Multiobjective optimization of the production process for ground granulated blast furnace slags

Kang Wang; Xiaoli Li; Chao Jia; Shengxiang Yang; Miqing Li; Yang Li

doi:10.1007/s00500-017-2761-x

Multiobjective optimization of the production process for ground granulated blast furnace slags

Kang Wang, Xiaoli Li^*, Chao Jia, Shengxiang Yang, Miqing Li, Yang Li

^*Corresponding author for this work

Computer Science

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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.

Original language	English
Pages (from-to)	8177-8186
Number of pages	10
Journal	Soft Computing
Volume	22
Issue number	24
DOIs	https://doi.org/10.1007/s00500-017-2761-x
Publication status	Published - 1 Dec 2018

Bibliographical note

Funding Information:
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 (Z1611 00004916041).

Publisher Copyright:
© 2017, Springer-Verlag GmbH Germany.

Keywords

Ground granulated blast furnace slag
MOEA
Multiobjective optimization
PSO-based LS-SVM

ASJC Scopus subject areas

Software
Theoretical Computer Science
Geometry and Topology

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10.1007/s00500-017-2761-x

Cite this

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title = "Multiobjective optimization of the production process for ground granulated blast furnace slags",

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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author = "Kang Wang and Xiaoli Li and Chao Jia and Shengxiang Yang and Miqing Li and Yang Li",

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AU - Wang, Kang

AU - Li, Xiaoli

AU - Jia, Chao

AU - Yang, Shengxiang

AU - Li, Miqing

AU - Li, Yang

N1 - Funding Information: 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 (Z1611 00004916041). Publisher Copyright: © 2017, Springer-Verlag GmbH Germany.

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AB - 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.

KW - Ground granulated blast furnace slag

KW - MOEA

KW - Multiobjective optimization

KW - PSO-based LS-SVM

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Multiobjective optimization of the production process for ground granulated blast furnace slags

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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