An optimization model for iron-making system covering sinter matching process to blast furnace process is established, in which the energy consumption, CO_2 emission and cost minimizations are taken as optimization ob...An optimization model for iron-making system covering sinter matching process to blast furnace process is established, in which the energy consumption, CO_2 emission and cost minimizations are taken as optimization objectives. Some key constraints are considered according to practical production experience in the modelling. The combination of linear programming(LP)and nonlinear programming(NLP) methods is applied. The optimal sinter matching scheme under given conditions and the optimization results for different objectives are obtained. Effects of sinter grade and basicity on all the optimal objectives and coke ratio in blast furnace process are analyzed, respectively. The results obtained indicate that compared with the initial values,the energy consumption/CO_2 emission of iron-making system decreases by 2.03% for objectives of energy consumption/CO_2 emission minimizations and 1.89% for the objective of cost minimization, the cost decreases by 17.88% and 18.13%, respectively.All the three criteria decrease with the increasing lump usage, coal powder injection, blast temperature, and decreasing coke ratio for the iron-making system.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 p...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 off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （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-out- put （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. 2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estima ting accuracy than other MIQ modeling methods.展开更多
基金supported by the National Key Basic Research and Development Program of China(Grant No.2012CB720405)the Natural Science Foundation of Naval University of Engineering(Grant No.HG DYDJJ-13002)
文摘An optimization model for iron-making system covering sinter matching process to blast furnace process is established, in which the energy consumption, CO_2 emission and cost minimizations are taken as optimization objectives. Some key constraints are considered according to practical production experience in the modelling. The combination of linear programming(LP)and nonlinear programming(NLP) methods is applied. The optimal sinter matching scheme under given conditions and the optimization results for different objectives are obtained. Effects of sinter grade and basicity on all the optimal objectives and coke ratio in blast furnace process are analyzed, respectively. The results obtained indicate that compared with the initial values,the energy consumption/CO_2 emission of iron-making system decreases by 2.03% for objectives of energy consumption/CO_2 emission minimizations and 1.89% for the objective of cost minimization, the cost decreases by 17.88% and 18.13%, respectively.All the three criteria decrease with the increasing lump usage, coal powder injection, blast temperature, and decreasing coke ratio for the iron-making system.
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 off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （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-out- put （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. 2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estima ting accuracy than other MIQ modeling methods.
