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先进控制技术在预焙铝电解过程控制中的应用
引用本文:袁艳,张泰山. 先进控制技术在预焙铝电解过程控制中的应用[J]. 计算机测量与控制, 2008, 16(5): 637-640
作者姓名:袁艳  张泰山
作者单位:中南大学,信息科学与工程学院,湖南,长沙,410083;中南大学,信息科学与工程学院,湖南,长沙,410083
摘    要:设计了预焙铝电解生产过程先进控制系统的总体结构,建立了基于神经网络的电解槽参数及阳极效应预测模型,设计了基于支持向量机的槽况解析与槽况诊断专家系统,提出了极距NN—PID控制策略、电解质温度基于混沌优化的模糊控制策略及氧化铝浓度的模糊专家控制策略;同时,针对有关参数的优化控制和产量质量能耗优化控制问题,提出了基于模糊专家控制技术的智能协调策略,以满足全局优化的控制目标;实验结果表明,系统能有效地实现预焙铝电解槽的优化控制,实现槽状态在线解析,并能达到优良的生产控制指标,为企业带来了巨大的经济效益和社会效益。

关 键 词:先进控制  预焙铝电解槽  混沌神经网络  支持向量机  专家模糊控制
文章编号:1671-4598(2008)05-0637-03
修稿时间:2007-09-27

Application of APC in Pre-baked Aluminium Electrolysis Process Control
Yuan Yan,Zhang Taishan. Application of APC in Pre-baked Aluminium Electrolysis Process Control[J]. Computer Measurement & Control, 2008, 16(5): 637-640
Authors:Yuan Yan  Zhang Taishan
Affiliation:Yuan Yan Zhang Taishan (Information Science & Engineering Institute,Central South University,Changsha 410083,China)
Abstract:The collectivity structure of pre-baked aluminium electrolysis process advanced control system is designed.Prediction model of cell parameters based on neural network is constituted;the neural network expert system of cell state diagnosis based on support vector machine(SVM)is designed.The NN-PID control strategy of anode distance,the fuzzy control strategy based on chaos optimal of electro- lyte temperature and the fuzzy-expert control strategy of alumina concentration are proposed.At the same time,considering the optimal control of important parameters,output,quality and energy consumption,the intelligent coordination strategy based on fuzzy-expert con- trol technology is proposed,which can satisfy global optimal control target.The experiment results showed that the system could realize opti- mal control of aluminium electrolysis effectively;realize the cell state diagnosis on-line,and gain good control effect.The proposed system can bring large economic and social profits for the corporations.
Keywords:advanced process control(APC)  pre-baked aluminium electrolysis cell  chaos neural network  support vector machine(SVM)  expert fuzzy control
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