Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes

Shule Wang; Yiying Wang; Ziyi Shi; Kang Sun; Yuming Wen; Lukasz Niedzwiecki; Ruming Pan; Yongdong Xu; Ilman Nuran Zaini; Katarzyna Jagodzińska; Christian Aragon-Briceno; Chuchu Tang; Thossaporn Onsree; Nakorn Tippayawong; Halina Pawlak-Kruczek; Pär Göran Jönsson; Weihong Yang; Jianchun Jiang; Sibudjing Kawi; Chi-Hwa Wang

doi:10.1038/s42004-023-01077-z

Van Krevelen diagrams based on machine learning visualize feedstock-product relationships in thermal conversion processes

Commun Chem. 2023 Dec 13;6(1):273. doi: 10.1038/s42004-023-01077-z.

Authors

Shule Wang^#^{1

2}, Yiying Wang^#³, Ziyi Shi^#⁴, Kang Sun^#^{1

2}, Yuming Wen⁵, Lukasz Niedzwiecki^{6

7}, Ruming Pan^{8

9}, Yongdong Xu¹⁰, Ilman Nuran Zaini⁴, Katarzyna Jagodzińska⁴, Christian Aragon-Briceno¹¹, Chuchu Tang¹², Thossaporn Onsree¹³, Nakorn Tippayawong¹⁴, Halina Pawlak-Kruczek⁶, Pär Göran Jönsson⁴, Weihong Yang⁴, Jianchun Jiang^{15

16}, Sibudjing Kawi¹⁷, Chi-Hwa Wang¹⁸

Affiliations

¹ 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.
² 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.
³ Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore.
⁴ Department of Materials Science and Engineering, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden.
⁵ Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore. yuming@nus.edu.sg.
⁶ Department of Energy Conversion Engineering, Wroclaw University of Science and Technology, 27 wybrzeże Stanisława Wyspiańskiego st. 50-370, Wroclaw, Poland.
⁷ Energy Research Centre, Centre for Energy and Environmental Technologies, VŠB-Technical University of Ostrava, 708 00, Ostrava, Poruba, Czech Republic.
⁸ School of Energy Science and Engineering, Harbin Institute of Technology, 150001, Harbin, China.
⁹ Institut de Mécanique des Fluides de Toulouse (IMFT) - Université de Toulouse, CNRS-INPT-UPS, 31400, Toulouse, France.
¹⁰ Laboratory of Environment-Enhancing Energy (E2E), Key Laboratory of Agricultural Engineering in Structure and Environment of Ministry of Agriculture and Rural Affairs, China Agricultural University, 100083, Beijing, China.
¹¹ Department of Industry and Energy, CIRCE-Research Centre for Energy Resources and Consumption, 50018, Zaragoza, Spain.
¹² Faculty of Creative Arts, University of Malaya, 50603, Kuala Lumpur, Malaysia.
¹³ Department of Chemical Engineering, University of South Carolina, 301 Main St, Columbia, SC, 29208, USA.
¹⁴ Department of Mechanical Engineering, Chiang Mai University, 239 Huay Kaew Rd., Muang District, Chiang Mai, 50200, Thailand.
¹⁵ 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. jiangjc@caf.ac.cn.
¹⁶ 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. jiangjc@caf.ac.cn.
¹⁷ Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore. chekawis@nus.edu.sg.
¹⁸ Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore. chewch@nus.edu.sg.

^# Contributed equally.

Abstract

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.

Abstract

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