[Objective] Multi-objective optimization of coal-fired boiler combustion is a critical strategy to achieve high-efficiency and environmentally friendly boiler operation. However, due to the strong nonlinearity, multivariable coupling, and inherent conflict between boiler efficiency and NO x emission, it is particularly challenging to identify optimal operating parameters using traditional trial -and-error approaches. 

[Methods] This study systematically builds prediction models for boiler exhaust flue gas temperature, carbon content in fly ash and NOx emission mass concentration based on the XGBoost model. Subsequently, the various heat losses are calculated, and a boiler efficiency model is further constructed using the anti -balance method. The g lobal optimization of XGBoost hyperparameters is performed using Bayesian optimization (BO) . To enable a comparative analysis, the corresponding BO-LSSVM and BO -BP models are also established . Based on the traditional NSGA -Ⅱ, the hypercube Latin sampling strategy and NDX crossover operator are introduced to improve the algorithm. 2 Collaborative optimization is carried out aiming at both enhancing boiler efficiency and lowering NO x emission mass concentration. The optimization decision variables include the furnace oxygen content, primary air pressure, secondary air damper openings and over -fire air damper openings . 

[Results] The results demonstrate that BO-XGBoost achieves a lower mean absolute error (EMAE) and a higher coefficient of determination (R²) on the test set compared to the other two models, which reveal that the BO-XGBoost model yields superior prediction accuracy and generalization performance. Relative to the traditional NSGA-Ⅱ, the improved NSGA-Ⅱ yields superior initial populations, reinforces global exploration capability, and effectively avoids the problem of local optima. As a result, the quality of Pareto solutions is significantly improved . The improved NSGA -II algorithm can generate many operating schemes superior to the original working conditions. The TOPSIS decision-making is adopted to select a trade-off solution from the Pareto solutions that balances boiler efficiency and NO x emission mass concentration. Statistical analysis over 200 operating conditions shows that the average boiler efficiencies for the original condition, the NSGA‑II and improved NSGA‑II trade-off solutions are 94.036 1%, 94.356 9%, and 94.506 8%, respectively, with the corresponding average NOx emission mass concentration of 173.13, 150.16, and 139.32 mg/m3. Accordingly, the trade-off solution obtained by the TOPSIS decision-making method successfully realizes the dual objectives of improving boiler efficiency and reducing NO x emission mass concentration. 

[Conclusion] This study establishes an integrated “prediction-optimization-decision” framework, which effectively combines BO-XGBoost prediction, improved NSGA -II optimization, and TOPSIS decision -making to achieve the collaborative optimization of complex combustion processes in coal -fired boilers. Moreover, t he proposed framework provides a systematic and practical solution for multi -objective optimization of boiler combustion systems.