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A PSO-RRBF neural network for BOD prediction in sewage treatment

Aiming at the question that the effluent biochemical oxygen demand (BOD) in sewage treatment is difficult to measure accurately in real time, a soft-measurement model of recursive radial basis function (RRBF) neural network based on PSO algorithm (PSO-RRBF) is proposed to predict the effluent BOD. Firstly, the PSO algorithm is put forward to determine not only the input variables but also the structure of the RRBF effectively. Secondly, the gradient descent method is used to adjust the weights, center and width. Finally, the soft-measurement model is applied to the actual sewage treatment process. The experimental results show that the soft-measurement model has a more compact structure and its accuracy is improved compared with other models.