Abstract
The production process of ground granulated blast furnace slag (GGBS) aims to produce products of the best grade and the highest yields. However, grade and yields are two competing objectives which can not be optimized at the same time by one single solution. Meanwhile, the production process is a multivariable strong coupling complicated nonlinear system. It is hard to establish the accurate mechanism model of this system. Considering above problems, we formulate the GGBS production process as an multiobjective optimization problem, introduce a least square support vector machine method based on particle swarm optimization to build the data-based system model and solve the corresponding multiobjective optimization problem by several multiobjective optimization evolutionary algorithms. Simulation example is presented to illustrate the performance of the presented multiobjective optimization scheme in GGBS production process.
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Acknowledgements
This study was funded by National Natural Science Foundation of China (61473034, 61673053), Specialized Research Fund for the Doctoral Program of Higher Education (20130006110008), Beijing Nova Programme Interdisciplinary Cooperation Project (Z161100004916041).
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Communicated by V. Loia.
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Wang, K., Li, X., Jia, C. et al. Multiobjective optimization of the production process for ground granulated blast furnace slags. Soft Comput 22, 8177–8186 (2018). https://doi.org/10.1007/s00500-017-2761-x
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DOI: https://doi.org/10.1007/s00500-017-2761-x