A review on the modeling and validation of biomass pyrolysis with a focus on product yield and composition

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dc.contributor.authorXia, Changleien
dc.contributor.authorCai, Lipingen
dc.contributor.authorZhang, Haifengen
dc.contributor.authorZuo, Leien
dc.contributor.authorShi, Sheldon Q.en
dc.contributor.authorLam, Su Shiungen
dc.date.accessioned2021-04-20T19:39:27Zen
dc.date.available2021-04-20T19:39:27Zen
dc.date.issued2021en
dc.description.abstractModeling is regarded as a suitable tool to improve biomass pyrolysis in terms of efficiency, product yield, and controllability. However, it is crucial to develop advanced models to estimate products' yield and composition as functions of biomass type/characteristics and process conditions. Despite many developed models, most of them suffer from insufficient validation due to the complexity in determining the chemical compounds and their quantity. To this end, the present paper reviewed the modeling and verification of products derived from biomass pyrolysis. Besides, the possible solutions towards more accurate modeling of biomass pyrolysis were discussed. First of all, the paper commenced reviewing current models and validating methods of biomass pyrolysis. Afterward, the influences of biomass characteristics, particle size, and heat transfer on biomass pyrolysis, particle motion, reaction kinetics, product prediction, experimental validation, current gas sensors, and potential applications were reviewed and discussed comprehensively. There are some difficulties with using current pyrolysis gas chromatography and mass spectrometry (Py-GC/MS) for modeling and validation purposes due to its bulkiness, fragility, slow detection, and high cost. On account of this, the applications of Py-GC/MS in industries are limited, particularly for online product yield and composition measurements. In the final stage, a recommendation was provided to utilize high-temperature sensors with high potentials to precisely validate the models for product yield and composition (especially CO, CO2, and H-2) during biomass pyrolysis. (C) 2021 BRTeam. All rights reserved.en
dc.description.notesThis research is financially supported by the USDA NIFA Foundation Program Award (2017-67021-26138), and the Natural Science Foundation of Jiangsu Province (BK20200775). The authors are grateful to Dr. Eric Suuberg (Brown University) for his valuable suggestions throughout this work. The authors would also like to thank the Universiti Malaysia Terengganu under Golden Goose Research Grant Scheme (GGRG) (Vot 55191) for supporting Dr. Lam to perform this review project in collaboration with the Nanjing Forestry University.en
dc.description.sponsorshipUSDA NIFA Foundation [2017-67021-26138]; Natural Science Foundation of Jiangsu ProvinceNatural Science Foundation of Jiangsu Province [BK20200775]; Universiti Malaysia Terengganu under Golden Goose Research Grant Scheme (GGRG) [Vot 55191]; Nanjing Forestry Universityen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.18331/BRJ2021.8.1.2en
dc.identifier.issn2292-8782en
dc.identifier.issue1en
dc.identifier.urihttp://hdl.handle.net/10919/103061en
dc.identifier.volume8en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBioenergyen
dc.subjectLignocelluloseen
dc.subjectBiomassen
dc.subjectPyrolysisen
dc.subjectReaction kineticsen
dc.subjectSensoren
dc.titleA review on the modeling and validation of biomass pyrolysis with a focus on product yield and compositionen
dc.title.serialBiofuel Research Journal-BRJen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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