Abstract
The purpose of the research in this article is to improve the operation of urban water treatment plants using biological purification using neural network technologies. Before controlling aeration in the aeration tank, the neural network processes the volume of incoming wastewater (flow and level sensors measure the volume of incoming wastewater, flow rate and its dynamics. Dissolved oxygen sensors monitor the oxygen level in the aeration tank, which is critical for the work of microorganisms, for example, if an increase in water volume is predicted, the system increases the oxygen supply in advance. The constant collection of data from sensors allows you to compare the actual parameters with the predicted ones, correcting the operation of the system in real time. The integration of predictive analytics with aerotank control systems allows for a synergistic effect: 1 – Forecasting the parameters of incoming wastewater provides an opportunity to adjust the system operation in advance, 2 – Adaptive control of aeration and activated sludge reduces energy consumption and increases the efficiency of biological treatment. Continuous monitoring and feedback will allow the system to dynamically respond to changes, ensuring process stability and preventing emergencies. Thus, neural network algorithms, combined with SCADA systems, form the basis for intelligent automation of wastewater treatment plants, which contributes not only to improving the quality of wastewater treatment, but also to significant savings in energy resources, reducing operating costs and improving environmental safety.