# 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  
Studying the treatment processes from Romanian industrial WWTPs, it was observed that the traditional wastewater treatment is usually based on classical control, optimization, monitoring, and prediction technologies that have been refined over time. For instance, as control systems in the majority of WWTPs use SCADA systems for basic monitoring and data gathering (in some WWTPs, in recent years, SCADA systems have been replaced with PLC controllers due to their advantages), PID (Proportional-Integral-Derivative) controllers are used for classical parameter tuning (such as for wastewater pH and dissolved oxygen control). Also, the plant manual, based on human operator experience gained over time, is used, along with standard operating procedures. As optimization tools, statistical methods (for historical data analysis), trial-and-error-based parameter tuning, reactive maintenance (in the case of equipment failure), and mechanical adjustments for energy consumption reduction are typically employed. For monitoring and prediction, traditional methods include laboratory chemical tests (for instance, to evaluate plant effluent quality), manual sampling, trending analysis (for plant performance evaluation), and offline analysis, among others. But all these traditional technologies come with a set of limitations, such as their inability to handle complex and highly nonlinear processes (as the majority of the WWTP processes are), the dependence on the human plant operator know-how, excessive usage of energy and chemical reactants (which leads to supplementary and unnecessary costs), the inability to effectively prevent the occurrence of process disturbances and plant equipment failures, limited real-time optimization resources, etc. From a chemical point of view, according to [PERSON], [PERSON] et al. [21], for the removal of heavy metal ions (Cu(II)) from wastewater, adsorption was used with chemically modified biochar with silicon (Si) and manganese (Mn) (b-BC), obtained by hydrothermal carbonization of corn stalks. Adsorption with chemically modified biochar with Si and Mn (b-BC) is an innovative wastewater treatment method (especially for those wastewaters contaminated with heavy metal ions such as copper, lead, mercury, cadmium, and chromium), combining the used nanotechnology applied to natural materials with a high adsorption efficiency and overall with ecological sustainability and is a good candidate for advanced industrial wastewater treatment applications. The work of [PERSON], [PERSON] et al. [22] presents a wastewater treatment system and includes the designing, testing, and theoretical modelling of a pilot ozonator (based on a special high-frequency electric discharge) capable of destroying pathogenic microorganisms from biologically contaminated surface water, microorganisms that exceed the maximum permissible concentration. The proposed ozonator with high-frequency electric discharge is an advanced and efficient method for industrial wastewater treatment, it being an ideal solution for rigorous WWTP effluent treatment before discharge into the environment or reuse. In the work by [PERSON], [PERSON] et al. [23], an innovative modeling framework was introduced to elucidate nitrate nitrogen migration within heterogeneous vadose zones, addressing key challenges in simulating non-equilibrium pollutant transport at the watershed scale. The proposed model Ensemble improved Stream Tube Model (ESTM), which integrates Pearson Type III distributions for robustly characterizing the heterogeneity of transport parameters (v, D) and includes nitrate degradation and adsorption functions, outperformed traditional models in simulating early breakthrough and tailing. ESTM is an innovative, robust, and adaptable tool for understanding and predicting pollutant fate in heterogeneous subsurface environments, with practical implications for groundwater resource management and pollution control.