Liu Yiqi, Zhang Jing, Qiu Zhuyi, Zhang Yigang, Yu Guangping, Ye Hongtao, Cai Zefan
Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, School of Automation Science & Engineering, South China University of Technology, Guangzhou, 510640, China.
Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle Systems, School of Automation Science & Engineering, South China University of Technology, Guangzhou, 510640, China.
Water Res X. 2024 Jul 30;24:100245. doi: 10.1016/j.wroa.2024.100245. eCollection 2024 Sep 1.
Advanced controllers often offer an innovative solution to proper quality control in wastewater treatment processes (WWTPs). However, nonlinearity and uncertain disturbances usually make the conventional control strategies inadequate or impossible for the stable operations of WWTPs. To guarantee the stability of ammonia nitrogen concentration ( ) control in WWTPs, a direct adaptive neural networks-based sliding mode control (ANNSMC) strategy has been proposed in this article. A sliding mode controller is designed and implemented with the help of an adaptive Neural Network (ANN), named Radial Basis Function Neural Network (RBFNN), which can approach the desired control law accurately. Also, the stability of a system installed with the ANNSMC is analyzed by using the Lyapunov theorem, which ensures system robustness and adaptability. Additionally, to deal with high energy consumption and low treatment efficiency problems in the wastewater denitrification processes, this paper proposes a dual-loop denitrification control strategy and validates it in the Benchmark Simulation Model No.2 (BSM2) platform. The strategy can strengthen the denitrification efficiency by collaborating the with nitrate nitrogen ( ) concentration in the WWTPs properly. The experimental results demonstrate that the proposed strategy can obtain remarkable stability and robustness, reducing energy consumption effectively compared with other standard and advanced control strategies.
先进的控制器常常为污水处理过程（WWTPs）中的适当质量控制提供创新解决方案。然而，非线性和不确定干扰通常使得传统控制策略对于污水处理厂的稳定运行而言不足或无法实现。为保证污水处理厂中氨氮浓度（ ）控制的稳定性，本文提出了一种基于直接自适应神经网络的滑模控制（ANNSMC）策略。借助自适应神经网络（ANN），即径向基函数神经网络（RBFNN），设计并实现了一种滑模控制器，其能够精确逼近期望控制律。此外，利用李雅普诺夫定理分析了安装有ANNSMC的系统的稳定性，这确保了系统的鲁棒性和适应性。另外，为解决废水反硝化过程中的高能耗和低处理效率问题，本文提出了一种双环反硝化控制策略，并在基准模拟模型2（BSM2）平台上对其进行了验证。该策略通过在污水处理厂中适当地将 与硝酸盐氮（ ）浓度协同作用来提高反硝化效率。实验结果表明，与其他标准和先进控制策略相比，所提出的策略能够获得显著的稳定性和鲁棒性，并有效降低能耗。
