Evidence map›Paper›PMID 38087001›Full record

ArticleCommunications chemistry2023

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 and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Comprehensive Mapping of Compositional Dynamics in the Formose Reaction Network.Angewandte Chemie (International ed. in English) · 2026
    Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Shule Wang *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.ORCID http://orcid.org/0000-0002-0728-8671
Yiying Wang *Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore.
Ziyi Shi *Department of Materials Science and Engineering, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden.
Kang Sun *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.
Yuming WenDepartment of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore. yuming@nus.edu.sg.ORCID http://orcid.org/0000-0003-4946-6525
Lukasz NiedzwieckiDepartment of Energy Conversion Engineering, Wroclaw University of Science and Technology, 27 wybrzeże Stanisława Wyspiańskiego st. 50-370, Wroclaw, Poland.ORCID http://orcid.org/0000-0001-7045-0785
Ruming PanSchool of Energy Science and Engineering, Harbin Institute of Technology, 150001, Harbin, China.ORCID http://orcid.org/0000-0003-2146-4472
Yongdong XuLaboratory 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.
Ilman Nuran ZainiDepartment of Materials Science and Engineering, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden.
Katarzyna JagodzińskaDepartment of Materials Science and Engineering, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden.
Christian Aragon-BricenoDepartment of Industry and Energy, CIRCE-Research Centre for Energy Resources and Consumption, 50018, Zaragoza, Spain.
Chuchu TangFaculty of Creative Arts, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Thossaporn OnsreeDepartment of Chemical Engineering, University of South Carolina, 301 Main St, Columbia, SC, 29208, USA.
Nakorn TippayawongDepartment of Mechanical Engineering, Chiang Mai University, 239 Huay Kaew Rd., Muang District, Chiang Mai, 50200, Thailand.
Halina Pawlak-KruczekDepartment of Energy Conversion Engineering, Wroclaw University of Science and Technology, 27 wybrzeże Stanisława Wyspiańskiego st. 50-370, Wroclaw, Poland.
Pär Göran JönssonDepartment of Materials Science and Engineering, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden.
Weihong YangDepartment of Materials Science and Engineering, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden.
Jianchun JiangJiangsu 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.ORCID http://orcid.org/0009-0006-9046-1645
Sibudjing KawiDepartment of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore. chekawis@nus.edu.sg.ORCID http://orcid.org/0000-0002-0710-8423
Chi-Hwa WangDepartment of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore. chewch@nus.edu.sg.

Funding

Agency for Science, Technology and Research (A*STAR) U2102d2011Nanjing Forestry University (NFU) Research Fund for High-level Talents IntroductionSvenska Forskningsrådet Formas (Swedish Research Council Formas) RECOWATDIG
6 · The paper itself

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.

Identifiers

PMID38087001
PMCPMC10716171

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.