Online learning-empowered smart management for A2O process in sewage treatment processes

Using artificial intelligence method to describe general working process is a more meaningful and widely used idea in various practical projects. At the same time, it is also an important way to realize intelligent management. Water pollution is serious all over the world, also the intelligent management of sewage treatment has always been one of the urgent problems to be solved. For this, an intelligent management system is designed in this study to realize automatic monitoring and intelligent decision-making of sewage treatment. However, the existing technology usually trains artificial intelligence models based on historical data, and such models have some limitations in describing nonlinear and complex wastewater treatment processes. Offline machine learning lacks dynamic adaptive characteristics to scene changes. Considering this, this paper designs an online learning empowered smart management for A2/O process in sewage treatment processes (OL-AP). Online learning is based on the new data generated by the scene transformation, so that the model can learn again and give better results. In this study, relevant simulation experiments are carried out on the sewage treatment data of a sewage treatment plant in Chongqing. Firstly, automatic data collection is realized based on the sensor network of the IoT. Then, according to the preprocessed data, the designed prediction model is trained and a set of parameters with better evaluation indexes is obtained. Finally, online learning uses the latest data samples based on the online feedback of real scenes to optimize the model by retraining and adjusting parameters.

Automated summary

Describing working processes using artificial intelligence is a meaningful and widely used idea in various projects. This study designs an intelligent management system for sewage treatment, which integrates automatic monitoring and intelligent decision-making. The introduction of online learning enhances the adaptive nature of the model and improves the effectiveness of wastewater treatment processes.