# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 1 Introduction  
Regarding the global and regional context of wastewater treatment infrastructure, in Europe, over 23,000 municipal WWTPs operate, serving approximately five hundred million people, annually treating approximately fifty billion cubic meters of wastewater. In Romania, there are around four hundred municipal WWTPs (including the industrial ones), the most representative being the ones from Bucharest city (with a capacity of 6.2 m\({}^{3}\)/s),Timisoara, and Cluj-Napoca. In order to comply with European Union Water Framework Directive requirements, these WWTPs are facing high pressure regarding their efficiency improvement because, at the present time, they are consuming between one and three percent of national electricity (the cost with the necessary reactants is between fifty and twenty-five percent of the plant budget, while the maintenance cost is between twenty to thirty percent of the budget) [1; 2; 3]. It can be said that the attempt to apply AI techniques (ML models) to optimize wastewater treatment processes from WWTP is justified by the current situation, which is not exactly the best one. In the literature, several reviews have previously analyzed the integration of AI into wastewater treatment processes optimization, each one with a certain specificity and certain limitations. So, in [PERSON] et al. [4] a review of ML application in drinking water and wastewater treatment monitoring systems is performed, but it does not not present the used systematic methodology, focusing on monitoring rather than on optimization. In [PERSON] et al. [5], the AI technologies used in wastewater treatment are reviewed, with the current trends being highlighted, without the use of a systematic review protocol or a comprehensive performance comparison of ML methods [6]. In [PERSON] and [PERSON] [7], the potential and limitations of AI technologies in wastewater applications are analyzed, but without systematic literature mapping and research trend quantitative analysis. So, the main gap of the existing literature is the fact that they lack a systematic methodology following established guidelines (PRISMA) or miss relevant studies. They also combine drinking water with wastewater treatment applications (which are not analyzed separately), focusing on monitoring rather than on optimization. Also, limited comparative analyses of ML methods are presented, necessary attention not being given to the ML models' practical implementation and integration challenges with the usually outdated infrastructure of industrial WWTPs. Because of the rapid advancement in DL and hybrid approaches (2020-2025), these techniques have not been analyzed in depth in previous studies. This article explores the implications of using artificial intelligence, specifically ML, a subdomain of AI, in optimizing wastewater treatment processes from an industrial WWTP. The documentation level of the specialized literature regarding the application of ML methods in the wastewater treatment domain is assessed, as this topic is important due to the challenges in optimizing the wastewater treatment processes in a plant. For instance, most of the treatment processes from a WWTP are very complex, having dynamic and highly nonlinear behavior (a suggestive example in this sense is the wastewater pH neutralization process), making them very difficult to analyze, model, control, and optimize. Thus, works from specialized literature that are aimed at optimizing wastewater treatment processes using ML methods are identified.