Ironmaking involves the separation of iron ores. It not only represents the first step in steelmaking but also is the most capital-intensive and energy-intensive process in the production of steel. The main route for ...Ironmaking involves the separation of iron ores. It not only represents the first step in steelmaking but also is the most capital-intensive and energy-intensive process in the production of steel. The main route for producing iron for steelmaking is to use the blast furnace, which uses metallurgical coke as the reductant. Concerns over the limited resources, the high cost of coking coals, and the environmental impacts of coking and sinter plants have driven steelmakers to develop alternative ironmaking processes that can use non-coking coals to reduce iron ores directly. Since the efficiency and productivity of modern large capacity blast furnaces will be difficult to surpass, blast furnaces will continue to retain their predominant position as the foremost ironmaking process for some time to come. The alternative ironmaking processes are therefore expected to play an increasingly significant role in the iron and steel industry, especially in meeting the needs of small-sized local and regional markets. It is likely that the importance of direct reduced iron （DRI） and hot metal as sources of virgin iron will continue to increase, especially in the developing countries where steelmaking is, and will be, primarily based on electric arc furnace （EAF） minimills. Consequently, the challenges that are faced by the new technology have to be embraced.展开更多
Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data....Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.展开更多
文摘Ironmaking involves the separation of iron ores. It not only represents the first step in steelmaking but also is the most capital-intensive and energy-intensive process in the production of steel. The main route for producing iron for steelmaking is to use the blast furnace, which uses metallurgical coke as the reductant. Concerns over the limited resources, the high cost of coking coals, and the environmental impacts of coking and sinter plants have driven steelmakers to develop alternative ironmaking processes that can use non-coking coals to reduce iron ores directly. Since the efficiency and productivity of modern large capacity blast furnaces will be difficult to surpass, blast furnaces will continue to retain their predominant position as the foremost ironmaking process for some time to come. The alternative ironmaking processes are therefore expected to play an increasingly significant role in the iron and steel industry, especially in meeting the needs of small-sized local and regional markets. It is likely that the importance of direct reduced iron （DRI） and hot metal as sources of virgin iron will continue to increase, especially in the developing countries where steelmaking is, and will be, primarily based on electric arc furnace （EAF） minimills. Consequently, the challenges that are faced by the new technology have to be embraced.
基金This work is supported by the NationalNatural Science Foundation of China(No.52075350)the Major Science and Technology Projects of Sichuan Province(No.2022ZDZX0001)the Special City-University Strategic Cooperation Project of Sichuan University and Zigong Municipality(No.2021CDZG-3).
文摘Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.
