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逆流糖化的动力学建模

Kinetic modeling of countercurrent saccharification.

作者信息

Liang Chao, Gu Chao, Karim M Nazmul, Holtzapple Mark

机构信息

1Department of Chemical Engineering, Texas A&M University, College Station, TX 77843-3122 USA.

2Texas A&M Institute for Neuroscience, Texas A&M University, College Station, TX 77843-3122 USA.

出版信息

Biotechnol Biofuels. 2019 Jul 11;12:179. doi: 10.1186/s13068-019-1517-5. eCollection 2019.

Abstract

BACKGROUND

Countercurrent saccharification is a promising way to minimize enzyme loading while obtaining high conversions and product concentrations. However, in countercurrent saccharification experiments, 3-4 months are usually required to acquire a single steady-state data point. To save labor and time, simulation of this process is necessary to test various reaction conditions and determine the optimal operating point. Previously, a suitable kinetic model for countercurrent saccharification has never been reported. The Continuum Particle Distribution Modeling (CPDM) satisfactorily predicts countercurrent fermentation using mixed microbial cultures that digest various feedstocks. Here, CPDM is applied to countercurrent enzymatic saccharification of lignocellulose.

RESULTS

CPDM was used to simulate multi-stage countercurrent saccharifications of a lignocellulose model compound (α-cellulose). The modified HCH-1 model, which accurately predicts long-term batch saccharification, was used as the governing equation in the CPDM model. When validated against experimental countercurrent saccharification data, it predicts experimental glucose concentrations and conversions with the average errors of 3.5% and 4.7%, respectively. CPDM predicts conversion and product concentration with varying enzyme-addition location, total stage number, enzyme loading, liquid residence time (LRT), and solids loading rate (SLR). In addition, countercurrent saccharification was compared to batch saccharification at the same conversion, product concentration, and reactor volume. Results show that countercurrent saccharification is particularly beneficial when the product concentration is low.

CONCLUSIONS

The CPDM model was used to simulate multi-stage countercurrent saccharification of α-cellulose. The model predictions agreed well with the experimental glucose concentrations and conversions. CPDM prediction results showed that the enzyme-addition location, enzyme loading, LRT, and SLR significantly affected the glucose concentration and conversion. Compared to batch saccharification at the same conversion, product concentration, and reactor volume, countercurrent saccharification is particularly beneficial when the product concentration is low.

摘要

背景

逆流糖化是一种很有前景的方法,可在获得高转化率和产物浓度的同时尽量减少酶的用量。然而,在逆流糖化实验中,通常需要3至4个月才能获得一个单一的稳态数据点。为了节省人力和时间,有必要对该过程进行模拟,以测试各种反应条件并确定最佳操作点。此前,从未有过适用于逆流糖化的动力学模型的报道。连续颗粒分布模型(CPDM)能够令人满意地预测使用混合微生物培养物消化各种原料的逆流发酵过程。在此,CPDM被应用于木质纤维素的逆流酶促糖化。

结果

CPDM用于模拟木质纤维素模型化合物(α-纤维素)的多级逆流糖化。准确预测长期分批糖化的修正HCH-1模型被用作CPDM模型中的控制方程。当根据逆流糖化实验数据进行验证时,它预测的实验葡萄糖浓度和转化率的平均误差分别为3.5%和4.7%。CPDM可预测在不同酶添加位置、总级数、酶用量、液体停留时间(LRT)和固体装载率(SLR)下的转化率和产物浓度。此外,还在相同转化率、产物浓度和反应器体积下将逆流糖化与分批糖化进行了比较。结果表明,当产物浓度较低时,逆流糖化特别有益。

结论

CPDM模型用于模拟α-纤维素的多级逆流糖化。模型预测结果与实验葡萄糖浓度和转化率吻合良好。CPDM预测结果表明,酶添加位置、酶用量、LRT和SLR对葡萄糖浓度和转化率有显著影响。与在相同转化率、产物浓度和反应器体积下的分批糖化相比,当产物浓度较低时,逆流糖化特别有益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d196/6621958/5cf2279bdd24/13068_2019_1517_Fig1_HTML.jpg

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