CAJ | 학술논문

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。
침대생물질기화과정적복잡특성，제출일충기우신식적적생물질기화로온도예측방법。수선，해모형이용회색과정신경망락모형급예측모형대생물질기화로적온도분별진행예측，통과사용신식적법학정예측자모형적가권계수；연후파량개자모형진행가권집성，종이득도경가준학적로온예측모형，학보료생물질기화로온도적은정공제。방진효과표명료해방법적유효성。
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 informa-tion 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.