# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 1 Introduction
In order to comply with stringent environmental regulations, WWTPs face a series of problems and challenges, such as wastewater treatment process complexity (dynamic and high-nonlinear processes behavior), difficulties in ensuring real-time control of chemical and biological processes through traditional methods (which leads to high energy consumption and reactants usage), lack of specialized and highly trained human plant operators (which leads to wrong or ineffective decisions), the inability to predict and to prevent plant equipment failures and process disturbances (reactive rather that proactive actions), old plant infrastructure (which imposes the existence of modern control systems and equipment), climate change impact (which requires improved and more efficient control and prevention tools), energy recovery problems, and public pressure regarding environmental protection. Wastewater challenges are becoming more critical as the wastewater flowrate is increased due to population growth, more restrictive regulations regarding plant effluent quality, the sanctions that a WWTP can receive if it pollutes the environment, and the need of sustainable and cost-effective treatment solutions. Nowadays, ML methods (especially DL methods), such as Convolutional Neural Networks (CNNs), Random Forest (RF), and Long Short-Term Memory (LSTM) networks, are used in the prediction of various wastewater quality parameters, in the models accuracy improvement through the identification of relevant data features, in the management of time-series data, in the identification of complex relationships in data, in the monitoring of dynamic variations in wastewater parameters, and so on [8; 9]. In addition, integrating Internet of Things (IoT) technologies with ML methods increases the ML models' real-time monitoring abilities, thus being an intriguing subject for future research [9; 10]. Five important databases, such as Web of Science, Scopus, IEEE Xplore, PubMed, and arXiv, were used to perform a detailed analysis, including studies published from 2010 or newer that employ ML for optimizing wastewater treatment processes. We have defined three main research questions (presented in Section 3.2. Objectives and Purpose of the Review); in this way, an in-depth analysis of ML applications in wastewater treatment optimization (including keyword analysis, analysis of publications' geographical and temporal distribution) was achieved; the main uses of AI technologies in different contexts was investigated (real-time monitoring and control, wastewater quality parameters predictive modeling, process optimization, etc. ); and the essential challenges, limitations, and gaps encountered in the use of ML/DL methods in the wastewater treatment domain were identified. The paper is structured in six sections. Section 2 presents the wastewater treatment processes from a WWTP, the technologies used in this domain, challenges in wastewater treatment, and the performance indicators used in wastewater treatment. Section 3 presents the methodology used to identify gaps in the literature, and in Section 4 the classification of ML/DL methods used in the optimization of wastewater treatment processes is achieved. In Section 5, the results obtained are presented, and the conclusions are presented in Section 6. In this study, PRISMA 2020 guidelines are strictly used for ML applications in wastewater treatment, alongside Litmap citation network analysis for literature exploration. Antecedent reviews also generally used traditional searching databases without systematic citation network maps or standardized reporting. Whereas typical literature surveys concentrate on subcategories of ML, we offer a comprehensive overview that includes classical ML, state-of-the-art deep learning, hybrids, and optimization-based approaches under one systematic framework. Moreover, this literature review focuses on the integration of IoT and digital twins with ML models for real-time wastewater optimization.