谷歌浏览器插件
订阅小程序
在清言上使用

Grading Herbaceous Biomass for Biorefineries: a Case Study Based on Chemical Composition and Biochemical Conversion

BioEnergy research/BioEnergy Research(2019)

引用 10|浏览7
暂无评分
摘要
Due to the highly variable nature of biomass resources, establishing a quantitative system for evaluating biomass quality offers tangible benefits for emerging bioeconomy markets. This paper demonstrates an approach for developing a grading system, based on inherent biomass characteristics, to inform markets on how natural variability impacts conversion performance. The case study considers a biochemical biorefining pathway, in which monomeric sugars are fermented to mainly ethanol, while the preliminary demonstration is based on five grass types, typically used as feedstock in this type of biorefinery. Samples of the biomass resources were characterized and converted to carbohydrates using dilute-acid pretreatment and enzymatic hydrolysis. The approach involved three steps: (1) identify key biomass compositional characteristics that impact biochemical conversion performance and fermentable sugar yields using a linear regression approach, (2) assess the range of variability for the key characteristics using a diverse set of biomass resources, and (3) grade ranges of variability based on predicted conversion yields. It was demonstrated that five chemical characteristics, structural glucan, hemicellulose carbohydrates, acid-soluble lignin, acid-insoluble lignin, and total ash, could be used to estimate conversion performance. Five grades were established using a hierarchical cluster analysis. Future research is needed to confirm that these characteristics can predict product yields over multiple pretreatment methods and enzymatic hydrolysis/fermentation conditions. In addition, it would be beneficial to extend a grading system to include other biomass characteristics that impact preprocessing operations, such as grinding and conveying.
更多
查看译文
关键词
Biomass,Grade,Chemical characteristics,Biochemical conversion,Multivariate modeling
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要