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

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	Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks
其他题名	纤维素酶水解动力学的人工神经网络模型研究
	Zhang Yu1,2 ; Xu Jingliang1 ; Yuan Zhenhong1 ; Zhuang Xinshu1 ; Lue Pengmei 1
	2009-04-01
发表期刊	CHINESE JOURNAL OF CATALYSIS
ISSN	0253-9837
卷号	30 期号:4 页码:355-358
通讯作者	yuanzh@ms.giec.ac.cn
摘要	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.
文章类型	Article
其他摘要	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.
关键词	Enzymatic Kinetics Enzymatic Hydrolysis Of Cellulose Artificial Neural Network Response Surface Model Heterogeneous Catalysis
WOS标题词	Science & Technology ; Physical Sciences ; Technology
研究领域[WOS]	Chemistry ; Engineering
关键词[WOS]	HIGH-THROUGHPUT ; OPTIMIZATION
收录类别	SCI
语种	英语
项目资助者	国家高技术研究发展计划(863计划, 2007AA100702-4 和 2007AA05Z406);中国科学院知识创新工程重大项目(KSCX1-YW-11-A3)和重要方向项目(KSCX2-YW-G-063-1)
WOS类目	Chemistry, Applied ; Chemistry, Physical ; Engineering, Chemical
WOS记录号	WOS:000267277100015
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.giec.ac.cn/handle/344007/3334
专题	中国科学院广州能源研究所
作者单位	1.Chinese Acad Sci, Key Lab Renewable Energy & Gas Hydrate, Guangzhou Inst Energy Convers, Guangzhou 510640, Guangdong, Peoples R China 2.Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Zhang Yu,Xu Jingliang,Yuan Zhenhong,et al. Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks[J]. CHINESE JOURNAL OF CATALYSIS,2009,30(4):355-358.
APA	Zhang Yu,Xu Jingliang,Yuan Zhenhong,Zhuang Xinshu,&Lue Pengmei.(2009).Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks.CHINESE JOURNAL OF CATALYSIS,30(4),355-358.
MLA	Zhang Yu,et al."Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks".CHINESE JOURNAL OF CATALYSIS 30.4(2009):355-358.

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