A Cross-Domain Multi-Objective Optimization Framework for Wastewater Aeration Systems 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, newly established or emerging-market WWTPs often lack historical aeration logs, 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 based on air-to-water ratio and removal rate features. By employing a gradient reversal layer (GRL) to extract domain-invariant representations, this network achieves 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 total nitrogen (TN) 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 optimal aeration scheduling strategies conforming to biochemical dynamics for target WWTPs lacking aeration action labels, demonstrating substantial practical engineering value.