基于递归RBF神经网络的MBR膜透水率软测量

针对膜生物反应器(membrane bio-reactor,MBR)污水处理过程中膜透水率难以测量的问题,提出一种基于递归径向基神经网络(recurrent radial basis function neural network,RRBFNN)的软测量方法.首先,基于污水处理过程中的实际运行数据,应用偏最小二乘法(partial least squares,PLS)筛选出与膜透水率相关的过程变量;其次,基于RRBFNN建立膜透水率的软测量模型,利用快速梯度下降算法对RRBFNN的参数进行调整,保证了软测量模型的精度;最后,将设计的膜透水率软测量模型应用于实际污水处理过程中,使用污水处理厂实测数据对模型进行验证.验证结果表明,该软测量模型能够实现膜透水率的准确预测,具有较好的预测精度.

Soft-sensor Method for Permeability of the Membrane Bio-Reactor Based on Recurrent Radial Basis Function Neural Network

A soft-sensor method, based on the recurrent radial basis function neural network ( RRBFNN) , was proposed in this paper to solve the problem of the permeability measurement of membrane bio-reactor ( MBR) . First, the data was collected from a real wastewater treatment process in Beijing and the partial least squares ( PLS) technique was utilized to select the variables which have the largest correlation with the permeability. Then, the soft-sensor model was developed to predict the permeability via RRBFNN. Meanwhile, a fast gradient descent method was used to adjust the parameters of RRBFNN. Finally, this soft-sensor method was applied to the real wastewater treatment process. The results show that the proposed soft-sensor method can predict the permeability of MBR with high accuracy.