强跟踪平方根UKFNN的铝电解槽工耗动态演化模型

李太福; 姚立忠; 易军; 胡文金; 苏盈盈

doi:10.3724/SP.J.1004.2014.00522

强跟踪平方根UKFNN的铝电解槽工耗动态演化模型

doi: 10.3724/SP.J.1004.2014.00522

1.
重庆科技学院电气与信息工程学院重庆 401331;
2.
西安石油大学电子工程学院西安 710065

基金项目:

国家自然科学基金（51075418，51374268，61174015），重庆市自然科学基金（CSTC2012JJA1475），重庆市教委科学技术研究项目（KJ121410），重庆科技学院校内科研基金（CK2011B04，CK2011Z01）资助

详细信息

作者简介:
李太福重庆科技学院电气与信息工程学院教授. 2004 年获得重庆大学博士学位. 主要研究方向为智能控制和软测量.E-mail：litaifuemail@qq.com

通讯作者:
姚立忠

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出版历程
- 收稿日期: 2012-07-18
- 修回日期: 2013-02-22
- 刊出日期: 2014-03-20

An Improved UKFNN Based on Square Root Filter and Strong Tracking Filter for Dynamic Evolutionary Modeling of Aluminum Reduction Cell

1.
Department of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331;
2.
School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710065

Funds:

Supported by National Natural Science Foundation of China (51075418, 51374268, 61174015), Natural Science Foundation of Chongqing (CSTC2012JJA1475), Science Technology Research Project of CQJW (KJ121410), and Campus Research Foundation of Chongqing University of Science and Technology (CK2011B04, CK2011Z01)

摘要

摘要: 铝电解过程具有多变量、强耦合、强干扰、参数时变等特征，故其模型开发是一个技术难点. 根据该过程的特点，本文提出强跟踪平方根无迹Kalman神经网络（Strong tracking square root unscented Kalman filter neural network，STR-UKFNN），并用其建立铝电解槽工艺能耗的动态演化模型. 该方法利用误差协方差矩阵的平方根代替UKFNN算法中的协方差阵，避免误差协方差矩阵可能出现负定而导致滤波发散，并在UKFNN算法中引入渐消因子和弱化因子，实时调整滤波增益，提高模型收敛速度和其对突变状态的跟踪能力. 通过某铝厂170kA预焙槽的日报样本验证表明，该方法提高了能耗模型的精度和对电解槽突变状态的实时跟踪能力，有助于指导铝电解过程操作参数的优化.
- 铝电解 /
- 无迹卡尔曼滤波 /
- 神经网络 /
- 强跟踪滤波 /
- 动态演化建模
Abstract: The aluminum electrolysis process has multiple characteristics including multivariate, strong coupling, strong interference and time-varying parameters. Therefore, its model development is technically difficult. According to the characteristics of the process, an improved unscented Kalman filter neural network based on strong tracking filter and square root filter (STR-UKFNN) is proposed in this paper. Then, the STR-UKFNN is used to create the dynamic evolutionary model for energy consumption of aluminum reduction cell. Firstly, the state covariance matrix of the UKFNN algorithm is replaced by its square root to participate in recursive operations; Secondly, the filter gain matrice in the algorithm of UKFNN is adjusted by introducing the time-varying fading factor and the diminishing factor. A series of experiments have been conducted by using the daily samples from the 170kA new pre-baked cell. The experimental results show that the method improves the precision of the energy model and the real-time tracking ability for the abrupt state change of the aluminum reduction cell. So the method is helpful to guide the optimization of operating parameters in the aluminum electrolysis process.
- Aluminum electrolysis /
- unscented Kalman filter (UKF) /
- neural network /
- strong tracking filter /
- dynamicevolutionary modeling

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参考文献(24)

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强跟踪平方根UKFNN的铝电解槽工耗动态演化模型

doi: 10.3724/SP.J.1004.2014.00522

作者简介:
李太福重庆科技学院电气与信息工程学院教授. 2004 年获得重庆大学博士学位. 主要研究方向为智能控制和软测量.E-mail：litaifuemail@qq.com

通讯作者:
姚立忠

计量

An Improved UKFNN Based on Square Root Filter and Strong Tracking Filter for Dynamic Evolutionary Modeling of Aluminum Reduction Cell

计量

目录

留言板

强跟踪平方根UKFNN的铝电解槽工耗动态演化模型

doi: 10.3724/SP.J.1004.2014.00522

作者简介: 李太福 重庆科技学院电气与信息工程学院教授. 2004 年获得重庆大学博士学位. 主要研究方向为智能控制和软测量.E-mail：litaifuemail@qq.com

通讯作者: 姚立忠

计量

出版历程

An Improved UKFNN Based on Square Root Filter and Strong Tracking Filter for Dynamic Evolutionary Modeling of Aluminum Reduction Cell

计量

出版历程

目录

作者简介:
李太福重庆科技学院电气与信息工程学院教授. 2004 年获得重庆大学博士学位. 主要研究方向为智能控制和软测量.E-mail：litaifuemail@qq.com

通讯作者:
姚立忠