Combined autohydrolysis and alkali pretreatments for cellulose enzymatic hydrolysis of Eucalyptus grandis wood

Cebreiros, Florencia; Ferrari, Mario Daniel; Lareo, Claudia

doi:10.1007/s13399-016-0236-4

Cited by 16 publications

(4 citation statements)

References 36 publications

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“…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%

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

Wang

Ballachay²,

Cai³

et al. 2022

Front. Chem. Eng.

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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.

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Section: Data Collection and Processingmentioning

confidence: 99%

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

show abstract

“…It is worth mentioning that these functional groups, especially the hydroxyl and aromatic structure, crucially impact the chemical properties and reactivity of lignin. Despite the wide relevance of these resources, the development of pretreatment methods is essential to break up the lignin structures for isolation of the three biopolymers (lignin, cellulose, and hemicellulose) and improve enzyme digestibility of cellulose [11].…”

Section: Introductionmentioning

confidence: 99%

Application of Clostridium thiosulphatireducens, Enterobacter aerogenes and their co-culture inoculum for Biohydrogen Production

Kedar,

Nair,

Konale

et al. 2023

Preprint

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Biohydrogen has drawn the attention of researchers all over the world due to its advantages over conventional fuels. However, it is necessary to make the process of biohydrogen production economically and environmentally sustainable. In this study, biohydrogen production from soybean straw in anaerobic batch reactor (sera bottles) using H2 producing bacteria (Clostridium thiosulphatireducens and Enterobacter aerogenes) was investigated. Candidate strains were identified and analyzed by phylogenetic analysis. These bacteria were tested for their biohydrogen production singly as well as in combination. C. thiosulphatireducens, E. aerogenes and their co-culture inoculums were named as strain I, strain II and co-culture inoculum respectively. The fermentation process was carried out at 37°C at pH 6. Physico-chemical characteristics of substrate, cellulase enzyme activity, and 16S rDNA gene sequences were investigated. Maximum cellulase production was observed in co-culture inoculum which was 4.004 IU/ml. Maximum biohydrogen yield obtained was 1.39 mol of H2/g TS. By products formed during fermentation were acetic, butyric and propionic acid and formic acid. The analysis of variance (ANOVA) R2 value 0.843 indicates that 84.3% of variation in production of mol of H2 is explained by its relationship with microbial culture.

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“…Liquid hot water (LHW) pretreatment is an efficient method to extract hemicellulose from biomass [21]. The surface area and porosity of the pretreated biomass can be increased after LHW pretreatment, which is beneficial to improve the accessibility of cellulose [22]. However, the lignin cannot be effectively degraded with LHW pretreatment.…”

Section: Introductionmentioning

confidence: 99%

A Combination Method of Liquid Hot Water and Phosphotungstic Acid Pretreatment for Improving the Enzymatic Saccharification Efficiency of Rice Straw

et al. 2022

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Chemical pretreatment can significantly improve the enzymatic hydrolysis efficiency of lignocellulosic biomass, thereby improving the yield of sugar materials for the production of cellulosic ethanol, but commonly used acid–base catalysts are difficult to recover and reuse. In this work, a combination method of liquid hot water (LHW) and phosphotungstic acid (PTA) pretreatment was performed to improve the saccharification efficiency of rice straw, and we attempted to evaluate the reuse effect of PTA catalysts. The rice straw was first treated with LHW at 180 °C for 90 min, and then treated with 20 mM PTA at 130 °C for 60 min. After pretreatment, the cellulose hydrolysis efficiency and glucose recovery of the rice straw increased by 201.85% and 164.25%, respectively. Glucose accounted for 96.8% of the total reducing sugar in the final enzymatic hydrolysate. After each PTA pretreatment, approximately 70.8–73.2% of the PTA catalyst could be recycled. Moreover, the catalytic activity of the PTA catalyst that had been used five times did not decrease. The improved enzymatic saccharification efficiency was attributed to the removal of 89.24% hemicellulose and 21.33% lignin from the lignocellulosic substrate. The two-step LHW-PTA pretreatment could pretreat biomass in the field of cellulosic ethanol production.

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Combined autohydrolysis and alkali pretreatments for cellulose enzymatic hydrolysis of Eucalyptus grandis wood

Cited by 16 publications

References 36 publications

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

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

Application of Clostridium thiosulphatireducens, Enterobacter aerogenes and their co-culture inoculum for Biohydrogen Production

A Combination Method of Liquid Hot Water and Phosphotungstic Acid Pretreatment for Improving the Enzymatic Saccharification Efficiency of Rice Straw

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