The aeration system of municipal sewage treatment equipment is the core unit ensuring the stable operation of the biological treatment process. Its precise oxygen supply control directly affects the effluent quality compliance rate and operational energy consumption. Traditional aeration control relies heavily on manual experience or fixed parameter adjustments, making it difficult to cope with complex operating conditions such as fluctuations in influent water quality and changes in microbial activity. This can easily lead to insufficient oxygen supply causing sludge bulking or excessive oxygen supply resulting in energy waste. Modern municipal sewage treatment equipment integrates multiple technologies to construct a closed-loop control system of "sensing-decision-execution," achieving dynamic matching between oxygen supply and microbial needs.
Real-time water quality monitoring is the "sensing layer" for precise oxygen supply. Municipal sewage has a complex composition; the mixing of industrial wastewater and domestic sewage causes real-time changes in indicators such as COD, ammonia nitrogen, and pH. These parameters directly affect the oxygen consumption rate of microorganisms. Modern aeration systems deploy a high-density sensor network at the influent end and in the aerobic zone to collect key indicators such as flow rate, dissolved oxygen (DO), and oxidation-reduction potential (ORP) in real time, and transmit the data to the central control system via IoT technology. For example, when a sudden increase in ammonia nitrogen concentration is detected, the system can predict the increased oxygen demand of the nitrification reaction and adjust the oxygen supply strategy in advance to avoid ammonia nitrogen exceeding the standard due to reaction lag.
Dynamic model calculations provide a "decision-making basis" for precise oxygen supply. Based on activated sludge mathematical models (ASMs), the system inputs real-time water quality data and process parameters (such as sludge concentration and sludge age) into the simulation platform. Through mass conservation and kinetic equations, it quantifies the oxygen demand of microorganisms under different operating conditions. For example, in the AAO process, the model can distinguish the differences in oxygen demand between the aerobic nitrification reaction and the anoxic denitrification reaction, setting differentiated DO target values for different regions to avoid process imbalance caused by a "one-size-fits-all" approach to oxygen supply. Simultaneously, the model can also simulate the impact of environmental factors such as temperature and pH on microbial activity, further optimizing oxygen supply parameters.
Intelligent control algorithms are the "core engine" for achieving precise regulation. Traditional PID control struggles to handle complex operating conditions with multiple coupled variables. Machine learning-based intelligent algorithms, however, can simultaneously integrate dozens of parameters, including water quality, water quantity, and equipment status, and optimize control strategies through training with massive amounts of data. For example, after applying reinforcement learning algorithms, a municipal wastewater treatment plant's system can autonomously identify the optimal aeration intensity during low-load nighttime periods and automatically switch to energy-saving mode, reducing aeration energy consumption by approximately 20% while ensuring effluent meets standards. Furthermore, the algorithm can predict faults, proactively detecting anomalies such as sensor drift and valve jamming, preventing control distortion.
Zoning control technology addresses the drawbacks of traditional "average distribution" aeration. Aerobic tanks in municipal sewage treatment equipment are typically divided into multiple independent zones, each requiring differentiated oxygen supply due to variations in microbial communities and pollutant concentrations. Precision aeration systems dynamically adjust air distribution across zones by installing electric valves and flow meters on each branch pipe, combined with a network hydraulic model. For example, when the dissolved oxygen (DO) level is high in the terminal area due to returned sludge carrying dissolved oxygen, the system automatically reduces the opening of the branch valves in that area while increasing the oxygen supply in the upstream area to ensure that the DO level in all areas meets the standard, avoiding localized insufficient or excessive oxygen supply.
Equipment collaborative optimization is the physical basis for precise oxygen supply. As the core oxygen supply equipment, the blower needs to have a wide airflow adjustment range and rapid response capability to match load fluctuations; electric valves need to use high-precision diamond valves or ball valves to avoid control distortion caused by the nonlinear relationship between opening and airflow in butterfly valves; sensors need to have high stability and low maintenance requirements to reduce the impact of data distortion on control accuracy. For example, after a plant adopted magnetic levitation centrifugal blowers, its adjustment range reached 40%-100%, and the response time was shortened to within 2 seconds, providing hardware support for precise oxygen supply.
Remote operation and maintenance platforms improve the intelligence level of system management. Through IoT technology, the operating data of the aeration system (such as blower vibration, valve opening, and DO curves) is uploaded to the cloud platform in real time. Maintenance personnel can remotely monitor equipment status and promptly handle abnormal alarms. Simultaneously, the platform can generate energy consumption analysis reports and optimization suggestions to help managers adjust operational strategies. For example, one platform, through analyzing historical data, discovered that oxygen supply consistently exceeded demand during a certain period. It then suggested adjusting feedforward control parameters, ultimately achieving annual energy savings of approximately 150,000 kWh.
From real-time monitoring to intelligent decision-making, from zoned regulation to equipment collaboration, municipal sewage treatment equipment's aeration systems are evolving towards "precision, intelligence, and low carbon emissions" through technological innovation. This transformation not only improves sewage treatment efficiency and stability but also provides key technological support for energy conservation and emission reduction in the industry, helping municipal sewage treatment equipment move towards a green and sustainable direction.
