内循环流化床生物质气化过程的神经网络模型

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。

Upgrading of liquid fuel from the vacuum pyrolysis of biomass over the Mo-Ni/gamma-Al2O3 catalysts

High amounts of acid compounds in bio-oil not only lead to the deleterious properties such as corrosiveness and high acidity, but also set up many obstacles to its wide applications. By hydrotreating the bio-oil under mild conditions, some carboxylic acid compounds could be converted to alcohols which would esterify with the unconverted acids in the bio-oil to produce esters. The properties of the bio-oil could be improved by this method. In the paper, the raw bio-oil was produced by vacuum pyrolysis of pine sawdust. The optimal production conditions were investigated. A series of nickel-based catalysts were prepared. Their catalytic activities were evaluated by upgrading of model compound (glacial acetic acid). Results showed that the reduced Mo-10Ni/gamma-Al2O3 catalyst had the highest activity with the acetic acid conversion of 33.2%. Upgrading of the raw bio-oil was investigated over reduced Mo-10Ni/gamma-Al2O3 catalyst. After the upgrading process, the pH value of the bio-oil increased from 2.16 to 2.84. The water content increased from 46.2 wt.% to 58.99 wt.%. The H element content in the bio-oil increased from 6.61 wt.% to 6.93 wt.%. The dynamic viscosity decreased a little. The results of GC-MS spectrometry analysis showed that the ester compounds in the upgraded bio-oil increased by 3 times. it is possible to improve the properties of bio-oil by hydrotreating and esterifying carboxyl group compounds in the bio-oil.