# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 4 Classification of ML Models Applied in the Optimization of Wastewater Treatment Processes
### ML Models Applied in the Optimization of Wastewater Treatment Processes
#### 4.2.1 Artificial Neural Networks (ANNs)  
Some of ANNs' specific applications in WWTPs, applications through which various problems are solved that can occur during the plant operating process, are as follows: the prediction of plant effluent quality (with the achievement of real-time prediction of wastewater quality indicators, the prevention of plant emissary pollution, and compliance with the imposed normative domain is assured), the optimization of the wastewater treatment processes parameters (with the optimal chemical reactants dosage and energy consumption reduction being assured), the prediction of plant equipment failure (treatment process interruptions being prevented due to reagent dosage pumps, blower, transducers, or sensors failure, etc.), the forecasting of sludge production (high volume of resulting sludge can raise storage and recycling problems and also supplementary costs), real-time control of treatment processes (the usage of ANN-based controllers usually is a viable solution to manual control; this way the controller parameters are tuned in real-time), and so on.  
A versatile computational model used for predictive and analytical tasks is ANNs, such as Multilayer Perceptron--MLP and Radial Basic Function--RBF networks, mainly used for classification and regression purposes.  
MLP is a type of ANN with a single input layer, one or more hidden layers (the neurons of these type of layers uses a nonlinear activation function), and one output layer, a network that uses a feedforward architecture and that is trained using backpropagation (which adjust the network weights) for error minimization through gradient descendent [31; 67; 71]. Because of their flexibility and predictive power, MLPs are usually used in environmental data analysis (for trend prediction) and for improving treatment processes [31].  
An extension of MLPs with multiple hidden layers are Deep Neural Networks (DNNs) that use a nonlinear activation function (ReLu--Rectified Linear Unit) to improve the network prediction accuracy (in the case of complex datasets) and, in order to prevent overfitting, are trained using unsupervised learning models [39; 72].  
Although they are used for the same tasks, MLP and RBF networks are different because of their architectures (MLPs can have one or more hidden layers, while RBFs usually have just one hidden layer), due to the type of activation functions that are used in hidden layers (ReLu is usually used by MLPs and Gaussian Radial Basis activation function for RBFs), and due to the training methodologies. These differences leave their mark on the network's performance; RBFs usually exceeding MLPs because of their high speed and nonlinear data-efficient handling.  
In order to compare the ANNs' performance with other models, specific metrics such as the coefficient of determination (R\({}^{2}\)) and mean squared error (MSE) [27; 31] are used.  
ANNs have been successfully applied in the wastewater treatment domain, such as in the prediction of essential pollutants and in the optimization of control strategies [39; 43; 79]. The usage of ANNs with GAs is a hybrid solution (GA-ANN) that uses evolutionary principles in order to improve the solution development process, highlighting ANNs' adaptability in solving complex problems, such as those from wastewater treatment process optimization [31].