Multi-objective Optimization of Operating Parameters Based on Neural Network and Genetic Algorithm in the Blast Furnace

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• 英文论文题名：Multi-objective Optimization of Operating Parameters Based on Neural Network and Genetic Algorithm in the Blast Furnace
• 论文作者：Heng Zhou ; Chunjie Yang ; Tian Zhuang ; Zelong Li ; Yuxuan Li ; Lin Wang
• 英文论文作者：Heng Zhou ; Chunjie Yang ; Tian Zhuang ; Zelong Li ; Yuxuan Li ; Lin Wang ; College of Control Science and Engineering ; State Key Laboratory of Industrial Control Technology ; Zhejiang University
• 年：2017
• 作者机构：College of Control Science and Engineering,State Key Laboratory of Industrial Control Technology,Zhejiang University;
• 英文论文关键词：Multi-objective Optimization ; Neural Network ; Genetic Algorithm ; NSGA-Ⅱ ; Pareto Optimality
• 会议召开时间：2017-07-26
• 会议录名称：第36届中国控制会议论文集（B）
• 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
• 语种：英文
• 分类号：TF54;TP18
• 学会代码：KZLL
• 会议名称：第36届中国控制会议
• 会议地点：中国辽宁大连
• 主办单位：中国自动化学会控制理论专业委员会
• 学会名称：中国自动化学会控制理论专业委员会
• 页数：4
• 文件大小：194k
• 原文格式：D
• 会议级别：全国

摘要

Addressing the complexity and isolation of the blast furnace, field engineers generally operate the system according to their former experience. While stability and safety are the first priority, it is natural to see extra consumption of ores and fuels.Over the recent years, researchers have been searching for the optimal operation point within the blast furnace by mathematical methods that include mechanism and data-driven modeling. The reported models have seldom succeeded in offering proper operation parameters to optimize several goals at the same time, as the factors of the blast furnace are highly correlative, which cannot be analyzed separately. In order to get the conflicting targets such as the lowest production cost and coke ratio as well as the highest iron quality, a multi-objective optimization model for the blast furnace based on BP neural network and genetic algorithm is proposed. The model is basically constructed on two theories. One is the BP neural network which can provide a mapping rule for multiple inputs and multiple outputs, the other is the genetic algorithm whose core contents are NSGA-Ⅱ algorithm and Pareto optimality that are fit for the multi-objective optimization goal. By optimizing the blast furnace system with this model, the plant is feasible to keep the iron making process efficient and stable simultaneously.
Addressing the complexity and isolation of the blast furnace, field engineers generally operate the system according to their former experience. While stability and safety are the first priority, it is natural to see extra consumption of ores and fuels.Over the recent years, researchers have been searching for the optimal operation point within the blast furnace by mathematical methods that include mechanism and data-driven modeling. The reported models have seldom succeeded in offering proper operation parameters to optimize several goals at the same time, as the factors of the blast furnace are highly correlative, which cannot be analyzed separately. In order to get the conflicting targets such as the lowest production cost and coke ratio as well as the highest iron quality, a multi-objective optimization model for the blast furnace based on BP neural network and genetic algorithm is proposed. The model is basically constructed on two theories. One is the BP neural network which can provide a mapping rule for multiple inputs and multiple outputs, the other is the genetic algorithm whose core contents are NSGA-Ⅱ algorithm and Pareto optimality that are fit for the multi-objective optimization goal. By optimizing the blast furnace system with this model, the plant is feasible to keep the iron making process efficient and stable simultaneously.

引文

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