Multi-furnace optimization in silicon single crystal production plants by power load scheduling. (September 2022)

• Record Type: Journal Article
• Authors: Kang, Lu; Liu, Ding; Wu, Yali; Zhao, Yingzhen; Ping, Guozheng
• Is Part Of: Journal of process control. Volume 117(2022)
• Page Start: 1
• Page End: 13
• Publication Date: 2022-09
• Subjects: Power load scheduling -- Silicon single crystal furnace -- Multi-objective optimization -- DNSGPSO; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.06.013
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: The power consumption in the process of silicon single crystal growth is huge, so the power load must be limited to a specific range to ensure the safe and stable operation on the silicon single crystal furnace. This article establishes a two-objective mathematical model for the multi-furnace optimal scheduling problem in silicon single crystal production process. The first objective is to minimize the maximum completion time of silicon single crystal growth process on multi-furnaces, and the second objective is to minimize the maximum power consumption obtained by the multi-furnace operation together. A disturbed non-dominated sorting particle swarm optimization combined with genetic algorithm (DNSGPSO) is proposed to solve the multi-objective optimization problem. Through simulation analysis of actual growth data, the effectiveness of the proposed model and the superiority of the algorithm are verified. Highlights: This article expands the research fields of actual industrial production scheduling problems. This article can provide theoretical reference for the planning and scheduling of production tasks. This article establishes a multi-objective scheduling model for the silicon single crystal production process to increase production efficiency and decrease production cost. This article proposed DNSGPSO algorithm to solve the multi-objective optimization problem.