Correlation Analysis and Monitoring Method of Carbon Emissions in the Steel Industry Based on Big Data
DOI:
https://doi.org/10.13052/spee1048-5236.4312Keywords:
Big data, carbon emissions, carbon emission monitoring, energy consumption, steel industryAbstract
Excessive carbon emissions will lead to catastrophic consequences such as global warming and rising oceans and will also have a serious negative impact on the human food supply and living environment. The steel industry is characterized by high pollution, and about 18% of China’s carbon emissions come from the steel industry. The ‘double carbon’ strategy has brought important tasks and severe challenges to China’s steel industry. With a view to evaluating the achievements of carbon emission control, carbon emission monitoring systems at home and abroad have been continuously established and improved. For the steel industry, accurate and efficient carbon monitoring technology has a guiding role in guiding energy conservation and carbon reduction. Traditional carbon emission accounting methods have some problems, such as long cycles and poor data quality, which restrict the improvement of the lean level of carbon emission monitoring management. Firstly, this paper investigates and analyzes the productive process and carbon emission process of the steel industry and constructs an entropy weight-grey correlation -TOPSIS analysis method for the correlation between carbon emissions and influencing factors. Based on the above content, a carbon emission monitoring method based on multiple influencing factors is put forward, and the high monitoring accuracy of the model is proved by taking the Tianjin steel industry as an example. The results show that information mining of relevant data can strikingly increase the accuracy of carbon emission monitoring in the steel industry.
Downloads
References
Tian Yong, Tian Zhongming, Wang Nianjun, et al. Analysis and Strategy Research of Carbon Emission Source Control in China Iron and Steel Industry[J]. Angang Technology, 2022(05):1–7.
Xing Yi, Cui Yongkang, Tian Jinglei, et al. Application status and prospect of low carbon technology in iron and steel industry[J]. Chinese Journal of Engineering, 2022, 44(04):801–811.
Tang Xiaoliang, He Chuan, Liu Qing. Overestimation of carbon emissions of gas-fired power plants by emission factor method – based on the observation results of a power plant in Gaoyou[J]. Electric Power Technology and Environmental Protection, 2020, 36(3):17–20.
Liang Tianqi, Xu Hong, Zheng Tianlin, et al. Measurement Uncertainty in Flue Gas Pollutants from Thermal Power Plant by Continuous Emission Monitoring System[J]. Acta Metrologica Sinica, 2021, 42(05):668–674.
Ren YiShuai, Apergis Nicholas, Ma Chaoqun, et al. FDI, economic growth, and carbon emissions of the Chinese steel industry: new evidence from a 3SLS model[J]. Environmental Science and Pollution Research International, 2021, 28(37):52547–52564.
Ya Chen, Xiaoli Fan, Qian Zhou. An Inverted-U Impact of Environmental Regulations on Carbon Emissions in China’s Iron and Steel Industry: Mechanisms of Synergy and Innovation Effects[J]. Sustainability, 2020, 12(3):1038.
Sun L, Jin H, Li Y. Research on Scheduling of Iron and Steel Scrap Steelmaking and Continuous Casting Process Aiming at Power Saving and Carbon Emissions Reducing[J]. IEEE Robotics & Automation Letters, 2018, 3:1–1.
Li Xinchuang, Li Bing, Huo Dongmei, et al. Thoughts on carbon emission standards for promoting low-carbon development of China steel industry [J]. China Metallurgy, 2021, 31(6):1–6.
Wang Xuying, Li Bing, Lu Chen, et al. Research on the peak path of carbon dioxide emission in China’s iron and steel industry [J]. Environmental Science Research, 2022, 35(02):339–346.
Guo Zhiqiang, Ling Guangxin. Production and operation of blast furnace[M]. Hebei Science & Technology Press, 2015.
Ma Xiuqin. Carbon emission verification technology and low-carbon technology in China’s steel and cement industry [M]. China Environmental Publishing House, 2015.
Zhang Changyou, Zhang Wenyu, Luo Weina, et al. Analysis of Influencing Factors of Carbon Emissions in China’s Logistics Industry: A GDIM-Based Indicator Decomposition[J]. Energies, 2021, 14.
Zhang Yuan, Yu Zhen, Zhang Juan. Research on carbon emission differences decomposition and spatial heterogeneity pattern of China’s eight economic regions.[J]. Environmental Science and Pollution Research International, 2022, 29(20).
Partha Protim Das, Shankar Chakraborty. A grey correlation-based TOPSIS approach for optimization of surface roughness and micro hardness of Nitinol during WEDM operation [J]. Materials Today: Proceedings, 2020, 28(Pt 2).
Cheng K, Fan T, Jin Y, et al. SecureBoost: A Lossless Federated Learning Framework[J]. Intelligent Systems, IEEE, 2021, PP(99):1–1.
Ana Belén Ramos-Guajardo, Maria Brigida Ferraro. A fuzzy clustering approach for fuzzy data based on a generalized distance[J]. Fuzzy Sets and Systems, 2020, 389.
Fan Guoxuan. An anomaly detection method based on trend adaptive elimination and improved LOF[D]. Beijing University of Chemical Technology, 2022.
Abdelghani Hamaz, Ouerdia Arezki, Farida Achemine. Impact of missing data on the prediction of random fields[J]. Journal of Applied Statistics, 2020, 47(1).
Sun Fangyuan, Kong Xiangyu, Wu Jianzhong, et al. DSM pricing method based on A3C and LSTM under cloud-edge environment[J]. Applied Energy, 2022(Jun.1):315.
Zhang Zhonglin, Zhang Yan. Time series prediction model of LSTM optimized by improved FA [J]. Computer Engineering and Application, 2022, 58(11):125–132.
Kong Xiangyu, Liu Chao, Chen Songsong, et al. Assessment Method for Multi-time-node Response Potential of Adjustable Resource Cluster Considering Dynamic Process [J]. Automation of Electric Power Systems, 2022, 46(19):55–64.
Jing Ye, Yaoguo Dang, Yingjie Yang. Forecasting the multifactorial interval grey number sequences using grey relational model and GM (1, N) model based on effective information transformation[J]. Soft Computing: A Fusion of Foundations, Methodologies and Applications, 2020, 24(8).
Xiangyu Kong, Xiaopeng Zhang, Xuanyong Zhang, et al. Adaptive Dynamic State Estimation of Distribution Network Based on Interacting Multiple Model [J]. IEEE Transactions on Sustainable Energy, 2022, 13(2):643–652.
https://aeeeuropeenergy.com/
