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, Łukasz Niedźwiecki, Ruming Pan, Yongdong Xu, Ilman Nuran Zaini, Katarzyna Jagodzińska, Christian Aragón-Briceño, Chuchu Tang, Thossaporn Onsree, Nakorn Tippayawong, Halina Pawlak-Kruczek, Pär G. Jönsson, Weihong Yang, Jianchun Jiang, Sibudjing Kawi, Chi‐Hwa Wang
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.