欧盟碳边境调节机制(CBAM)作为补充和加强碳排放交易体系,能够达到防止碳泄漏,确保欧盟气候政策的有效性,有助于全球脱碳以及欧盟2050年实现气候中和。在CBAM所管辖范围内,钢铁行业作为占比极高的行业来看,对未来各行业如何有效降低排...欧盟碳边境调节机制(CBAM)作为补充和加强碳排放交易体系,能够达到防止碳泄漏,确保欧盟气候政策的有效性,有助于全球脱碳以及欧盟2050年实现气候中和。在CBAM所管辖范围内,钢铁行业作为占比极高的行业来看,对未来各行业如何有效降低排放量具有重要意义。本文通过对钢铁行业的碳核算方法和相关已经出现的减排技术进行介绍,以及对未来钢铁行业的四个发展方向(碳捕集利用与封存规模化、氢气直接还原炼钢试点、电炉 + 废钢的循环经济、关注过渡性技术)进行分析总结,提出未来所要改进的趋势,为后续我国钢铁产业在生产制造以及出口方面提供理论依据。The EU Carbon Border Adjustment Mechanism (CBAM), as a complement and enhancement of the ETS, can prevent carbon leakage, ensure the effectiveness of the EU’s climate policy, contribute to global decarbonization and the EU’s climate neutrality by 2050. Within the jurisdiction of the CBAM, the steel industry, as an industry with a very high proportion, is of great significance to how to effectively reduce emissions in various industries in the future. This paper introduces the carbon accounting methods of the steel industry and related emission reduction technologies that have emerged, and analyzes and summarizes the four development directions of the steel industry in the future (large-scale carbon capture, utilization and storage, hydrogen direct reduction steelmaking pilot, circular economy of electric furnace + scrap steel, and focus on transitional technologies), and puts forward the trend to be improved in the future, so as to provide a theoretical basis for the subsequent production and export of China’s steel industry.展开更多
Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenari...Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenarios,including fluctuating noise levels and unpredictable environmental elements,these techniques do not fully resolve these challenges.We introduce an innovative strategy that builds upon Retinex theory and integrates a novel deep network architecture,merging the Convolutional Block Attention Module(CBAM)with the Transformer.Our model is capable of detecting more prominent features across both channel and spatial domains.We have conducted extensive experiments across several datasets,namely LOLv1,LOLv2-real,and LOLv2-sync.The results show that our approach surpasses other methods when evaluated against critical metrics such as Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM).Moreover,we have visually assessed images enhanced by various techniques and utilized visual metrics like LPIPS for comparison,and the experimental data clearly demonstrate that our approach excels visually over other methods as well.展开更多
文摘欧盟碳边境调节机制(CBAM)作为补充和加强碳排放交易体系,能够达到防止碳泄漏,确保欧盟气候政策的有效性,有助于全球脱碳以及欧盟2050年实现气候中和。在CBAM所管辖范围内,钢铁行业作为占比极高的行业来看,对未来各行业如何有效降低排放量具有重要意义。本文通过对钢铁行业的碳核算方法和相关已经出现的减排技术进行介绍,以及对未来钢铁行业的四个发展方向(碳捕集利用与封存规模化、氢气直接还原炼钢试点、电炉 + 废钢的循环经济、关注过渡性技术)进行分析总结,提出未来所要改进的趋势,为后续我国钢铁产业在生产制造以及出口方面提供理论依据。The EU Carbon Border Adjustment Mechanism (CBAM), as a complement and enhancement of the ETS, can prevent carbon leakage, ensure the effectiveness of the EU’s climate policy, contribute to global decarbonization and the EU’s climate neutrality by 2050. Within the jurisdiction of the CBAM, the steel industry, as an industry with a very high proportion, is of great significance to how to effectively reduce emissions in various industries in the future. This paper introduces the carbon accounting methods of the steel industry and related emission reduction technologies that have emerged, and analyzes and summarizes the four development directions of the steel industry in the future (large-scale carbon capture, utilization and storage, hydrogen direct reduction steelmaking pilot, circular economy of electric furnace + scrap steel, and focus on transitional technologies), and puts forward the trend to be improved in the future, so as to provide a theoretical basis for the subsequent production and export of China’s steel industry.
文摘Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenarios,including fluctuating noise levels and unpredictable environmental elements,these techniques do not fully resolve these challenges.We introduce an innovative strategy that builds upon Retinex theory and integrates a novel deep network architecture,merging the Convolutional Block Attention Module(CBAM)with the Transformer.Our model is capable of detecting more prominent features across both channel and spatial domains.We have conducted extensive experiments across several datasets,namely LOLv1,LOLv2-real,and LOLv2-sync.The results show that our approach surpasses other methods when evaluated against critical metrics such as Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM).Moreover,we have visually assessed images enhanced by various techniques and utilized visual metrics like LPIPS for comparison,and the experimental data clearly demonstrate that our approach excels visually over other methods as well.
