In municipal sewage treatment equipment, the intelligent control system achieves precise matching of aeration volume and dissolved oxygen concentration through multi-level technology integration and dynamic control strategies. Its core lies in constructing a closed-loop control architecture of "sensing-decision-execution-feedback." This process relies on a high-precision sensor network, intelligent algorithm models, automated actuators, and real-time feedback mechanisms to dynamically optimize the biochemical reaction process of sewage treatment, ensuring that the dissolved oxygen concentration remains within the optimal range for microbial metabolism while avoiding energy waste caused by over-aeration.
The sensing layer is the foundation of intelligent control. It collects key parameters such as dissolved oxygen concentration, influent flow rate, COD, and ammonia nitrogen in real time through devices such as dissolved oxygen sensors, flow sensors, and water quality sensors deployed within the biological treatment tank. These sensors need to have high sensitivity and anti-interference capabilities; for example, the dissolved oxygen sensor needs to be accurate to ±0.2 mg/L to capture minute fluctuations; the water quality sensor needs to cover indicators such as organic matter, nitrogen, and phosphorus to provide multi-dimensional data support for the algorithm model. The sensor network transmits data to the control center via wired or wireless means, forming a "digital profile" of the sewage treatment process.
The core of the decision-making layer is the intelligent algorithm model, which combines a mechanistic model and a data-driven model to achieve dynamic prediction and optimization of aeration volume. The mechanistic model, based on the principles of the activated sludge process, simulates the microbial metabolic process through an ASM (Activated Sludge Model) to establish a quantitative relationship between dissolved oxygen concentration, aeration volume, and influent water quality. The data-driven model utilizes deep learning algorithms such as LSTM (Long Short-Term Memory) networks to uncover hidden patterns in historical data and compensate for deviations in the mechanistic model under complex operating conditions. For example, when the influent COD concentration suddenly increases, the model can predict changes in microbial oxygen demand in advance and generate the optimal aeration strategy.
The execution layer translates decision commands into physical operations through automated equipment, mainly including variable frequency blowers, intelligent aeration valves, and electric regulating valves. The variable frequency blower dynamically adjusts its speed to change the air supply based on the total aeration volume demand output by the algorithm, with a response time controlled within seconds to match dissolved oxygen fluctuations. The intelligent aeration valve integrates pressure sensors and AI chips, adjusting its opening in real time according to the branch pipe flow demand to ensure uniform distribution of dissolved oxygen in each area. The actuators must possess high precision and reliability. For example, the flow control accuracy of electric regulating valves must reach ±5% to avoid control failure due to execution deviations.
The feedback layer continuously corrects the control strategy to cope with dynamic operating conditions through real-time monitoring and closed-loop adjustment. The dissolved oxygen sensor feeds back the deviation between the actual concentration and the set value to the control center. The algorithm model adjusts the aeration rate based on the direction and magnitude of the deviation. For example, if the dissolved oxygen concentration is lower than the set value, the system prioritizes increasing the blower speed; if the deviation persists, it further adjusts the aeration valve opening or starts the standby blower. Furthermore, the system must consider the impact of environmental factors such as temperature and pH on dissolved oxygen saturation, ensuring control accuracy through compensation algorithms.
The advantages of intelligent control systems lie in their adaptive capabilities and global optimization characteristics. Traditional control methods often use fixed PID parameters, making it difficult to cope with dynamic changes such as fluctuations in influent water quality and equipment aging. Intelligent systems, through online learning mechanisms, can periodically update model parameters to adapt to seasonal changes or process adjustments. For example, when rising water temperatures in summer lead to increased microbial activity, the system can automatically lower the dissolved oxygen setpoint to save energy; in winter, it adjusts in the opposite direction to maintain reaction efficiency. Furthermore, the system can construct virtual aeration tanks using digital twin technology to simulate control effects under different operating conditions, providing optimized solutions for actual operation.
From an application perspective, the intelligent control system for municipal sewage treatment equipment can significantly improve sewage treatment efficiency and resource utilization. By dynamically and precisely matching aeration volume and dissolved oxygen concentration, the system can avoid energy waste caused by over-aeration, while preventing effluent quality exceeding standards due to insufficient aeration. In addition, intelligent control can extend equipment lifespan and reduce mechanical wear caused by frequent start-ups, shutdowns, or sudden load changes. With the deep integration of technologies such as the Internet of Things, big data, and artificial intelligence, future intelligent control systems will develop towards more advanced autonomous decision-making, such as achieving self-evolution of control strategies through reinforcement learning, or coupling with photovoltaic power generation to build "zero-carbon sewage treatment plants," providing technical support for the sustainable development of the municipal sewage treatment industry.
