Near Infrared Calibration Models for Pretreated Corn Stover Slurry Solids, Isolated and in situ

Author:

Sluiter Amie1,Wolfrum Ed1

Affiliation:

1. National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado, USA

Abstract

Biomass pretreatment processes often yield slurry, a two-phase material consisting of an aqueous phase with solubilised components and a solid phase with insoluble constituents. Chemical characterisation of this material using conventional wet chemical analysis requires that the two phases be analysed separately. We have previously demonstrated near infrared (NIR) models that successfully predict the chemical composition of the solid phase after separation, washing and drying. In this work, we present the current version of this calibration model, as well as a model that uses spectra of the whole slurry samples (without separation) to predict the solids composition in situ. Removing the slurry solid/liquid separation step saves large amounts of time and effort during analysis. The model using washed and dried solids provided predicted vs measured correlation coefficient ( R2) values of 0.97, 0.99 and 0.98 and root mean square error of calibration ( RMSEC) values of 1.5, 0.8 and 0.8 dry weight percent for glucan, xylan and lignin, respectively. These RMSEC values are similar to established wet chemical analysis uncertainties. Validation samples also showed similar uncertainties and an average r2 value of 0.98 for the major constituents. The whole slurry model provided R2 values of 0.93, 0.93 and 0.95 and RMSEC values of 2.3,1.7 and 1.0 dry weight percent for glucan, xylan and lignin, respectively. The RMSEC values are larger than established wet chemical analysis uncertainties. Validation samples showed uncertainties and r2 values that were not statistically significantly different ( p = 0.05) from calibration model values for glucan, xylan, and lignin. The slurry model was not equivalent to the washed and dried pretreated solids model, with relative increases in RMSEC values of 20%–50% for major constituents. However, the model was highly successful for the intended purpose, which was to predict the composition of samples without the significant added effort of separating, washing and drying solids prior to scanning.

Publisher

SAGE Publications

Subject

Spectroscopy

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3