基于信息熵的生物质气化炉预测

Biomass Gasification Furnace Prediction Based on Information Entropy

针对生物质气化过程的复杂特性,提出一种基于信息熵的生物质气化炉温度预测方法。首先,该模型利用灰色过程神经网络模型及预测模型对生物质气化炉的温度分别进行预测,通过使用信息熵法确定预测子模型的加权系数;然后把两个子模型进行加权集成,从而得到更加准确的炉温预测模型,确保了生物质气化炉温度的稳定控制。仿真效果表明了该方法的有效性。

Aiming at the complex characteristics of biomass gasification process,proposes a prediction model of biomass air gasification furnace temperature based on information entropy,the model first by using the grey forecast model and the process neural network model to predict the temperature respectively net biomass gasification furnace,then using information entropy to determine the weight coefficient of each prediction model,the two sub models are weighted integration,obtain more models predict biomass gasification furnace temperature accurately,to ensure a stable temperature control of biomass gasification furnace. The simulation results show the effectiveness of the proposed method.