# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 5 Results
### Integration of IoT and ML for Real-Time Predictions  
Significant progress has been made in environmental management using AI, especially deep neural networks that model the complex, time-varying relationships in water treatment systems [96].  
Real-time monitoring and optimization of treatment plants is made possible by digital twins, which are another breakthrough. A successful instance was developed for a facility in Eindhoven, Netherlands [7].  
Combining IoT sensors with machine learning allows for real-time predictions of water quality. In aquaculture, some of the latest deep learning models (transformer models) use IoT data to rapidly respond to environmental changes. Meanwhile, wastewater treatment plants use IoT along with LSTM models to better manage their facilities, conserving energy and cutting costs by predicting the chemical oxygen demand in real-time [98]. The combination of AI with self-powered IoT sensor systems is a leap forward for continuous monitoring in wastewater treatment plants, making autonomous and no-power data capture a reality [106].  
The incorporation of the Internet of Things and machine learning into plant control systems vastly enhances real-time monitoring and the kind of predictive maintenance that lets operators make adjustments before things go wrong, thereby reducing the amount of manual intervention required [12].  
Digital twin platforms in water treatment plants show how IoT and machine learning are coming together using real-time data and predictive analytics to enhance operational efficiency and reduce maintenance costs. At some water utilities, where the digital twin is more developed, it is used for predicting maintenance needs [34].  
Real-time predictions in an IoT environment where resources are constrained are fundamentally an edge computing problem. These environments require solutions that minimize hold-up times, save energy, and guarantee that predictions happen in real time, even when the network is down and all that is available are ultralow-power, ultralow-performance Edge devices [34]. Decision-making related to the selection of Cloud vs. local infrastructure has strong implications on the performance of IoT-supported systems, with a recent study showing trade-offs between computational power and real-time responsiveness on automated environmental monitoring [85].  
Edge computing boosts the efficiency of the processing of that data, allowing for much more timely and often much more accurate predictions to be made by machine learning algorithms. This matters a great deal in various applications (like water quality monitoring and wastewater treatment) where all of that class of algorithm really need to work in an efficient, environment-responsive, and often in a real-time or near-real-time way, to do its job. Edge computing also boosts IoT security, so it is a very large bonus for the sensitive data involved in water treatment. Timely access to all that data and timely access to all the alarms that systems like clarifier alarms generate are critical for maintaining the efficiency of the process. Edge computing integrates well with IoT and offers the promise of even better operational efficiency [4; 34; 43; 65].  
Water management is undergoing a transformation thanks to digital twins. These virtual copies of real-world systems update in real time and are starting to be used in the water supply sector. Knowing both the predicted problem location and the time at which it will happen allows network managers to solve problems before they become deep crises. At the same time, model use is allowing those in the water supply sector to trim costs and extend the lives of the networks and the facilities.  
Digital twins have real power because they can continuously monitor everything and predict when equipment might fail. This allows maintenance teams to fix things before they shut down or cause problems with water quality [53; 107].