冲天炉熔炼过程神经-模糊建模与遗传优化

Modeling and Optimization of Cupola Melting Operation with Adaptive Neuro-Fuzzy Inference System Modeling and Genetic Algorithms

网形图是指导冲天炉熔炼操作的重要工具。为认识熔炼规律,实现冲天炉的优化控制,必须建立恰当的数学模型,以优化操作工艺。综合自适应模糊推理的建模功能和神经网络的学习能力,直接从实验数据中提取推理规则,建立了基于网形图的冲天炉熔炼过程模型。模型具有较高的预测精度和泛化能力,利用它可以帮助操作者认识熔炼规律,据此得出的新型网形图使用起来更为方便快捷。同时,将自适应模糊推理模型与遗传算法耦合,得到了最高铁水温度和最高热效率时的供风量和焦耗。研究形成的建模与优化方法推广应用到其它工艺过程的建模与优化。

Copula net diagram is an important tool for guiding the cupola operation. In order to control and optimize the melting process properly, it is necessary for mathematical model to describe the relationships between melting parameters. By the coupled use of adaptive fuzzy inference modeling and artificial neural network learning ability, a set of operating rules, which can help us better understand the basic principles of cupola melting operation, has been generated directly from the experimental net diagram data. The developed fuzzy inference systems can the relationships between operating parameters accurately. A new type of diagram is proposed to simplify the determination of optimal blast of and carbon rates by the cupola supervisor. By integrating in the genetic optimization procedure, the fuzzy inference systems are used to determine the optimal blast and carbon rate for the highest melting temperature and the lowest energy consumption. The methodology presened can be used for other data based process modeling and optimization practices.