Study on neural network-based prediction model for biomass ash softening temperature
The softening temperature of biomass ash serves as an indicator of boiler slagging and heat transfer efficiency.Therefore,establishing a predictive model for the softening temperature of biomass ash can contribute to a better understanding of the slagging characteristics in biomass boilers.This paper presents the development of a prediction model for biomass ash softening temperature,leveraging the Back Propagation neural network and Elman neural network.Firstly,the original dataset comprised the mass fraction of oxide in biomass ash and its corresponding softening temperature.This dataset was utilized to establish the training parameters for the model.Subsequently,the model underwent rigorous testing.Finally,an evaluation of the model's adaptability was conducted,wherein the determination coefficient(R2),Root Mean Square Error(ERMS),Mean Absolute Error(EMA),and Mean Absolute Percentage Error(EMAP)of both the BP neural network and the Elman neural network were compared and analyzed.Subsequently,the Elman network prediction model was validated using laboratory-obtained poplar ash samples.The results indicate that for the test set data,the R2 values of the softening temperature prediction models for biomass ash using both the BP neural network and Elman neural network are 0.998 87 and 0.999 96,respectively.Additionally,the Elman neural network model exhibits smaller values for ERMS,EMA and EMAP.The Elman neural network demonstrates superior predictive capabilities for the softening temperature of biomass ash.Specifically,at residence times of 2 h and ash formation temperatures of 600 ℃,800 ℃,and 1 000℃,the relative error of the Elman neural network in predicting the softening temperature of poplar ash remains below 20％,indicating a robust predictive performance.This study delves into the correlation between seven components of biomass ash and softening temperature,providing valuable insights for establishing prediction models and achieving accurate predictions of the softening temperature of biomass ash.
万方数据
维普
