# 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
This combination of ML models (ANFIS, ANNs, and FL) was also applied for optimizing the treatment processes of a plant in predicting the plant effluent quality, in increasing the pollutant removal efficiency degree, in the reduction of the plant operational costs, and overall improving wastewater quality prediction and systems prediction reliability and interpretability [27; 38; 39; 40]. According to [27], these hybrid models have significantly improved the prediction reliability and interpretability of WWTP systems, providing practical applications for agricultural reuse and wastewater management. According to [PERSON] et al. [41], the combination of ML and DL techniques has shown promising results in the handling of multidimensional and large-scale data and in the improvement of the prediction accuracy for various parameters (such as nitrous oxide emissions, etc.). Hybrid DL models, which combine CNNs, LSTM, and GPR (Gaussian Process Regression), were used for predicting water quality parameters (total dissolved solids, electrical conductivity, etc.) in coastal aquifers [42]. According to [PERSON] et al., [PERSON] and [PERSON], and [PERSON] et al. [8; 9; 33], the hybrid methods that combine the advantages (ability to model nonlinear data, robustness in capturing intricate patterns in the data, efficient management of time-series data, the ability to capture long-term dependencies, etc.) of LSTM networks with CNNs were successfully used in wastewater pH prediction, accurate time-series forecasting in wastewater treatment processes, in increasing the WWTP operational efficiency, and in ensuring the environmental compliance of the wastewater treatment processes. Because of their ability to process data in parallel, CNNs require less computational power than LSTM networks (which, on the other hand, ensure a superior accuracy in scenarios with significant temporal dependencies), this being an essential advantage for real-time applications [9]. The usage of explainable AI (such as CNNs, Support Vector Machines--SVMs, ANNs, LSTM networks, GRUs, decision trees, linear and logistic regression, rule-based models, RFs, eXtreme Gradient Boosting--XGBoost, SHapley Additive exPlanations--SHAP, integrated gradients, LIME--Local Interpretable Model Agnostic Explanations, GradCAM--Gradient-weighted Class Activation Mapping, etc. ), such as explainable ML and DL, reduces the discrepancy between human model interpretability and complex model predictions, thus gaining the trust of the plant engineers and decision entities [24; 43; 44; 45; 46]. Also, the usage of statistical methods (complementary to AI techniques), such as fractional factorial designs (FFDs) for wastewater treatment process optimization, led to operational downtime minimization and to the reduction of experimental usage on a large scale, being very useful tools in modelling the complex physicochemical processes from a WWTP and also in scenario development [47]. Moreover, the combination of FFDs with Belief Functions (BFs) and Support Vector Machine (SVM) learning comes with a set of improvements in the optimization of recycling processes (an example in this sense being the processes from a paper mill) [47]. Because they can handle complex, nonlinear treatment process behavior (such as wastewater pH neutralization) and very large plant databases, and also due to their adaptability to changing conditions, ML and DL techniques such as ANNs, SVMs, LSTM, CNNs, and RFs have been widely used for water quality prediction, and also for wastewater treatment optimization (the reducing of energy consumption and alignment with environmental standards) [5; 43; 48]. Also, these ML and DL models were successfully applied in predictingmembrane fouling in membrane bioreactors (MBRs), optimizing their filtration processes, and modelling the fouling dynamics [43,49].