Sludge Bulking Prediction Using Principle Component Regression and Artificial Neural Network

Sludge bulking is the most common solids settling problem in wastewater treatment plants, which is caused by the excessive growth of filamentous bacteria extending outside the flocs, resulting in decreasing the wastewater treatment efficiency and deteriorating the water quality in the effluent. Previous studies using molecular techniques have been widely used from the microbiological aspects, while the mechanisms have not yet been completely understood to form the deterministic cause-effect relationship. In this study, system identification techniques based on the analysis of the inputs and outputs of the activated sludge system are applied to the data-driven modeling. Principle component regression PCR and artificial neural network ANN were identified using the data from Chongqing wastewater treatment plant CQWWTP , including temperature, pH, biochemical oxygen demand BOD , chemical oxygen demand COD , suspended solids SSs , ammonia NH4 , total nitrogen TN , total phosphorus TP , and mixed liquor suspended solids MLSSs . The models were subsequently used to predict the sludge volume index SVI , the indicator of the bulking occurrence. Comparison of the results obtained by both models is also presented. The results showed that ANN has better prediction power R2 0.9 than PCR R2 0.7 and thus provides a useful guide for practical sludge bulking control.