Research on Optimization of "Deoxidation Alloying" of Molten Steel Based on Linear Programming

Anna Li; Dongqing Xu

doi:10.18282/ims.v3i1.310

Anna Li Anhui University of Finance and Economics
Dongqing Xu Anhui University of Finance and Economics

Ariticle ID: 310

427 Views, 18 PDF Downloads

DOI: https://doi.org/10.18282/ims.v3i1.310

Keywords: Alloy Element Yield, Optimization Scheme, Principal Factor Analysis, Multiple Linear Regression Analysis, Linear Programming Model

Abstract

Aiming at the optimization of the supporting solution for molten steel "deoxidation alloying", the cost of "deoxidation alloying" is minimized from an economic perspective. Using Excel, Eviews and spss software programming, through factor analysis, clustering dimension reduction, principal component analysis Multiple linear regression analysis and linear programming optimization analysis, the author found out the main factors that affected the yield of alloy elements. This paper establishes a multiple linear regression mathematical model that affects the main factors of alloy elements and yield. According to the reference alloy price, the linear programming model is adopted to find the optimal solution of alloy ingredients.

References

Qian Z, Ge L, Wang W. Research on optimization of converter alloy composition based on grey correlation method (in Chinese). Journal of Fuyang Teachers College (Natural Science Edition) 2020; 37(1): 11-16.

Fang Y, Gao Z. Model of influencing factors of element yield based on principal component analysis (in Chinese). China Metal Bulletin 2019; (6): 140-141.

Cheng R, Wang R, Ren C, et al. Optimization of the "deoxidation alloying" batching scheme for molten steel (in Chinese). Energy Saving 2020; 39(2): 86-87.

Ren X, Zhu R, Yao Q, et al. Research on the yield of molten steel "deoxidation alloying" based on multiple linear regression (in Chinese). Computer Programming Skills and Maintenance 2020; (3): 23-25+70.

Jiang H. Research on the prediction model for the yield of molten steel "deoxidation alloying" (in Chinese). World Nonferrous Metals 2020; (1): 180-181.

Zhou S, Dong C, Zhang Z, et al. Research on the prediction of deoxidized alloying composition based on fuzzy programming model (in Chinese). Scientific Consulting (Science and Technology Management) 2019; (10): 84.

Tan Y, Wu J, Li K. Research on the prediction of metal element yield based on gray neural network model (in Chinese). Information Recording Materials 2019; 20(9): 40-41.

Li M, Wu Y, Guo W. Research on the yield of alloy in molten steel deoxidation alloying. Chinese and Foreign Entrepreneurs 2019; (21): 87.

Du J, Han C, Cai K. Predictive control for mold level of continuous casting based on least squares support vector machine. Journal of Iron and Steel Research 2007; (8): 28-31.

Yang G. Mathematical modeling (in Chinese). Shanghai: Shanghai University of Finance and Economics Press; 2015.

Fan H, Feng Z, Yu R. Optimization of the scheme cost of "deoxidized alloying" in steel and water (in Chinese). Yunnan Chemical Industry 2020; 47(2): 45-46.

Liu P, Zhu J, Gao Z, et al. Analysis of deoxidation alloying ingredients based on BP neural network (in Chinese). Journal of Qilu University of Technology 2019; 33(5): 74-80.

Zhang T, Zhu J, Wang H, et al. Research on optimization of batching scheme for molten steel deoxidation alloying based on fuzzy linear programming (in Chinese). Journal of Qinghai University 2019; 37(5): 73-81.

Dai Y, Xie W, Chen J, et al. Analysis and research of deoxidized alloying ingredients based on gray correlation (in Chinese). Scientific Consulting (Science and Technology Management) 2019; (9): 31.

Dong C, Gong X, Xu C, et al. Research on the prediction of deoxidation alloying composition based on BP neural network (in Chinese). Scientific Consulting (Science and Technology Management) 2019; (9): 45.

Zhou H, Zhang Q, Han X, et al. Optimized design of deoxidation alloying scheme for converter smelting (in Chinese). World Nonferrous Metals 2019; (13): 12-13.

Liu F, Zhou Y, Tang J, et al. Optimization of the "deoxidation alloying" batching scheme for molten steel (in Chinese). Modern Computer 2019; (22): 8-13.

Wang M. Research on the optimization of the "deoxidation alloying" batching scheme for molten steel (in Chinese). Think Tank Times 2019; (31): 292+296.

Zheng T. Optimization of batching scheme for molten steel "deoxidation alloying" (in Chinese). Think Tank Times 2019; (29): 287+294.