Analysis and multi-objective optimization of slag powder process

Date

2020-07-28

Advisors

Journal Title

Journal ISSN

ISSN

1568-4946

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

Slag powder is a process with characters of multivariables, strongly coupling and nonlinearity. The material layer thickness plays an important role in the process. It can reflect the dynamic balance between the feed volume and discharge volume in the vertical mill. Keeping the material layer thickness in a suitable range can not only improve the quality of powder, but also save electrical power. Previous studies on the material layer thickness did not consider the relationship among the material layer thickness, quality and yield. In this paper, the yield and quality factors are taken into account and the variables that affect the material layer thickness, yield and quality are analyzed. Then the models of material layer thickness, yield and quality are established based on generalized regression neural network. The production process demands for highest yield, best production quality and smallest error of material layer thickness at the same time. From this point of view, the slag powder process can be regarded as a multi-objective optimization problem. To improve the diversity of solutions, a CT-NSGAII algorithm is proposed by introducing the clustering-based truncation mechanism into solution selection process. Simulation shows that the proposed method can solve the multi-objective problem and obtain solutions with good diversity.

Description

open access article

Keywords

Slag powder, Material layer thickness, Modeling, Multiobjective optimization algorithm

Citation

X. Li, S. Shen, S. Yang, K. Wang, and Y. Li. (2020). Analysis and multi-objective optimization of slag powder process. Applied Soft Computing, 96, 106587

Rights

Research Institute

Institute of Artificial Intelligence (IAI)