J].Computers & Industrial Engineering,150:106915.

Zeng Bo Liu Sifeng2010.Analysis of indirect DGM(1,1) model of non-homogeneous exponential incremental sequences[J].Journal of Statistics and Information25(8):30-33.

Zeng Bo Liu Sifeng2012.Prediction model of stochastic oscillation sequence based on amplitude compression[J].Systems Engineering-Theory & Practice32(11):2493-2497.

Zeng Bo Liu Sifeng Qu Xuexin2017.Research on a grey common prediction modeling with strong compatibility and its properties[J].Chinese Journal of Management Science25(5):150-156.

Zhang Chao2023.Game Theory-based Research on Energy Conservation and Carbon Reduction Management Decisions in the Steel Industry[D].Beijing:University of Science and Technology.

Zhang F Y Zhou Y D Sun W Q,et al,2018.CO2 capture from reheating furnace based on the sensible heat of continuous casting slabs[J].International Journal of Energy Research42(6):2273-2283.

Zhang Q Wang Y J Zhang W,et al,2019.Energy and resource conservation and air pollution abatement in China’s iron and steel industry[J].Resources Conservation and Recycling,147:67-84.

Zhou Zhiyong Xiao Wei Chen Jianhong,et al,2018.Prediction model of mine ecological environment based on PCA and GM(1,1) [J].Gold Science and Technology26(3):372-378.

钞寅康,龚立雄,黄霄,等,2023.GM(1,1)-MEA-BP组合模型电能消耗预测及应用[J].重庆理工大学学报(自然科学)37(7):306-314.

陈光,李玲云,丁毅,等,2014.钢铁企业系统能耗影响因素分析[J].钢铁49(4):86-89.

邓高,杨珊,2017.基于灰色聚类与灰色预测组合模型的矿产资源利用情况分析[J].黄金科学技术25(5):85-92.

邓聚龙,1984.灰色动态模型(GM)及在粮食长期预测中的应用[J].大自然探索,(3):37-43.

范中洲,赵羿,周宁,等,2020.基于灰色BP神经网络组合模型的水上交通事故数预测[J].安全与环境学报20(3):857-861.

冯正元,1992.直接灰色模型[J].应用数学学报15(3):345-354.

郭晓君,刘思峰,方志耕,2014.基于标准区间灰数的发展带离散DDGM预测模型[J].数学的实践与认识44(6):19-25.

何承香,曾波,杨乐彬,2021.基于灰色参数组合优化模型的重庆PM2.5浓度预测与对比分析[J].系统科学与数学41(10):2855-2867.

何坤,王立,2021.中国钢铁工业生产能耗的发展与现状[J].中国冶金31(9):26-35.

刘强,严修,鲁誉,等,2022.考虑驾驶风格的电动公交车能耗灰色关联投影-随机森林预测模型[J].交通信息与安全40(5):129-138.

马红燕,崔杰,王雨,2019.建模序列数乘变换下的DDGM(1,1)模型参数特性[J].统计与决策35(7):60-62.

童明余,周孝华,曾波,2015.基于直接估计法的NGM(1,1)模型拓展[J].控制与决策30(10):1841-1846.

王江荣,刘硕,靳存程,2020.基于变权缓冲算子的灰色G(1,1)模型在地铁能耗预测中的应用[J].数学的实践与认识50(7):90-96.

王维兴,2017.我国钢铁工业能耗现状与节能潜力分析[J].冶金管理,(8):50-58.

王新普,周想凌,邢杰,等,2016.一种基于改进灰色BP神经网络组合的光伏出力预测方法[J].电力系统保护与控制44(18):81-87.

谢乃明,刘思峰,2005.离散GM(1,1)模型与灰色预测模型建模机理[J].系统工程理论与实践,(1):93-99.

徐海伦,潘国友,邵远敬,等,2017.钢铁生产能耗评估指标分析[J].冶金能源36(2):3-7,56.

许立松,张琦,2021.中国重点区域钢铁产业能耗和CO2排放趋势分析[J].中国冶金31(9):36-45.

杨华龙,刘金霞,郑斌,2011.灰色预测GM(1,1)模型的改进及应用[J].数学的实践与认识41(23):39-46.

曾波,刘思峰,2010.近似非齐次指数增长序列的间接DGM(1,1)模型分析[J].统计与信息论坛25(8):30-33.

曾波,刘思峰,2012.基于振幅压缩的随机振荡序列预测模型[J].系统工程理论与实践32(11):2493-2497.

曾波,刘思峰,曲学鑫,2017.一种强兼容性的灰色通用预测模型及其性质研究[J].中国管理科学25(5):150-156.

张朝,2023.基于博弈论的钢铁行业节能减碳管理决策研究[D].北京:北京科技大学.

周智勇,肖玮,陈建宏,等,2018.基于PCA和GM(1,1)的矿山生态环境预测模型[J].黄金科学技术26(3):372-378.

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