A SVI soft sensor model based on improved PSO-Elman neural network

SVI, a sludge bulking index, is difficult to be obtained online. A soft sensor model of SVI based on improved PSO-Elman neural network is proposed in this paper. First, to solve the problems of nonlinear, hysteresis characteristics and so on of sludge bulking process, an Elman neural network with dynamic recursive properties is introduced to determine the model structure. Secondly, to improve the learning ability and convergent accuracy of the proposed SVI soft sensor model, an improved particle swarm algorithm is studied to optimize the connection weights of Elman neural network. Finally, the proposed SVI soft sensor model is applied to the actual process of wastewater treatment process. The simulation results show that the soft sensor model can predict the SVI values online, and owns better predicting accuracy.