# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 2 Wastewater Treatment Processes in an Industrial Wastewater Treatment Plant
### Traditional and AI-Based Technologies Used in Wastewater Treatment
To overcome the limitations of traditional wastewater treatment technologies and to make the transition from reactive to proactive plant management, the integration of AI and of advanced ML models (artificial neural networks--ANNs and DL techniques) provides a set of advantages in the optimization of treatment processes from a WWTP, such as in predicting plant effluent quality parameters, in modeling the complex and variable treatment processes behavior, in improving the plant operational control and efficiency overall, and in improving the predictive capabilities of plant systems and operational efficiency [2; 4; 10; 11]. Through the usage of advanced ML techniques, complex and nonlinear relationships that characterize the treatment processes can be optimized under varying conditions, ensuring an improved prediction accuracy (such as the adjustment of carbon source dosage and dissolved oxygen levels), necessary in the decision-making process of the plant's human operator [24; 25]. For reducing the impact of a plant wastewater treatment process on the environment (greenhouse gas emissions), ML techniques can be used to develop prediction models (such as carbon emission prediction models). They can improve the design of WWTP treatment facilities [26]. Thus, AI models like fuzzy logic (FL) and ANNs have been successfully applied to model the complex and highly nonlinear behavior of wastewater treatment processes, improving the prediction accuracy and the plant's operational efficiency [27]. The usage of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, models that belong to the DL subdomain, comes with an improved prediction accuracy of the wastewater quality parameters (like pH, dissolved oxygen, turbidity, etc. ), a better prediction of the plant effluent quality, an improved performance regarding the nonlinear data and pattern handling, and a superior performance in capturing temporal dependencies, all of these benefits leading to improved plant operational control strategies [8; 24; 28; 29; 30]. According to [PERSON] et al. [27], these AI models, especially ANNs, have been successfully applied in the estimation of wastewater quality parameters like pH, conductivity, and salinity, with high predictive accuracy. According to [PERSON] et al. [31], ANNs were successfully used in wastewater treatment optimization, especially in pollution control processes, in situations where traditional statistical models have not provided the desired results [32]. In work by [PERSON] et al. [33], CNNs (because of their ability to capture temporal dependencies) were used to achieve the prediction of wastewater pH, research that demonstrated that a CNN with six convolutional layers and three pooling layers, combined with a sliding window method, effectively predicts pH values between 7.2 and 8.6 units, ensuring compliance with the imposed standards [8]. According to [PERSON] et al. and [PERSON] et al. [5; 34] the usage of Recurrent Neural Networks (RNNs), particularly of Long Short-Term Memory (LSTM) networks, has proven effective in time-series data analysis, in the prediction of WWTP equipment failures, in the prediction of climate variables over time, and overall in the optimization of treatment process optimization [5; 34]. The integration of DL with LSTM networks (because of their time-series data management ability to track the dynamic variations of wastewaterparameters) has been analyzed from the usage potential point of view, like the prediction of essential (such as COD and pH) wastewater quality parameters [9]. In addition, the usage of hybrid models, such as adaptive neuro-fuzzy inference systems (ANFIS), combined with ANNs and fuzzy logic (FL), opens a new research direction that evaluates the potential of using treated wastewater as a water resource for agricultural reuse, being a viable solution for the sustainable management of water resources [27; 35; 36; 37].