A sewage treatment global optimization control method based on multi-agent collaborative game
By modeling the wastewater treatment system as a multi-agent collaborative control system, and using the Dec-POMDP framework and reinforcement learning mechanism, the problems of water quality fluctuation and energy waste in wastewater treatment are solved, and global optimal control and resource optimization are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUHAN ZHENGYUAN AUTOMOTIVE INSTR ENG CO LTD
- Filing Date
- 2026-05-28
- Publication Date
- 2026-07-17
AI Technical Summary
Existing wastewater treatment control methods lack cross-stage information exchange and collaborative optimization mechanisms, leading to increased water quality fluctuations, increased energy consumption, and waste of chemicals. It is difficult to simultaneously ensure water quality compliance, minimum energy consumption, and minimum chemical consumption in multi-objective optimization scenarios.
The key control links of wastewater treatment are modeled as five independent heterogeneous intelligent agents. A distributed Dec-POMDP optimization framework is adopted, and global collaborative control is achieved through reinforcement learning and collaborative game mechanism. The PPO algorithm and consensus negotiation mechanism are combined to solve the problems of goal conflict and action incoordination among intelligent agents. The Lagrange multiplier method and attention mechanism are used to optimize the reward distribution.
The system achieves optimal global operation of the wastewater treatment system, saves 10% to 15% on energy in the aeration system, saves 15% to 20% on carbon source chemical consumption, ensures stable and compliant effluent quality, and improves system robustness and safety.
Smart Images
Figure CN122411331A_ABST