The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so.

This paper deals with the fault classification of centrifugal pumps, based on the residuum between the output and the input of an Autoencoding Convolutional Neural Network previously trained for abnormal behaviour detection. The proposed classification method performs a dimensional reduction of the residuum vector using Principal Component Analysis, and then, based on the first 3 principal components, classifies the data, using a simple rule-based algorithm, in one of the classes: normal, clogged filter, broken fan blade, detached rotor section and other fault source. The classification method proved to be reliable in an industrial application, providing a 90% correct identification of the machine condition.

This paper deals with the increasing of the operation efficiency of a wastewater treatment plant by reducing the level of pollutant concentration in the effluent. In essence, the main goal is the reduction of the concentration of organic substrate, Model Predictive Control algorithm being used for this, considering as a model of the wastewater treatment process a neural network model based on a simplified mathematical model of the 4th order. For the implementation of the control algorithm, two neural networks were trained. In the first version of the neural network, it was considered that all 4 state variables are measurable and in the second version, only the organic substrate was considered measurable. Regarding the training of the two neural networks, good results were obtained in the case of both versions. Instead, the control algorithm gave better results when the second neural network was used (lower values of the substrate, lower variations of the aeration rate, which means a lower cost of operation).