Multi-furnace optimization in silicon single crystal production plants by power load scheduling

Kang, Lu; Liu, Ding; Wu, Yali; Zhao, Yue; Ping, Guozheng

doi:10.1016/j.jprocont.2022.06.013

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…These accomplishments provide valuable guidance and support for the collaborative optimization of the entire process in the MSWI process. In the field of intelligent optimization decision making, studies on the intelligent decision making of the entire process operation in mineral processing [226], scheduling optimization of the ethylene cracking furnace system [227], multifurnace optimal scheduling of silicon single crystal and fused magnesium production processes [228,229], and others have been successfully conducted. These achievements underscore that research on multi-objective real-time optimization algorithms and human-machine collaborative enhanced interactive evolution for intelligent decision making in the MSWI process has a solid theoretical foundation and practical feasibility.…”

Section: Collaborative Optimization Of Whole Processmentioning

confidence: 99%

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Sustainability

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Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.

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Section: Collaborative Optimization Of Whole Processmentioning

confidence: 99%

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Sustainability

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“…Te purpose of scheduling is to determine the jobs allocated on diferent types of single crystal furnace equipment, so as to minimize the maximum fuzzy completion time of the whole production system. Te following constraints must be met [18]:…”

Section: Te Model Of the Silicon Single Crystal Batch Schedulingmentioning

confidence: 99%

“…For the same job number, the number of occurrences represents the number of the processing of the same size silicon single crystal rod. And the second stage variable encoding representation is shown in equation(18). Take Table3as example, in the frst stage, the frst column corresponds to the column summation x i1 + x i2 + x i3 � 10, then the variable dimension is 10 dimensions.…”

mentioning

confidence: 99%

Two‐Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time

Kang

Liu

et al. 2023

Mathematical Problems in Engineering

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Considering the widely existing processing time uncertainty in the real-world production process, this paper constructs a fuzzy mathematical model for the silicon single crystal production batch scheduling problem to minimize the maximum completion time. In this paper, a two-stage hybrid optimization algorithm (TSHOA) is proposed for solving the scheduling model. Firstly, the improved differential evolution algorithm (IDE) is used to solve the order quantity allocation problem of silicon single crystal with different sizes to obtain the quantity of silicon single crystal rods with different sizes produced by different types of single crystal furnaces. Secondly, the variable neighborhood search (VNS) algorithm is adopted to optimize the order quantity sequencingof batch production processes. Finally, simulations and comparisons demonstrate the feasibility of the model and the effectiveness of TSHOA.

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Adaptive Constraint Penalty-Based Multiobjective Operation Optimization of an Industrial Dynamic System With Complex Multiconstraint

Zhou,

Zhang,

Chai

2024

IEEE Trans. Cybern.

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Multi-furnace optimization in silicon single crystal production plants by power load scheduling

Cited by 4 publications

References 19 publications

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Two‐Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time

Adaptive Constraint Penalty-Based Multiobjective Operation Optimization of an Industrial Dynamic System With Complex Multiconstraint

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