木糖
琥珀酸
生物量(生态学)
生物过程
批处理
化学
产量(工程)
补料分批培养
间歇式反应器
单糖
批量生产
制浆造纸工业
生物化学
化学工程
发酵
生物
有机化学
计算机科学
材料科学
催化作用
农学
程序设计语言
冶金
工程类
作者
Itziar A. Escanciano,Vanessa Ripoll,Miguel Ladero,Victoria E. Santos
出处
期刊:Fermentation
[MDPI AG]
日期:2023-07-14
卷期号:9 (7): 663-663
被引量:3
标识
DOI:10.3390/fermentation9070663
摘要
Succinic acid (SA) is one of the most prominent C4 biomass-based platform chemicals that can be biologically obtained. This article verifies, for the first time, the possibility of producing succinic acid with fed-batch or repeated batch operations with Actinobacillus succinogenes in a resting state, that is, in the absence of a nitrogen source. In this work it is possible to optimise separately the stages of cell growth and production in the fed-batch or repeated batch modes, minimising the costs associated with the nitrogen source and facilitating the subsequent purification of SA. These experiments were carried out with xylose, the most abundant monosaccharide in hemicelluloses, with the results subsequently being compared to those obtained in equivalent operations carried out with cells in a state of growth. First, a cost-effective synthetic growth medium was proposed and successfully employed for SA production. Biocatalysts’ reutilisation showed that the bioprocess can be carried out successfully in repeated batch and fed-batch modes. The best mode for growing cells is repeated batch, achieving a maximum productivity of 0.77 g‧L−1‧h−1, a selectivity of 53% and a yield of 51% with respect to xylose consumed. In contrast, the fed-batch mode was found to be the most convenient mode with resting cell biocatalyst, reaching a maximum productivity of 0.83 g‧L−1‧h−1, a selectivity of 0.78 g‧g−1 and a yield of 68% with respect to the xylose consumed. In addition, by-product formation is significantly reduced when employing resting cells. An unstructured non-segregated kinetic model was developed for both biocatalysts, capable of simulating cell growth, xylose consumption, SA production and by-product generation, with successful estimation of kinetic parameters supported by statistical criteria.
科研通智能强力驱动
Strongly Powered by AbleSci AI