Analysis and multi-objective optimization of slag powder process

• Published in: Applied soft computing Vol. 96; p. 106587
• Main Authors: Li, Xiaoli, Shen, Shiqi, Yang, Shengxiang, Wang, Kang, Li, Yang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2020
• Subjects: Material layer thickness; Modeling; Multiobjective optimization algorithm; Slag powder; Multiobjective optimization algorithm; Slag powder; Modeling; Material layer thickness
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2020.106587

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. •Slag powder process is introduced.•Objectives and affecting factors of slag powder process are analyzed.•The models are established by using the generalized regression neural network.•Optimal solution for slag powder production process is determined by proposed CT-NSGAII algorithm.