A Cross-Domain Optimization Framework for Wastewater Aeration Coupling Transfer Learning and Physics-Informed Constraints

Data-driven aeration optimization is an effective approach for reducing energy consumption in wastewater treatment plants (WWTPs). However, in information-limited scenarios, newly established or emerging-market WWTPs often lack historical labels for aeration actions, making it difficult to construct high-precision surrogate models. Conventional cross-plant model deployments face severe data distribution shifts, and standard multi-objective optimization algorithms are prone to generating non-physical extrapolation errors, such as achieving compliance with “zero aeration” under low-concentration conditions. To break through inter-plant data barriers, this study proposes an intelligent aeration decision-making framework that integrates cross-domain transfer learning with physics-informed constraints. First, this study designs an adversarial network incorporating a state-action decoupling bypass. By employing a gradient reversal layer (GRL) to extract domain-invariant representations while the decoupling bypass preserves the physical sensitivity of control commands, this network achieves robust cross-plant knowledge transfer. Second, this study proposes a physics-informed multi-objective particle swarm optimization (PI-MOPSO) algorithm, which embeds the theoretical oxygen demand as a physical penalty into the fitness function, ensuring the physical reliability of the optimization decisions. Experiments demonstrate that the surrogate model restricts the prediction errors for effluent chemical oxygen demand (COD) and effluent ammonium nitrogen removal rates to within 1%. Validated by statistical tests, the improved algorithm effectively circumvents non-physical prediction biases. Its Pareto front achieves a spacing metric of 0.0027, outperforming baseline algorithms in hypervolume stability. This framework provides reliable aeration scheduling references conforming to biochemical dynamics for target WWTPs lacking historical action labels, offering a promising theoretical foundation for future practical engineering applications.