Multi-objective modeling of boiler combustion based on feature fusion and Bayesian optimization

摘要

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.

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