Modeling biochemical conversion of lignocellulosic materials for sugar production: A review
Modeling biochemical conversion of lignocellulosic materials for sugar production: A review
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DOI：
10.15376/biores.6.4.5282-5306
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发表时间：
2011-10
期刊：
影响因子：
1.5
通讯作者：
Ziyu Wang;Jiele Xu;Jay J. Cheng
中科院分区：
文献类型：
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作者：
Ziyu Wang;Jiele Xu;Jay J. Cheng
为了深入了解影响木质纤维素生物质转化为可发酵糖的因素，应将实验结果与过程模拟结合起来。本文的目的是回顾已发表的关于使用稀酸、碱和蒸汽爆炸预处理等领先技术的预处理过程建模的研究，以及将木质纤维素转化为糖的酶水解过程。最常用的预处理模型是动力学模型，假设反应速率对生物质成分的一阶依赖性以及速率常数和温度之间的阿伦尼乌斯型相关性。鉴于预处理中涉及的反应的异质性，严重性因子、人工神经网络和模糊推理系统的使用提供了预测系统行为的替代方法。纤维素生物质的酶水解动力学已经使用各种建模方法进行了模拟，其中基于Langmuir型吸附机制开发的模型和结合适当限速因素的改进的Michaelis-Menten模型最有潜力。在模拟水解过程时需要考虑的因素包括底物反应性、酶活性和可及性、酶与木质素的不可逆结合以及高转化水平下的酶失活。未来的研究前景应侧重于彻底了解生物质反应物和化学品/酶之间的相互作用——这是为整个转化过程开发复杂模型的关键。
To deeply understand the factors that affect the conversion of lignocellulosic biomass to fermentable sugars, experimental results should be bridged with process simulations. The objective of this paper is to review published research on modeling of the pretreatment process using leading technologies such as dilute acid, alkaline, and steam explosion pretreatment, as well as the enzymatic hydrolysis process for converting lignocellulose to sugars. The most commonly developed models for the pretreatment are kinetic models with assumptions of a first-order dependence of reaction rate on biomass components and an Arrhenius-type correlation between rate constant and temperature. In view of the heterogeneous nature of the reactions involved in the pretreatment, the uses of severity factor, artificial neural network, and fuzzy inference systems present alternative approaches for predicting the behavior of the systems. Kinetics of the enzymatic hydrolysis of cellulosic biomass has been simulated using various modeling approaches, among which the models developed based on Langmuir-type adsorption mechanism and the modified Michaelis-Menten models that incorporate appropriate rate-limiting factors have the most potential. Factors including substrate reactivity, enzyme activity and accessibility, irreversible binding of enzymes to lignin, and enzyme deactivation at high conversion levels, need to be considered in modeling the hydrolysis process. Future prospects for research should focus on thorough understanding of the interactions between biomass reactants and chemicals/enzymes — the key to developing sophisticated models for the entire conversion process.