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基于神经网络的铝电解混合控制模型研究


  摘 要:铝电解优化控制生产过程的实质是将可控参数尽量控制在工艺要求的目标值范围内。传统的基于神经网络的控制方法或直接判断氧化铝浓度,缺乏对历史浓度的有效追踪,致使判断准确率下降;或对铝电解生产状况进行宏观识别,并调整,但缺乏实时性、及时性。针对上述问题,将神经网络和关联规则库、专家知识库、控制策略相结合,提出了一种新的氧化铝浓度识别及控制方法,从而将神经网络上升为一种混合控制模型HC-NN (hybrid control- neural network)。该模型以控制参数(自变量)为神经网络输入,和其输出共同作为关联规则库部分前件,对氧化铝浓度进行预测,并使其能对可控参数进行自适应调整,进而将氧化铝浓度(因变量)控制在专家知识库的目标范围内,最终实现对铝电解生产过程的优化控制。通过实际生产运行,验证了系统的可行性和有效性。

  关键词:神经网络;智能控制;铝电解;时间序列

  中图分类号:TP273.2文献标志码:A

  文章编号:1001-3695(2010)07-2536-04

  doi:10.3969/j.issn.1001-3695.2010.07.038

  Research on hybrid control methods of aluminum reduction on neural network

  WANG Cheng-lianga,b,PANG Xua,LU Zhi-jiana

  (a.College of Computer Science,b.College of Electrical Engineering, Chongqing University, Chongqing 400044, China)

  Abstract:Substance of control problem of optimal control in the process of aluminum electrolytic production is to control possible the controllable parameters into technological desired ranges. The traditional control method based on ANN is that judging and controlling the concentration of alumina directly. But the method can not follow efficiently historic concentration of alumina so that accuracy rating is not high. Another is that diagnosing and controlling aluminum electrolytic status. But this method is only macroscopic for recognize status and it lacks of real time. According to such problems, combining associative rule base,expert knowledge base,control strategy with ANN,this paper proposed a new method of recognizing and controlling, and it made ANN be a hybrid controlling model HC-NN. The new method used the input (controllable parameters-independent variable) and output of ANN to be one part of the front conditions of the associative rule base so that it could forecast the concentration of alumina, control the controllable parameters of the process of production self-adaptive, control dependent variables into technological desired ranges, and then realized optimal control in the process of aluminum electrolytic production. The feasibility and effectiveness of this approach has been proved by the practical production result.

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