多尺度特征融合强化神经网络的生物质循环流化床锅炉燃烧状态多目标预测模型OA

[Objectives]Most of the current prediction models for the combustion state of power station boilers can predict only a single objective.Due to different types of objectives,their data exhibit various temporal distribution characteristics.Besides,the simple long short-term memory(LSTM)neural network has limitations in predicting multiple different types of objectives.Given the above challenges,a multi-scale feature fusion enhanced LSTM neural network model is proposed.[Methods]First,a multi-scale LSTM neural network model is developed and compared with the conventional LSTM neural network model.Then,on the basis of this multi-scale model,two optimization modules,including the spectral attention mechanism and the self-modulation feature fusion module,are gradually added to develop the multi-scale feature fusion enhanced LSTM neural network model,thereby improving the ability of the model to identify,enhance,and effectively integrate data variation features.Finally,the proposed model is evaluated on the same dataset.[Results]Compared with the simple LSTM neural network model,the multi-scale model achieves a significant improvement in accuracy,verifying the effectiveness of the model in capturing temporal characteristics of different types of prediction objectives.Moreover,with the increasing number of optimization modules,the model performance is further enhanced,indicating that the addition of modules strengthens the ability of the model to process temporal characteristics.[Conclusions]The proposed model effectively addresses the problem of insufficient prediction accuracy in boiler multi-objective prediction caused by different temporal distribution characteristics of objective types,resulting in more accurate predictions and providing significant value for predicting multiple types of objectives.