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Nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass: A review

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FUEL
卷 335, 期 -, 页码 -

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ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2022.126995

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Nitrogen heterocyclic compounds; Hydro -thermal liquefaction; Protein -rich biomass; Maillard reaction; Bio-oil; Aqueous phase

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This study provides an overview of the effects of biomass and HTL processing parameters on the distribution and transformation mechanisms of N-H during HTL of nitrogen-rich biomass. Factors such as biomass composition, HTL process parameters, and extraction conditions were found to influence the formation of N-H. Strategies for optimizing biomass feedstock, HTL process, and N-H analysis were proposed to promote the development in this area, with machine learning-aided prediction and regulation of N-H formation reactions showing promise.
Hydrothermal liquefaction (HTL) of nitrogen-rich biomass (N-B) is a promising valorization technology. How-ever, nitrogen heterocycles (N-H, mainly 5-/6-membered) was produced considerably in bio-oil during HTL of N-B, impeding the application of bio-oil as a fuel due to NOx emission, and the use of bio-oil containing N-H as chemical precursors is not yet satisfactory due to relative low content and selectivity. This study aims to overview the effects of biomass and HTL processing parameters on N-H distribution and transformation mechanisms of N-H during HTL of N-B. It was found that factors influencing the formation of N-H are mainly biomass composition (particularly the compositions of amino acids and monosaccharides), HTL process parameters such as temperature, and extraction conditions such as solvents and procedures. Maillard reaction between amino acids and monosaccharides as well as the chain scission, dimerization, and cyclization of amino acids are the major paths to form N-H. Strategies in engineering biomass feedstock, HTL process, and bio-oil extraction procedure, in addition to optimizing N-H analysis and N-H classification, were proposed to promote the development of this area. Amongst, the use of machine learning-aided prediction, optimization, and regulation of N-H formation reactions is promising.

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