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木质纤维素生物质生产生物乙醇的不同预处理方法和动力学模型:综述

Different pre-treatments and kinetic models for bioethanol production from lignocellulosic biomass: A review.

作者信息

Pendse Dhanashri S, Deshmukh Minal, Pande Ashwini

机构信息

Research Scholar, School of Chemical Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, 411038, India.

School of Petroleum Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, 411038, India.

出版信息

Heliyon. 2023 May 25;9(6):e16604. doi: 10.1016/j.heliyon.2023.e16604. eCollection 2023 Jun.

Abstract

Lignocellulosic biomass is the generally explored substrate to produce bioethanol for environmental sustainability due to its availability in abundance. However, the complex network of cellulose-hemicellulose-lignin present in it makes its hydrolysis as a challenging task. To boost the effectiveness of conversion, biomass is pre-treated before enzymatic hydrolysis to alter or destroy its original composition. Enzymes like Cellulases are widely used for breaking down cellulose into fermentable sugars. Enzymatic hydrolysis is a complex process involving many influencing factors such as pH, temperature, substrate concentration. This review presents major four pre-treatment methods used for hydrolysing different substrates under varied reaction conditions along with their mechanism and limitations. A relative comparison of data analysis for most widely studied 10 kinetic models is briefly explained in terms of substrates used to get the brief insight about hydrolysis rates. The summary of pre-treatment methods and hydrolysis rates including cellulase enzyme kinetics will be the value addition for upcoming researchers for optimising the hydrolysis process.

摘要

木质纤维素生物质因其丰富的可获得性,是为实现环境可持续性而普遍探索的用于生产生物乙醇的底物。然而,其中存在的纤维素 - 半纤维素 - 木质素复杂网络使其水解成为一项具有挑战性的任务。为提高转化效率,在酶水解之前对生物质进行预处理,以改变或破坏其原始组成。像纤维素酶这样的酶被广泛用于将纤维素分解为可发酵糖。酶水解是一个复杂的过程,涉及许多影响因素,如pH值、温度、底物浓度。本综述介绍了在不同反应条件下用于水解不同底物的主要四种预处理方法及其作用机制和局限性。简要解释了针对最广泛研究的10种动力学模型的数据分析的相对比较,涉及所使用的底物,以便对水解速率有简要了解。预处理方法和水解速率的总结,包括纤维素酶动力学,将为未来的研究人员优化水解过程提供有价值的参考。

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