An optimization framework for achieving optimal hydrocyclone's performance aligning with decision-makers' preferences

• Published in: Powder technology Vol. 448; p. 120233
• Main Authors: E, Dianyu, Tan, Cong, Ye, Qing, Cui, Jiaxin, Zhou, Zongyan, Zou, Ruiping, Yu, Aibing, Kuang, Shibo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024
• Subjects: algorithms; Hydrocyclone; industry; INFO-ELM; multi-criteria decision making; Multi-objective optimization; PICEA-g; technology; TOPSIS; Multi-objective optimization; INFO-ELM; Hydrocyclone; PICEA-g; TOPSIS
• ISSN: 0032-5910
• DOI: 10.1016/j.powtec.2024.120233

Previous hydrocyclone optimization often neglected interactions among key performance objectives, which limits hydrocyclone wide applications to meet increasingly diverse industry demands. This study proposes an optimization framework to identify the most suitable hydrocyclone design and operating conditions with conflicting key performance objectives. An advanced multi-objective evolutionary algorithm (PICEA-g) is employed to generate Pareto-optimal solutions that capture trade-offs among multiple conflicting objectives. A novel data-driven predictive algorithm, INFO-ELM, is introduced to establish nonlinear relationships between key variables and performance objectives, thereby accelerating the search for Pareto-optimal solutions by PICEA-g. Furthermore, a multi-criteria decision-making method (TOPSIS) is utilized to determine the optimal solution based on decision-makers' preferences, ensuring consistency between preference information and decision outcomes. The framework's effectiveness is validated across various separation scenarios using two decision-making strategies. This study offers a comprehensive approach to address trade-offs in hydrocyclone optimization, widening its application in diverse separation scenarios. [Display omitted] •A framework optimizing four key performance objectives in hydrocyclones is proposed.•The framework identifies the most suitable solution for various separation scenarios.•The novel INFO-ELM method improves the predictive accuracy and reliability of BP-ANN.•PICEA-g outperforms other advanced MOEAs in terms of convergence and diversity.