内循环流化床生物质气化过程的神经网络模型

BP NEURAL NETWORK SIMULATION OF BIOMASS GASIFICATION IN AN INNER CIRCULATING FLUIDIZED BED

基于BP人工神经网络原理,利用MATLAB神经网络工具箱,以实验得到的57组气化实验数据作为样本,建立了一个以加料量和送风量为输入变量,以燃气热值、产气率、碳转化率和气化效率为输出变量,用于描述连续稳定气化过程的内循环流化床生物质气化模型。对模型的隐层节点数和训练周期改变对模拟结果的影响进行了分析,发现当隐层节点数为20,训练步骤为50步,模型的4个输出变量的模拟结果与实验结果相关系数均超过0.95;同时对该模型的预测能力进行了考察,模型预测结果与实验结果吻合良好,证明了该模型具有较强的泛化能力,为生物质内循环流化床气化系统的优化设计和自动控制提供新思路。

A three layers back propagation （BP） Neural Network model was built to simulate the biomass gasification process in an inner circulating fluidized bed. Two input variables, i.e. feeding rates, air flow rate, and four output variables, i.e. gas heating value, gas productivity, carbon conversion rate, and gasification efficiency were selected. 57 experimental data were taken as training and checking samples, the effects of nodes of hidden layer and training echoes on simulation results were investigated. The results showed that correlation coefficient of the four output variables between simulation results and experimental data exceeded 0.95, when the nodes of hidden layer were 20 and training echoes was 50. Model-predicted results were in agreement with the experimental data, showing good generalization capacity. This model will be the basis of automatic control of biomass gasification process in fluidized bed.