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Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes
Jiangsu Province Key Laboratory of Biomass Energy and Materials, National Engineering Laboratory for Biomass Chemical Utilization, Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry (CAF), 210042, Nanjing, China; Jiangsu Co-Innovation Center for Efficient Processing and Utilization of Forest Resources, College of Chemical Engineering, Nanjing Forestry University, Longpan Road 159, 210037, Nanjing, China.
Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore.
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Process.
Jiangsu Province Key Laboratory of Biomass Energy and Materials, National Engineering Laboratory for Biomass Chemical Utilization, Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry (CAF), 210042, Nanjing, China; Jiangsu Co-Innovation Center for Efficient Processing and Utilization of Forest Resources, College of Chemical Engineering, Nanjing Forestry University, Longpan Road 159, 210037, Nanjing, China.
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2023 (English)In: Communications Chemistry, E-ISSN 2399-3669, Vol. 6, no 1, article id 273Article in journal (Refereed) Published
Abstract [en]

Feedstock properties play a crucial role in thermal conversion processes, where understanding the influence of these properties on treatment performance is essential for optimizing both feedstock selection and the overall process. In this study, a series of van Krevelen diagrams were generated to illustrate the impact of H/C and O/C ratios of feedstock on the products obtained from six commonly used thermal conversion techniques: torrefaction, hydrothermal carbonization, hydrothermal liquefaction, hydrothermal gasification, pyrolysis, and gasification. Machine learning methods were employed, utilizing data, methods, and results from corresponding studies in this field. Furthermore, the reliability of the constructed van Krevelen diagrams was analyzed to assess their dependability. The van Krevelen diagrams developed in this work systematically provide visual representations of the relationships between feedstock and products in thermal conversion processes, thereby aiding in optimizing the selection of feedstock and the choice of thermal conversion technique.

Place, publisher, year, edition, pages
Springer Nature , 2023. Vol. 6, no 1, article id 273
National Category
Energy Engineering
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URN: urn:nbn:se:kth:diva-341602DOI: 10.1038/s42004-023-01077-zISI: 001122502600001Scopus ID: 2-s2.0-85179331588OAI: oai:DiVA.org:kth-341602DiVA, id: diva2:1822619
Note

QC 20231227

Available from: 2023-12-27 Created: 2023-12-27 Last updated: 2024-02-29Bibliographically approved

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Shi, ZiyiZaini, Ilman NuranJagodzińska, KatarzynaJönsson, PärYang, Weihong

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