The physical field (temperature, gas concentration, etc.) inside the furnace is closely related to the boiler combustion optimization. A novel multi-objective prediction framework based on feature fusion is proposed to provide the basis for the online combustion optimization of coal-fired boilers. Firstly, the physical field information is obtained through the CFD, which presented a strong correlation between thermal efficiency and NOx generation. Then the eXtreme Gradient Boosting and Bayesian Optimization are used to construct the model according to the changes of the real-time physical field and operating conditions. The modeling results demonstrated that the prediction accuracy of thermal efficiency from the model with the fusion information can be improved by 1.49% compared with the model using the operational data. The prediction accuracy of thermal efficiency and NOx generation is improved by 2.57% and 0.13%, respectively, which indicated that the expression ability of the model improved by combing the typical real-time physical field information.

炉膛内的物理场（温度、气体浓度等）与锅炉燃烧优化密切相关。提出了一种新的基于特征融合的多目标预测框架，为燃煤锅炉在线燃烧优化提供依据。首先，通过CFD获得物理场信息，表明热效率与NOx生成之间存在很强的相关性。然后根据实时物理场和运行条件的变化，采用eXtreme Gradient Boosting和贝叶斯优化构建模型。建模结果表明，与运行数据模型相比，融合信息模型对热效率的预测精度可提高1.49%。