纤维素酶水解动力学的人工神经网络模型研究

Enzymatic hydrolysis of cellulose was highly complex because of the unclear enzymatic mechanism and many factors that affect the heterogeneous system. Therefore, it is difficult to build a theoretical model to study cellulose hydrolysis by cellulase. Artificial neural network (ANN) was used to simulate and predict this enzymatic reaction and compared with the response surface model (RSM). The independent variables were cellulase amount X(1), substrate concentration X(2), and reaction time X(3), and the response variables were reducing sugar concentration Y(1) and transformation rate of the raw material Y(2). The experimental results showed that ANN was much more suitable for studying the kinetics of the enzymatic hydrolysis than RSM. During the simulation process, relative errors produced by the ANN model were apparently smaller than that by RSM except one and the central experimental points. During the prediction process, values produced by the ANN model were much closer to the experimental values than that produced by RSM. These showed that ANN is a persuasive tool that can be used for studying the kinetics of cellulose hydrolysis catalyzed by cellulase

Synthesis Gas from Pyrolysis of Cedar Sawdust as Biomass Materials: Characterization and Catalytic Performance of Nickel-Based Monolithic Catalyst

A series of monolithic catalysts with different NiO loadings were prepared by supported on the acid treated cordierite. Their specific surface area, pore volume, pore distribution, and catalytic performance in the reforming reaction of biomass pyrolysis gas for synthesis gas were studied. The results show that the specific surface area and pore volume of the cordierite after acid treatment are up to 156 m(2)/g and 0.099 m(3)/g respectively. However, with increasing nickel oxide loading, the specific surface area and pore volume of the catalyst decrease greatly and then tend to steady. The effect of nickel oxide loading on gas composition is quite small, and the total content of H(2) and CO is maintained at 90%. The tar conversion is not affected by the specific surface area of the catalysts. After 6 h catalytic reaction, the structure of the catalysts with 28% NiO does not change, and the quantity of carbon deposition is about 1%. The tar conversion decreases from 87.4% to 81.3%. It is suggested that the nickel-based catalyst has relatively stable activity under high tar concentration conditions, which is attributed to the high dispersion of nickel particles on the support and high stability of the catalyst phase structure