Abstract:

Due to the nonlinear and highly time-varying issues of wastewater treatment processes, a kind of multi-variable control method based on the recurrent fuzzy neural network (RFNN) is proposed. The proposed RFNN can obtain self-adaptive control accuracy of operating variables. The controller uses the learning rate on the basis of conventional BP learning algorithm on adaptive learning algorithm and the introduction of momentum to train network parameters, can avoid falling into local optimum network, which improved network control of the system accuracy. Finally, based on the benchmark simulation model (BSM1), experiments validate the effectiveness of the method that control the dissolved oxygen concentration in the fifth partition and nitrate nitrogen concentration in the second partition. Compared to PID, forward neural network and conventional recurrent neural network, the experimental results show that this control method can improve the adaptive control precision of the system.

Key words: wastewater treatment process, recurrent fuzzy neural network, control, adaptive learning algorithm, dissolved oxygen, nitrate nitrogen, dynamic simulation

摘要：

针对污水处理过程具有非线性、大时变等问题,提出了一种基于递归模糊神经网络的多变量控制方法。该方法通过递归模糊神经网络控制器自适应地获得对操作变量的控制精度,控制器在常规BP学习算法的基础上采用学习率自适应学习算法且引入了动量项来训练网络参数,避免网络陷入局部最优,提高了网络对系统的控制精度。最后,基于仿真基准模型(BSM1)平台对第五分区中的溶解氧和第二分区中的硝态氮控制进行动态仿真实验,结果表明,与PID、前馈神经网络和常规递归神经网络相比,该方法能有效提高系统的自适应控制精度。

关键词: 污水处理过程, 递归模糊神经网络, 控制, 自适应学习算法, 溶解氧 , 硝态氮, 动态仿真