# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 2 Wastewater Treatment Processes in an Industrial Wastewater Treatment Plant
### Challenges in Wastewater Treatment
On the other hand, the development of other explainable AI techniques (such as linear and logistic regression, decision trees, rule-based models, RF, eXtreme Gradient Boosting--XGBoost, SHapley Additive exPlanations--SHAP, integrated gradients, Gradient-weighted Class Activation Mapping--Grad-CAM, etc.) goes beyond the optimization of wastewater treatment processes, bringing real benefits to the interpretability of complex climate models, making things more accessible for researchers and decision makers [4; 61]. The success of explainable ML and DL models (due to the improved data processing power and pattern recognition abilities) in the improvement of climate models' accuracy and prediction suggests promising results in this domain [4; 46]. Another challenge is reducing plant energy consumption and operational costs, which in most countries is a significant problem due to the use of traditional treatment methods (sedimentation, filtration, coagulation, flocculation, etc.) [9]. According to [PERSON] et al. [49], AI -based systems can predict the presence of micro pollutants in wastewater, predict system membrane fouling (essential for maintaining wastewater purification efficiency), and identify patterns and trends in large databases, elements that traditional methods may miss. On the other hand, because of the plant influent fluctuating nature (changes in the input flowrate, such as daily and seasonal variations, variations caused by weather conditions, industrial or accidental variations, etc. ), the WWTP must continuously adapt (using adaptive learning techniques like reinforcement learning, ANNs, transfer learning, online learning, etc.) to these influent variations through the usage of real-time monitoring and control systems to maintain plant efficiency and to ensure an effluent that respects the imposed normative [24; 43]. Moreover, to remove specific pollutants (such as nitrous oxide emissions--N\({}_{2}\)O, hydrogen sulfide--H\({}_{2}\)S, carbon dioxide--CO\({}_{2}\), COD, BOD, ammonium--NH\({}_{4}\)+, nitrates--NO\({}_{3}\)- and nitrites--NO\({}_{2}\)-, phosphates--PO\({}_{4}\)3-, heavy metals, etc. ), in many cases, due to the wastewater treatment process' dynamic and high-nonlinear behavior, the usage of advanced predictive models is necessary (such as hybrid models that integrate multiple AI techniques, such as ANNs, ANFIS, and FL, evolutionary algorithms and metaheuristics, etc. ), which raises other specific model issues and challenges [41]. Additional challenges are given by the use of green materials necessary for adsorption, filtration, catalysis, or support for microorganisms (biochar, activated carbon from natural sources, reused agricultural residues, etc.) in the wastewater treatment processes, which raises financial and technical problems, as well as the need for lifecycle studies to ensure sustainability. So, the usage of AI techniques extends beyond wastewater treatment optimization, having an essential role in green material design and optimization for wastewater treatment, thus promoting the use of advanced solutions for environmental management and protection [62]. The integration of ML and DL models into the existing control systems (SCADA or programmable logic controllers--PLC) in a WWTP imposes the updating of existing software and hardware, and also plant human operator and engineer training (to develop the necessary skills to implement and manage plant AI-based systems effectively), processes that can be time-consuming and very expensive, this being another challenge in the analyzed domain [43]. Another challenge consists of integrating AL (ML and DL) models with IoT technologies to create intelligent networks dedicated to monitoring WWTPs located in rural or remote areas, where traditional monitoring methods are ineffective or infeasible [10; 54].