乙酰丁酸梭菌ATCC 824从油棕榈叶(OPF)果汁中生产生物丁醇的析因试验设计

Nur Syazana Muhamad Nasrah, M. A. K. Zahari, N. Masngut, H. Ariffin
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引用次数: 1

摘要

生物丁醇是一种很有前途的替代能源。它可以通过微生物从天然和可再生的农业废物中生产,如油棕叶(OPF)果汁。乙酰丁酸梭菌具有将OPF汁中的糖作为碳源发酵成生物丁醇的能力。本研究旨在探讨独立因素和相互作用因素的影响;对C. acetobutylicum ATCC 824从油棕榈叶(OPF)果汁中生产生物丁醇的初始pH培养基(5-7)、接种量(1-20%)、初始总糖浓度(40-60 g/L)、温度(32-42℃)和酵母提取物浓度(1-10 g/L)进行了研究,采用了由design Expert Software Version 7.1开发的两水平半因子设计。因子分析结果表明,酵母浸膏浓度对产量的贡献最大,为8.20%,其次是接种量和温度,分别为7.84%和7.56%。分析表明,该模型的r2值为0.9805,接种量与温度的交互作用对发酵过程的影响最大,贡献率达16.31%。从验证实验来看,实验值与预测值较为接近,误差分别为5.87%和10.09%。验证了预测模型的有效性和充分性。因此,设计专家软件开发的数据分析可以可靠地预测生物丁醇产量。本研究表明,每种因素都可能影响生物丁醇的发酵过程。化工研究通报19(2017)36-42
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Factorial Experimental Design for Biobutanol Production from Oil Palm Frond (OPF) Juice by Clostridium Acetobutylicum ATCC 824
Biobutanol is an alternative energy that can be promising as the future energy source. It can be produced from natural and renewable agriculture wastes such as oil palm frond (OPF) juice by microbes. Clostridium acetobutylicum has the ability to ferment the sugars in OPF juice as carbon source into biobutanol. This research aimed to investigate the effect of independent and interaction factors; initial pH medium (5-7), inoculum size (1-20%), initial total sugars concentration (40-60 g/L), temperature (32-42 ° C) and yeast extract concentration (1-10 g/L) on the production of biobutanol from oil palm frond (OPF) juice by C. acetobutylicum ATCC 824 using a two level half factorial design which have been developed by the Design Expert Software Version 7.1. Based on the factorial analysis, it was observed that the most significant parameter was yeast extract concentration, which contributes 8.20%, followed by inoculum size and temperature, which were contribute 7.84% and 7.56%, respectively. The analysis showed the R 2 value for the model was 0.9805 and the interaction between inoculum size and temperature gave the highest influenced to the fermentation process with contribution up to 16.31%. From the validation experiments, the experimental values were reasonable close to the predicted values with only 5.87% and 10.09% of errors. It confirmed the validity and adequacy of the predicted models. Hence, the data analysis developed from the Design Expert Software could reliably predict biobutanol yields. This study indicated that each of the factors may affect the fermentation process of the biobutanol production. Chemical Engineering Research Bulletin 19(2017) 36-42
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