基于POD-LSTM的污水处理过程模型预测控制
DOI:
作者:
作者单位:

沈阳化工大学信息工程学院 沈阳 110142

作者简介:

通讯作者:

中图分类号:

TP273;TN01

基金项目:

国家自然科学基金(61503257)、国家重点研发计划(2018YFB2003704)项目资助


Model predictive control of sewage treatment process based on POD-LSTM
Author:
Affiliation:

School of Information Engineering, Shenyang University of Chemical Technology,Shenyang 110142, China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    为了解决模型预测控制在污水处理等大型非线性系统中求解非线性优化问题时计算成本较高的问题,本文提出了一种应用于污水处理基准的降阶神经网络模型预测控制算法。首先,针对污水处理中的大规模非线性和强耦合性系统,采用本征正交分解方法构建出降阶过程模型,降低非线性系统的复杂度。然后,利用长短期记忆网络来近似降阶之后的系统,从而解决降阶后的系统难以用显式表达的问题。最后,在此降阶系统的基础上设计模型预测控制器,实现对污水处理的高效控制。实验结果表明,在保证较好控制效果的同时,所提出的降阶神经网络模型预测控制策略相较于污水处理第一原理模型的模型预测控制策略,计算时间大幅度减少。

    Abstract:

    In order to solve the problem of high computational cost of model predictive control when solving nonlinear optimization problems in large nonlinear systems such as wastewater treatment, this paper proposes a reduced-order neural network model predictive control algorithm applied to wastewater treatment benchmark. First, for large-scale nonlinear and strongly coupled systems in wastewater treatment, the intrinsic orthogonal decomposition method is used to construct a reduced-order process model to reduce the complexity of the nonlinear system. Then, the long short-term memory network is used to approximate the reduced-order system, thereby solving the problem that the reduced-order system is difficult to express explicitly. Finally, a model predictive controller is designed based on this reduced-order system to achieve efficient control of wastewater treatment. Experimental results show that while ensuring good control effect, the proposed reduced-order neural network model predictive control strategy significantly reduces the computational time compared with the model predictive control strategy of the first principle model of wastewater treatment.

    参考文献
    相似文献
    引证文献
引用本文

马会彪,曾静.基于POD-LSTM的污水处理过程模型预测控制[J].电子测量技术,2024,47(13):84-88

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2024-11-07
  • 出版日期: