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Mass production of chemicals from biomass-derived oil by directly atmospheric distillation coupled with co-pyrolysis.

ABSTRACT: Production of renewable commodity chemicals from bio-oil derived from fast pyrolysis of biomass has received considerable interests, but hindered by the presence of innumerable components in bio-oil. In present work, we proposed and experimentally demonstrated an innovative approach combining atmospheric distillation of bio-oil with co-pyrolysis for mass production of renewable chemicals from biomass, in which no waste was produced. It was estimated that 51.86 wt.% of distillate just containing dozens of separable organic components could be recovered using this approach. Ten protogenetic and three epigenetic compounds in distillate were qualitatively identified by gas chromatography/mass spectrometry and quantified by gas chromatography. Among them, the recovery efficiencies of acetic acid, propanoic acid, and furfural were all higher than 80 wt.%. Formation pathways of the distillate components in this process were explored. This work opens up a fascinating prospect for mass production of chemical feedstock from waste biomass.

SUBMITTER: Zhang XS

PROVIDER: S-EPMC3553461 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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Mass production of chemicals from biomass-derived oil by directly atmospheric distillation coupled with co-pyrolysis.

Zhang Xue-Song XS Yang Guang-Xi GX Jiang Hong H Liu Wu-Jun WJ Ding Hong-Sheng HS

Scientific reports 20130124

Production of renewable commodity chemicals from bio-oil derived from fast pyrolysis of biomass has received considerable interests, but hindered by the presence of innumerable components in bio-oil. In present work, we proposed and experimentally demonstrated an innovative approach combining atmospheric distillation of bio-oil with co-pyrolysis for mass production of renewable chemicals from biomass, in which no waste was produced. It was estimated that 51.86 wt.% of distillate just containing ...[more]

PMID: 23350028

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Project description:High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

Project description:IntroductionThe aggressive search for renewable energy resources and essential pyrosynthetic compounds has marked an exponential rise in the thermal degradation of biomass materials. Consequently, clean and sustainable transport fuels are increasingly desirable in a highly industrialized economy, for energy security and environmental protection. For this reason, biomass materials have been identified as promising alternatives to fossil fuels despite the challenges resulting from the possible formation of toxic nitrogen-based molecules during biomass degradation. In order to understand the free radical characteristic challenges facing the use of bio-oil, a brief review of the effects of free radicals in bio-oil is presented.MethodologyPyrolysis was conducted in a tubular flow quartz reactor at a residence time of 2 s at 1 atm. pressure, for a total pyrolysis time of 5 min. The thermal degradation of biomass components was investigated over the temperature range of 200 to 700 °C typically in 50 °C increments under two reaction conditions; pyrolysis in N2 and oxidative pyrolysis in 5% O2 in N2. The pyrolysate effluent was analysed using a Gas chromatograph hyphenated to a mass selective detector (MSD).ResultsThe yield of levoglucosan in the pyrolysis of cellulose in the entire pyrolysis temperature range was 68.2 wt % under inert conditions and 28.8 wt % under oxidative conditions. On the other hand, formaldehyde from pyrolysis of cellulose yielded 4 wt % while that from oxidative pyrolysis was 7 wt % translating to ⁓ 1.8 times higher than the yield from pyrolysis. Accordingly, we present for the first time dioxin-like and dibenzofuran-like nitrogenated analogues from an equimassic pyrolysis of cellulose and tyrosine. Levoglucosan and formaldehyde were completely inhibited during the equimassic pyrolysis of cellulose and tyrosine.ConclusionClearly, any small amounts of N-biomass components such as amino acids in cellulosic biomass materials can inhibit the formation of levoglucosan-a major constituent of bio-oil. Overall, a judicious balance between the production of bio-oil and side products resulting from amino acids present in plant matter should be taken into account to minimize economic losses and mitigate against negative public health concerns.

Dataset Information

Mass production of chemicals from biomass-derived oil by directly atmospheric distillation coupled with co-pyrolysis.

Publications

Mass production of chemicals from biomass-derived oil by directly atmospheric distillation coupled with co-pyrolysis.

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