Smart Wastewater Plant 2.0:
IoT, AI, Digital Twin, and Robotics for Autonomous Sewage Treatment and Energy Efficiency
1. IoT and Sensing Technology
Deploy various intelligent sensors (e.g., pH, DO, ORP, MLSS, ammonia nitrogen, nitrate sensors) and smart meters to form the "nerve endings" of the water plant, responsible for collecting end-to-end process data.
2. Big Data and Cloud Platform
Aggregate all data into a unified cloud platform or data middleware for storage, management, and analysis. This serves as the water plant’s "data brain," processing massive, multi-source, and heterogeneous data.
3. Artificial Intelligence and Smart Algorithms
This is the core of intelligence. Leverage machine learning (ML), deep learning (DL), and other algorithms to build predictive and optimization models.
Application Examples:
Effluent Water Quality Prediction: Forecast whether the treated water meets compliance standards based on influent data and current operational conditions.
Aeration System Optimization: Intelligently control blower airflow to ensure biochemical treatment efficiency while maximizing energy savings (aeration is the largest energy-consuming unit in a water plant).
Chemical Dosing Optimization: Precisely predict the required dosages of coagulants, carbon sources, etc., to avoid waste.
Equipment Failure Prediction: Analyze vibration, temperature, and current data to provide early warnings for potential equipment malfunctions.
4. Digital Twin
Create a fully corresponding digital model of the physical water plant in a virtual space. This model real-time mirrors the operational status of the physical plant, allowing engineers to conduct simulations, scenario analyses, and optimizations in the digital world. The optimal strategies are then deployed to the physical plant for execution, achieving "virtual-physical interaction." This represents the most advanced form of intelligence.
5. Robotics and Automation
Deploy inspection robots, underwater dredging robots, drones, and other automated systems to replace manual labor in hazardous, tedious, and repetitive tasks, enhancing safety and efficiency.
