Abstract
Sludge bulking is one of the most serious problems in the Wastewater Treatment Plants (WWTPs) and brings about significant economic loss. Sludge Volume Index (SVI), a key sludge sedimentation performance evaluation index, is difficult to be obtained accurately online. To monitor the SVI value, a new soft sensor modeling method based on Principal Component Analysis (PCA) and Elman Neural Network (ElmanNN) is proposed in this paper. The final inputs of model are determined by PCA. Then, the SVI value is modeled by the Elman network in the WWTPs. Finally, compared with other neural networks, the experimental results show that Elman network is more efficient in modeling the SVI. The scale of network can be simplified and its capability of dealing with dynamic information can be strengthened.
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