The metallurgy industry consumes a considerable amount of coal and fossil fuels,and its carbon dioxide emissions are increasing every year.Replacing coal with renewable,carbon-neutral biomass for metallurgical product...The metallurgy industry consumes a considerable amount of coal and fossil fuels,and its carbon dioxide emissions are increasing every year.Replacing coal with renewable,carbon-neutral biomass for metallurgical production is of great significance in reducing global carbon consumption.This study describes the current state of research in biomass metallurgy in recent years and analyzes the concept and scientific principles of biomass metallurgy.The fundamentals of biomass pretreatment technology and biomass metallurgy technology were discussed,and the industrial application framework of biomass metallurgy was proposed.Furthermore,the economic and social advantages of biomass metallurgy were analyzed to serve as a reference for the advancement of fundamental theory and industrial application of biomass metallurgy.展开更多
Molten iron temperature as well as Si,P,and S contents is the most essential molten iron quality(MIQ)indices in the blast furnace(BF)ironmaking,which requires strict monitoring during the whole ironmaking production.H...Molten iron temperature as well as Si,P,and S contents is the most essential molten iron quality(MIQ)indices in the blast furnace(BF)ironmaking,which requires strict monitoring during the whole ironmaking production.However,these MIQ parameters are difficult to be directly measured online,and large-time delay exists in offline analysis through laboratory sampling.Focusing on the practical challenge,a data-driven modeling method was presented for the prediction of MIQ using the improved multivariable incremental random vector functional-link networks(M-I-RVFLNs).Compared with the conventional random vector functional-link networks(RVFLNs)and the online sequential RVFLNs,the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems.Moreover,the proposed M-I-RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-output(MIMO)dynamic system,which is suitable for the BF ironmaking process in practice.Ultimately,industrial experiments and contrastive researches have been conducted on the BF No.2in Liuzhou Iron and Steel Group Co.Ltd.of China using the proposed method,and the results demonstrate that the established model produces better estimating accuracy than other MIQ modeling methods.展开更多
基金financially supported by the National Natural Science Foundation of China(No.51704216)the State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing(Nos.41620025,41620026,and 41621009)+1 种基金the Interdisciplinary Research Project for Young Teachers of University of ScienceTechnology Beijing(Fundamental Research Funds f or the Central Universities)(No.FRF-IDRY-20-014)。
文摘The metallurgy industry consumes a considerable amount of coal and fossil fuels,and its carbon dioxide emissions are increasing every year.Replacing coal with renewable,carbon-neutral biomass for metallurgical production is of great significance in reducing global carbon consumption.This study describes the current state of research in biomass metallurgy in recent years and analyzes the concept and scientific principles of biomass metallurgy.The fundamentals of biomass pretreatment technology and biomass metallurgy technology were discussed,and the industrial application framework of biomass metallurgy was proposed.Furthermore,the economic and social advantages of biomass metallurgy were analyzed to serve as a reference for the advancement of fundamental theory and industrial application of biomass metallurgy.
基金Item Sponsored by National Natural Science Foundation of China(61290323,61333007,61473064)Fundamental Research Funds for Central Universities of China(N130108001)+1 种基金National High Technology Research and Development Program of China(2015AA043802)General Project on Scientific Research for Education Department of Liaoning Province of China(L20150186)
文摘Molten iron temperature as well as Si,P,and S contents is the most essential molten iron quality(MIQ)indices in the blast furnace(BF)ironmaking,which requires strict monitoring during the whole ironmaking production.However,these MIQ parameters are difficult to be directly measured online,and large-time delay exists in offline analysis through laboratory sampling.Focusing on the practical challenge,a data-driven modeling method was presented for the prediction of MIQ using the improved multivariable incremental random vector functional-link networks(M-I-RVFLNs).Compared with the conventional random vector functional-link networks(RVFLNs)and the online sequential RVFLNs,the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems.Moreover,the proposed M-I-RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-output(MIMO)dynamic system,which is suitable for the BF ironmaking process in practice.Ultimately,industrial experiments and contrastive researches have been conducted on the BF No.2in Liuzhou Iron and Steel Group Co.Ltd.of China using the proposed method,and the results demonstrate that the established model produces better estimating accuracy than other MIQ modeling methods.
