2021
DOI: 10.1002/cjce.24071
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Data‐driven hypotheses of reaction networks for thermochemical conversion of a physical mixture of levoglucosan and 2‐phenoxyethyl benzene

Abstract: In this work, we analyze the hydrous pyrolysis of a physical mixture of the model components representing cellulose (levoglucosan) and lignin (2-phenoxyethyl benzene). Fourier transform infrared (FTIR) and proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy was used to characterize the products of the reaction. The main objective of the work was to use datadriven methods to develop a reaction network for this system based on the spectroscopic data. This was achieved using Bayesian hierarchical clustering… Show more

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Cited by 3 publications
(10 citation statements)
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“…These networks also seemed to match with the general trend noticed in the hydrolysis of biomass. 14,15,83 It is also interesting to note that the candidate molecules identified in each step closely resemble model compounds presented in the literature for the thermal pyrolysis of biomass. 6,84 4.2.…”
Section: Reaction Network For a Known Starting Moleculesupporting
confidence: 62%
“…These networks also seemed to match with the general trend noticed in the hydrolysis of biomass. 14,15,83 It is also interesting to note that the candidate molecules identified in each step closely resemble model compounds presented in the literature for the thermal pyrolysis of biomass. 6,84 4.2.…”
Section: Reaction Network For a Known Starting Moleculesupporting
confidence: 62%
“…If synergistic effects occur, then predicting a biomass feedstock’s behavior would be considerably more complex. In our previous work, , cellulose and lignin independently underwent HTL conversion using their model components: levoglucosan and 2-phenoxy-ethyl benzene, respectively, and their most probable BNs were developed. Figure shows those networks along with the BN for the HTL of biomass developed in this work.…”
Section: Resultsmentioning
confidence: 99%
“…Data fusion is a productive means of combining such independent measurements to reveal shared as well as distinct information. In this work, a signal-level data fusion approach was followed to concatenate measurements from the two spectroscopic devices. , The data was scaled and normalized with respect to minimum peak intensity prior to fusion, along with dimensional reduction (removal of uninformative variables/wavenumbers) using principal component analysis. The resultant signal had a better signal-to-noise ratio compared to the raw data from the individual signals.…”
Section: Methodsmentioning
confidence: 99%
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