Abstract
Sludge bulking is the most common solids settling problem in wastewater treatment plants, resulting in the wastewater treatment efficiency decreasing and the water quality in the effluent deteriorating. Previous studies showed that the mechanisms have not yet been completely understood to form the deterministic cause-effect relationship. In this study, Extreme Learning Machine (ELM) was identified using the data from Chongqing wastewater treatment plant (CQWWTP), including temperature, pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), ammonia (NH\(_{\rm 4}^{\rm +}\)), total nitrogen (TN), total phosphorus (TP), and mixed liquor suspended solids (MLSS). The models were subsequently used to predict the sludge volume index (SVI), the indicator of the bulking occurrence. Results showed that the model has the prediction power R2 of 0.85, which providing a useful guide for practical sludge bulking control.
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Zhao, Y., Xie, Z., Lou, I. (2015). Using Extreme Learning Machine for Filamentous Bulking Prediction in Wastewater Treatment Plants. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_1
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DOI: https://doi.org/10.1007/978-3-319-14066-7_1
Publisher Name: Springer, Cham
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