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Optimization of biomass pretreatments using fractional factorial experimental design

BACKGROUND: Pretreatments are one of the main bottlenecks for the lignocellulose conversion process and the search for cheaper and effective pretreatment methodologies for each biomass is a complex but fundamental task. Here, we used a 2ν(5−1) fractional factorial design (FFD) to optimize five pretr...

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Autores principales: Rezende, Camila A., Atta, Beatriz W., Breitkreitz, Marcia C., Simister, Rachael, Gomez, Leonardo D., McQueen-Mason, Simon J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6058377/
https://www.ncbi.nlm.nih.gov/pubmed/30061928
http://dx.doi.org/10.1186/s13068-018-1200-2
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author Rezende, Camila A.
Atta, Beatriz W.
Breitkreitz, Marcia C.
Simister, Rachael
Gomez, Leonardo D.
McQueen-Mason, Simon J.
author_facet Rezende, Camila A.
Atta, Beatriz W.
Breitkreitz, Marcia C.
Simister, Rachael
Gomez, Leonardo D.
McQueen-Mason, Simon J.
author_sort Rezende, Camila A.
collection PubMed
description BACKGROUND: Pretreatments are one of the main bottlenecks for the lignocellulose conversion process and the search for cheaper and effective pretreatment methodologies for each biomass is a complex but fundamental task. Here, we used a 2ν(5−1) fractional factorial design (FFD) to optimize five pretreatment variables: milling time, temperature, double treatment, chemical concentration, and pretreatment time in acid–alkali (EA) and acid–organosolv (EO) pretreatments, applied to elephant grass leaves. RESULTS: FFD allowed optimization of the pretreatment conditions using a reduced number of experiments and allowed the identification of secondary interactions between the factors. FFD showed that the temperature can be kept at its lower level and that the first acid step can be eliminated in both pretreatments, without significant losses to enzymatic hydrolysis. EA resulted in the highest release of reducing sugars (maximum of 205 mg/g substrate in comparison to 152 mg/g in EO and 40 mg/g in the untreated sample), using the following conditions in the alkali step: [NaOH] = 4.5% w/v; 85 °C and 100 min after ball milling the sample. The factors statistically significant (P < 0.05) in EA pretreatment were NaOH concentration, which contributes to improved hydrolysis by lignin and silica removal, and the milling time, which has a mechanical effect. For EO samples, the statistically significant factors to improved hydrolysis were ethanol and catalyst concentrations, which are both correlated to higher cellulose amounts in the pretreated substrates. The catalyst is also correlated to lignin removal. The detailed characterization of the main hemicellulosic sugars in the solids after pretreatments revealed their distinct recalcitrance: glucose was typically more recalcitrant than xylose and arabinose, which could be almost completely removed under specific pretreatments. In EA samples, the removal of hemicellulose derivatives was very dependent on the acid step, especially arabinose removal. CONCLUSION: The results presented herewith contribute to the development of more efficient and viable pretreatments to produce cellulosic ethanol from grass biomasses, saving time, costs and energy. They also facilitate the design of enzymatic cocktails and a more appropriate use of the sugars contained in the pretreatment liquors, by establishing the key recalcitrant polymers in the solids resulting from each processing step. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13068-018-1200-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-60583772018-07-30 Optimization of biomass pretreatments using fractional factorial experimental design Rezende, Camila A. Atta, Beatriz W. Breitkreitz, Marcia C. Simister, Rachael Gomez, Leonardo D. McQueen-Mason, Simon J. Biotechnol Biofuels Research BACKGROUND: Pretreatments are one of the main bottlenecks for the lignocellulose conversion process and the search for cheaper and effective pretreatment methodologies for each biomass is a complex but fundamental task. Here, we used a 2ν(5−1) fractional factorial design (FFD) to optimize five pretreatment variables: milling time, temperature, double treatment, chemical concentration, and pretreatment time in acid–alkali (EA) and acid–organosolv (EO) pretreatments, applied to elephant grass leaves. RESULTS: FFD allowed optimization of the pretreatment conditions using a reduced number of experiments and allowed the identification of secondary interactions between the factors. FFD showed that the temperature can be kept at its lower level and that the first acid step can be eliminated in both pretreatments, without significant losses to enzymatic hydrolysis. EA resulted in the highest release of reducing sugars (maximum of 205 mg/g substrate in comparison to 152 mg/g in EO and 40 mg/g in the untreated sample), using the following conditions in the alkali step: [NaOH] = 4.5% w/v; 85 °C and 100 min after ball milling the sample. The factors statistically significant (P < 0.05) in EA pretreatment were NaOH concentration, which contributes to improved hydrolysis by lignin and silica removal, and the milling time, which has a mechanical effect. For EO samples, the statistically significant factors to improved hydrolysis were ethanol and catalyst concentrations, which are both correlated to higher cellulose amounts in the pretreated substrates. The catalyst is also correlated to lignin removal. The detailed characterization of the main hemicellulosic sugars in the solids after pretreatments revealed their distinct recalcitrance: glucose was typically more recalcitrant than xylose and arabinose, which could be almost completely removed under specific pretreatments. In EA samples, the removal of hemicellulose derivatives was very dependent on the acid step, especially arabinose removal. CONCLUSION: The results presented herewith contribute to the development of more efficient and viable pretreatments to produce cellulosic ethanol from grass biomasses, saving time, costs and energy. They also facilitate the design of enzymatic cocktails and a more appropriate use of the sugars contained in the pretreatment liquors, by establishing the key recalcitrant polymers in the solids resulting from each processing step. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13068-018-1200-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-07-24 /pmc/articles/PMC6058377/ /pubmed/30061928 http://dx.doi.org/10.1186/s13068-018-1200-2 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Rezende, Camila A.
Atta, Beatriz W.
Breitkreitz, Marcia C.
Simister, Rachael
Gomez, Leonardo D.
McQueen-Mason, Simon J.
Optimization of biomass pretreatments using fractional factorial experimental design
title Optimization of biomass pretreatments using fractional factorial experimental design
title_full Optimization of biomass pretreatments using fractional factorial experimental design
title_fullStr Optimization of biomass pretreatments using fractional factorial experimental design
title_full_unstemmed Optimization of biomass pretreatments using fractional factorial experimental design
title_short Optimization of biomass pretreatments using fractional factorial experimental design
title_sort optimization of biomass pretreatments using fractional factorial experimental design
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6058377/
https://www.ncbi.nlm.nih.gov/pubmed/30061928
http://dx.doi.org/10.1186/s13068-018-1200-2
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