Literature DB >> 25255187

Monitoring lignocellulosic bioethanol production processes using Raman spectroscopy.

Jens A Iversen1, Birgitte K Ahring2.   

Abstract

Process control automation in the emerging biorefinery industry may be achieved by applying effective methods for monitoring compound concentrations during the production processes. This study examines the application of Raman spectroscopy with an excitation wavelength of 785nm and an immersion probe for in situ monitoring the progression of pretreatment, hydrolysis and fermentation processes in the production of lignocellulosic ethanol. Raman signals were attenuated by light scattering cells and lignocellulosic particulates, which the quantification method to some degree could correct for by using an internal standard in the spectra. Allowing particulates to settle by using a slow stirring speed further improved results, suggesting that Raman spectroscopy should be used in combination with continuous separation when used to monitor process mixtures with large amounts of particulates. The root mean square error of prediction (RMSE) of ethanol and glucose measured in real-time was determined to be 0.98g/L and 1.91g/L respectively.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Ethanol fermentation; Hydrolysis; Lignocellulose; Pretreatment; Raman spectroscopy

Mesh:

Substances:

Year:  2014        PMID: 25255187     DOI: 10.1016/j.biortech.2014.08.068

Source DB:  PubMed          Journal:  Bioresour Technol        ISSN: 0960-8524            Impact factor:   9.642


  3 in total

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Authors:  Charlotte Anne Vees; Lukas Veiter; Fritz Sax; Christoph Herwig; Stefan Pflügl
Journal:  Anal Bioanal Chem       Date:  2020-02-07       Impact factor: 4.142

2.  Improved bioethanol productivity through gas flow rate-driven self-cycling fermentation.

Authors:  Jie Wang; Michael Chae; David C Bressler; Dominic Sauvageau
Journal:  Biotechnol Biofuels       Date:  2020-01-24       Impact factor: 6.040

3.  Visual tool for real-time monitoring of membrane fouling via Raman spectroscopy and process model based on principal component analysis.

Authors:  Tiina Virtanen; Satu-Pia Reinikainen; Jussi Lahti; Mika Mänttäri; Mari Kallioinen
Journal:  Sci Rep       Date:  2018-07-23       Impact factor: 4.379

  3 in total

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