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Record W3198413643 · doi:10.1016/j.cep.2021.108614

One flow through hydrolysis and hydrogenation of semi-industrial xylan from birch (betula pendula) in a continuous reactor—Kinetics and modelling

2021· article· en· W3198413643 on OpenAlexaff
Xiaojia Lu, Paula Junghans, Stephanie Weckesser, Johan Wärnå, Gerd Hilpmann, Rüdiger Lange, Heather L. Trajano, Kari Eränen, Lionel Estel, Sébastien Leveneur, Henrik Grénman

Bibliographic record

VenueChemical Engineering and Processing - Process Intensification · 2021
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsHydrolysisChemistryXylitolXyloseXylanCatalysisBiorefineryMonosaccharideChemical reaction engineeringOrganic chemistryYield (engineering)Chemical engineeringChromatographyFermentationMaterials scienceRaw material

Abstract

fetched live from OpenAlex

Xylan obtained from birch (betula pendula) by a novel semi-industrial scale aqueous based method was used for studying the hydrolysis and consecutive hydrolysis-hydrogenation processes in continuous reactors. Dowex 50WX2-100 was chosen as the hydrolysis catalyst based on the results of catalyst screening performed previously in batch reactor. It was also observed to perform well in continuous reactor converting xylan to xylose in high yield under the studied reaction conditions. The influence of several reaction parameters were investigated for optimization. Similar experimental conditions used in the hydrolysis were then applied for studying one flow through hydrolysis and hydrogenation of the semi-industrial xylan. A consecutive catalyst bed consisting of ruthenium on carbon was introduced into the continuous reactor downstream from the hydrolysis bed to hydrogenate monosaccharides to xylitol. Hydrogen was co-fed into the reactor with the xylan solution. Reaction parameters, including temperature, residence time and hydrogen pressure, were varied to maximize the xylitol yield. The developed continuous process was demonstrated to be highly selective and efficient for the valorization of the semi-industrial xylan by yielding over 90% xylitol under optimal experimental conditions. It was noticed, that co-feeding hydrogenation decreased the degradation of monosaccharides during hydrolysis, thus improving the selectivity towards the target product and enabling remarkable process intensification. Moreover, mathematical modelling was performed for the hydrolysis and one flow through hydrolysis and hydrogenation processes. The models take into account the consecutive reaction pathways and the influence of the experimental conditions. Good fits of the model to the experimental data were obtained. The conversion of this novel, well characterized wood-based xylan to produce xylose or xylitol in continuous reactors has not been studied previously. The current work contributes significantly to understanding the processing of real feedstock in one flow through employing consecutive reactions and provides necessary data for process intensification.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.213
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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