Dissolved oxygen control of wastewater treatment process using self-organizing fuzzy neural network

周红标

doi:10.11949/j.issn.0438-1157.20161514

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Dissolved oxygen control of wastewater treatment process using self-organizing fuzzy neural network

更新时间：2025-12-10

- Dissolved oxygen control of wastewater treatment process using self-organizing fuzzy neural network
- CIESC Journal Issue 4, Pages: 1516-1524(2017)
- 作者机构：
  
  淮阴工学院自动化学院,江苏,淮安,223003
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20161514
  CLC： P273
- Online First：05 April 2017，
  
  Published：05 April 2017
- 稿件说明：
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周红标. Dissolved oxygen control of wastewater treatment process using self-organizing fuzzy neural network[J]. CIESC Journal, 2017, (4): 1516-1524.
DOI：

周红标. Dissolved oxygen control of wastewater treatment process using self-organizing fuzzy neural network[J]. CIESC Journal, 2017, (4): 1516-1524. DOI： 10.11949/j.issn.0438-1157.20161514.

摘要

针对活性污泥污水处理过程溶解氧浓度控制问题，提出一种基于自组织模糊神经网络（SOFNN）的控制方法。该神经网络控制器依据激活强度和互信息理论在线动态增长和修剪规则层神经元，以满足实际工况的动态变化。同时，采用梯度下降算法在线优化隶属函数层中心、宽度和输出权值，以保证SOFNN的收敛性。进一步通过Lyapunov稳定性理论对SOFNN学习率进行分析，给出控制系统稳定性证明。最后在国际基准仿真平台BSM1上进行实验验证。实验结果显示，与PID、模糊逻辑控制（FLC）和固定结构FNN等控制策略相比，SOFNN在跟踪精度、控制平稳性和自适应能力上更具有优势。

Abstract

A self-organizing fuzzy neural network (SOFNN) control method is proposed

and its application system is designed for controlling the dissolved oxygen (DO) concentration in the activated sludge wastewater treatment processes. The neurons of rule layer are grown or pruned adaptively based on firing strength and mutual information to meet dynamic change of the real operating condition. Meanwhile

the centers and widths of membership functions and weights of output layer are trained by gradient descent optimization algorithm to ensure the convergence of SOFNN. The stability of the control system is proved based on the analysis of the learning rates of parameters in SOFNN by applying the Lyapunov stability theory. This control strategy is investigated and evaluated based on international Benchmark Simulation Model No.1 (BSM1). Experimental results demonstrate that the SOFNN controller performs better than PID

fuzzy logical control (FLC)

model predictive control (MPC)

and some other existing control methods. Performance comparisons indicate that the proposed SOFNN control strategy obtains higher tracking accuracy

better control placidity and superior adaptive capability.

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