计算机工程 ›› 2018, Vol. 44 ›› Issue (6): 279-282,287.doi: 10.19678/j.issn.1000-3428.0046961

• 开发研究与工程应用 • 上一篇    下一篇

基于动态数据驱动的生物氧化槽进气量预测

孙振华,南新元,蔡鑫   

  1. 新疆大学 电气工程学院,乌鲁木齐 830047
  • 收稿日期:2017-04-25 出版日期:2018-06-15 发布日期:2018-06-15
  • 作者简介:孙振华(1991—),男,硕士研究生,主研方向为动态数据驱动、数据融合;南新元(通信作者),教授;蔡鑫,讲师。
  • 基金项目:
    国家自然科学基金“高海拔地区氰化提金生物氧化预处理过程氧化还原电位预估方法研究”(61463047)。

Prediction for Air Input of Bio-oxidation Tank Based on Dynamic Data Driven

SUN Zhenhua,NAN Xinyuan,CAI Xin   

  1. College of Electrical Engineering,Xinjiang University,Urumqi 830047,China
  • Received:2017-04-25 Online:2018-06-15 Published:2018-06-15

摘要: 针对生物氧化槽进气量预测系统的开环调节、强时滞性、预测精度低等问题,提出一种基于动态数据驱动的生物氧化槽进气量预测方法。通过多级氧化槽进气量时间序列数据建立氧化槽进气量状态空间模型,采用Kalman滤波算法进行预测数据和实测数据的动态融合,并对预测值及模型参数进行实时在线更新,由此构建基于动态数据驱动的生物氧化槽进气量预测模型框架。实验结果表明,与未考虑多级槽间相关性的传统方法相比,该预测框架能够对生物氧化槽进气量进行较准确的预测。

关键词: 动态数据驱动, 生物氧化预处理, 状态空间模型, 参数估计, 数据同化, Kalman滤波, 进气量

Abstract: For the open-loop control,strong time delay and low prediction precision in the air input system of bio-oxidation tank,a new method based on the dynamic data driven for predicting air input of bio-oxidation tank is proposed in this paper.Firstly,a state space model is constructed with the time series data of multistage bio-oxidation tanks.Then,Kalman filtering algorithm is used to implement data fusion from the prediction and measured one.And the prediction and model parameters are updated in real time online.Thus,the frame of the prediction model of bio-oxidation tank air input based on the dynamic data driven is constructed.Experimental results show that,the prediction framework can accurately predict the amount of air in the bio-oxidation tank.

Key words: dynamic data driven, bio-oxidation pretreatment, state space model, parameter estimation, data assimilation, Kalman filtering, air input

中图分类号: