纤维素酶水解动力学的人工神经网络模型研究

Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks

酶作用机制的模糊以及影响异相体系因素的大量存在,使得纤维素水解的酶催化过程高度复杂,很难为之建立理论模型.采用非理论模型人工神经网络模拟和预测了纤维素酶水解反应,并与常用的响应面模型进行了比较.选取加酶量X1,底物浓度X2和反应时间X3作为自变量,还原糖浓度Y1和原料转化率Y2作为响应值.结果表明,人工神经网络模型比响应面模型更适合作为研究纤维素酶水解的动力学工具.在模拟过程中,除中心试验点外,只有1个试验点上人工神经网络模拟值Y2产生的误差大于响应面模型.在预测过程中,人工神经网络模型的预测值都比响应面模型更接近实验值.

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 inde-pendent variables were cellulase amount X1,substrate concentration X2,and reaction time X3,and the response variables were reducing sugar concentration Y1 and transformation rate of the raw material Y2.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.