Fault detection of sludge bulking using a self-organizing type-2 fuzzy-neural-network

Modelling and optimization of membrane process for removal of biologics (pathogens) from water and wastewater: Current perspectives and challenges

“…ANN could also be a helpful approach to identifying imminent risks [ 31 , 105 , 116 ]. An efficient and potent technique that can replicate these non-linear processes even in the face of changing environmental variables is the artificial neural network (ANN) [ 128 , 129 ]. Furthermore, compared to mathematical models based on regression, the ANN model produces more reliable and accurate findings when used for process optimization [ 130 ].…”

Using a supervised machine learning approach to predict water quality at the Gaza wastewater treatment plant

“…26 SVM-based prediction has been extensively researched for tracking and forecasting intake conditions and sludge volume index in wastewater treatment plants. 27 An adaptive multi-output so sensor model, 28 hybrid linear-nonlinear method, 29 data-based predictive control technique, 30 and SVM model 31,32 have also been used to predict effluent index, total solid content, and water quality of wastewater treatment facilities. The least-squares support vector machine (LSSVM) has been presented as a solution to the drawbacks of SVM for large datasets.…”

Water Quality Indicator Interval Prediction in Wastewater Treatment Process Based on the Improved BES-LSSVM Algorithm

“…Cheng et al proposed a variety of kernel single-class SVMs to monitor and predict the intake conditions of wastewater treatment plants [21]. Han et al developed a neural network model for predicting the sludge volume index based on information transfer strength and adaptive second-order algorithms [22]. Wu et al proposed an adaptive multi-output soft sensor model for monitoring wastewater treatment and made several simulation comparisons to prove the superiority of the algorithm [23].…”