Degradation and Redeposition of the Chemical Components of Aspen Wood during Hot Water Extraction

Chen, Haiyan; Fu, Yingjuan; Wang, Zhaojiang; Qin, Menghua

doi:10.15376/biores.10.2.3005-3016

Cited by 11 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The ideal reductive catalytic fractionation process would proceed in pure water; however, redeposition of the dissolved lignin on the wood fiber surface presents a major problem, a phenomena only seldom discussed. 214 In this regard, the addition of an organic solvent plays an important role as it retards the redeposition of lignin onto the other biomass components after separation. 215 This is also likely the reason why higher product yields are obtained when an organic cosolvent is used.…”

Section: Catalytic Strategies Aiming At High Yield and Selective Prodmentioning

confidence: 99%

Bright Side of Lignin Depolymerization: Toward New Platform Chemicals

et al. 2018

View full text Add to dashboard Cite

Lignin, a major component of lignocellulose, is the largest source of aromatic building blocks on the planet and harbors great potential to serve as starting material for the production of biobased products. Despite the initial challenges associated with the robust and irregular structure of lignin, the valorization of this intriguing aromatic biopolymer has come a long way: recently, many creative strategies emerged that deliver defined products via catalytic or biocatalytic depolymerization in good yields. The purpose of this review is to provide insight into these novel approaches and the potential application of such emerging new structures for the synthesis of biobased polymers or pharmacologically active molecules. Existing strategies for functionalization or defunctionalization of lignin-based compounds are also summarized. Following the whole value chain from raw lignocellulose through depolymerization to application whenever possible, specific lignin-based compounds emerge that could be in the future considered as potential lignin-derived platform chemicals.

show abstract

Section: Catalytic Strategies Aiming At High Yield and Selective Prodmentioning

confidence: 99%

Bright Side of Lignin Depolymerization: Toward New Platform Chemicals

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Each wood and acid species is represented as a binary feature, also known as one-hot encoding. The dataset covers a wide range of wood species including acacia (Shi et al, 2019), aspen (Jensen et al, 2008;Mittal et al, 2009b;Morinelly et al, 2009;Jensen et al, 2010;Li et al, 2010;Chen et al, 2015;Yan and Liu, 2015), basswood (Jensen et al, 2008), beech (Nitsos et al, 2016(Nitsos et al, , 2013, birch (Li et al, 2010;Borrega et al, 2011;Gladyshko, 2011;Ahmad et al, 2016), carob (Dagnino et al, 2013), eucalyptus (Parajó et al, 1994(Parajó et al, , 1993Vázquez et al, 1995;Garrote et al, 1999Garrote et al, , 2001Canettieri et al, 2007a,b;Garrote et al, 2007;Yu et al, 2009;Romaní et al, 2010;Chirat et al, 2012;Gutsch et al, 2012;McIntosh et al, 2012;Wei et al, 2012;Castro et al, 2014;López et al, 2014;Tunc, 2014;Rangel et al, 2016;Inalbon et al, 2017;Mateo et al, 2014;Cebreiros et al, 2018;Peleteiro et al, 2018;Ma et al, 2017), maple (Jensen et al, 2008;Mittal et al, 2009a;L...…”

Section: Data Collection and Processingmentioning

confidence: 99%

“…In this study, we use machine learning models to predict the xylose yield from hardwood hemicellulose hydrolysis in batch reactors. Specifically, 1955 data points were mined from the literature (Jensen et al, 2008;Shi et al, 2019;Mittal et al, 2009b;Morinelly et al, 2009;Jensen et al, 2010;Chen et al, 2015;Yan and Liu, 2015;Nitsos et al, 2016;Borrega et al, 2011;Gladyshko, 2011;Ahmad et al, 2016;Dagnino et al, 2013;Parajó et al, 1994Parajó et al, , 1993Vázquez et al, 1995;Garrote et al, 1999Garrote et al, , 2001Canettieri et al, 2007a,b;Garrote et al, 2007;Yu et al, 2009;Romaní et al, 2010;Chirat et al, 2012;Gutsch et al, 2012;McIntosh et al, 2012;Wei et al, 2012;Castro et al, 2014;López et al, 2014;Tunc, 2014;Rangel et al, 2016;Inalbon et al, 2017;Mateo et al, 2014;Cebreiros et al, 2018;Peleteiro et al, 2018;Mittal et al, 2009a;Zhang et al, 2013;Rafiqul and Sakinah, 2012b,a;Rafiqul et al, 2014;Tunc and van Heiningen, 2008;Jeong and Lee, 2016;Spring...…”

Section: Introductionmentioning

confidence: 99%

Predicting xylose yield from prehydrolysis of hardwoods: A machine learning approach

Wang

Ballachay²,

Cai³

et al. 2022

Front. Chem. Eng.

View full text Add to dashboard Cite

Hemicelluloses are amorphous polymers of sugar molecules that make up a major fraction of lignocellulosic biomasses. They have applications in the bioenergy, textile, mining, cosmetic, and pharmaceutical industries. Industrial use of hemicellulose often requires that the polymer be hydrolyzed into constituent oligomers and monomers. Traditional models of hemicellulose degradation are kinetic, and usually only appropriate for limited operating regimes and specific species. The study of hemicellulose hydrolysis has yielded substantial data in the literature, enabling a diverse data set to be collected for general and widely applicable machine learning models. In this paper, a dataset containing 1955 experimental data points on batch hemicellulose hydrolysis of hardwood was collected from 71 published papers dated from 1985 to 2019. Three machine learning models (ridge regression, support vector regression and artificial neural networks) are assessed on their ability to predict xylose yield and compared to a kinetic model. Although the performance of ridge regression was unsatisfactory, both support vector regression and artificial neural networks outperformed the simple kinetic model. The artificial neural network outperformed support vector regression, reducing the mean absolute error in predicting soluble xylose yield of test data to 6.18%. The results suggest that machine learning models trained on historical data may be used to supplement experimental data, reducing the number of experiments needed.

show abstract

“…Среди лиственных пород деревьев второе место по площади произрастания занимает осина. В древесине осины присутствуют в значительном количестве гемицеллюлозы [8], затрудняющие применение традиционных технологий ее переработки в глюкозные гидролизаты для синтеза биоспиртов. Глюкозные гидролизаты, полученные непосредственно из древесины, загрязнены примесями (фурановыми соединениями, терпенами, фенолами и танинами), ингибирующими ферментативные процессы и снижающими выход этанола [9].…”

Section: Introductionunclassified

The Integration of Catalytic Processes of Acid Hydrolysis and Peroxide Delignification to Obtain the Bioethanol from Aspen Wood

Кузнецов¹,

Yatsenkova²,

Skripnikov³

et al. 2017

J. Sib. Fed. Univ. Chem.

View full text Add to dashboard Cite

The processes of catalytic hydrolysis of hemicelluloses and cellulose of aspen wood were studied at the temperatures 100 and 150 °C. The dissolved mineral acids (H 2 SO 4 , HCl), ion exchange resin Amberlyst-15 and acid-modified SBA-15, Sibunit-4 were used in hydrolysis of aspen wood as the catalysts. The optimal conditions for the implementation of catalytic processes of acid hydrolysis of wood hemicelluloses, peroxide delignification of lignocellulose and hydrolysis of cellulose for the production of hydrolysates with the maximum glucose yield and the minimum amount of inhibitors of enzymatic processes (furfural and 5-hydroxymethylfurfural) were established. The estimated yields of bioethanol obtained by integration of optimized catalytic processes of acid hydrolysis of polysaccharides and peroxide delignification of lignocellulose of aspen wood were evaluated.

show abstract

Degradation and Redeposition of the Chemical Components of Aspen Wood during Hot Water Extraction

Cited by 11 publications

References 21 publications

Bright Side of Lignin Depolymerization: Toward New Platform Chemicals

Bright Side of Lignin Depolymerization: Toward New Platform Chemicals

Predicting xylose yield from prehydrolysis of hardwoods: A machine learning approach

The Integration of Catalytic Processes of Acid Hydrolysis and Peroxide Delignification to Obtain the Bioethanol from Aspen Wood

Contact Info

Product

Resources

About