[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128725-en":3,"doc-seo-128725-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128725,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis - read online free","Energy shortages and global warming drive the need for sustainable renewable energy, with biomass emerging as a key feedstock for hydrothermal liquefaction that upgrades wet biomass into bio-crude oil. Because hydrothermal liquefaction is complex and difficult to model mechanistically, machine learning approaches are used to predict outputs and assess the influence of process variables. This study analyzes Scopus publications by extracting indexed ML and HTL terms to reveal keyword associations and co-citations. Results show a rising focus on ML for HTL, led largely by engineering publications.","RESEARCH ARTICLE  \nCurrent scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis  \n[version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved]  \nTossapon Katongtung 1,2, Somboon Sukpancharoen3, Sakprayut Sinthupinyo4, Nakorn Tippayawong 1  \n1 Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand  \n2Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand  \n3 Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand  \n4Siam Research and Innovation Co., Ltd, Bangkok, Thailand  \nv3  \nFirst published: 04 Oct 2024, 13:1131  \n[https://doi.org/10.12688/f1000research.156514.1](https://doi.org/10.12688/f1000research.156514.1)  \nSecond version: 02 Jan 2025, 13:1131  \n[https://doi.org/10.12688/f1000research.156514.2](https://doi.org/10.12688/f1000research.156514.2)  \n[Latest published:](Latest published: 11 Mar 2025)[ 11 Mar 2025](Latest published: 11 Mar 2025), 13:1131  \n[https://doi.org/10.12688/f1000research.156514.3](https://doi.org/10.12688/f1000research.156514.3)  \nAbstract  \nBackground  \nEnergy shortages and global warming have been significant issues throughout history. Therefore, the search for environmentally friendly renewable energy sources is crucial for achieving sustainability. Biomass energy is gaining global attention as a renewable energy option, particularly through the process of hydrothermal liquefaction, which converts wet biomass into bio-crude oil.  \nMethods  \nHydrothermal liquefaction is a complex process that is challenging to explain, leading to research on machine learning models for this process. These models aim to predict values and investigate the impact of variables on the hydrothermal liquefaction process. These models aim to predict values and investigate the impact of variableson the hydrothermal liquefaction process. However, the development of machine learning in hydrothermal liquefaction is still limited due to its novelty and the time required for comprehensive study. Thus, the objective of this study was to analyze relevant publications in the Scopus database, focusing on indexed ML and HTL keywords, to understand keyword associations and co-citations.  \nOpen Peer Review  \n\n| Approval Status  |  |  |  |  |\n| --- | --- | --- | --- | --- |\n| 1 |  | 2 | 3 | 4 |\n| version 3\u003Cbr>(revision)\u003Cbr>11 Mar 2025\u003Cbr>version 2\u003Cbr>(revision)\u003Cbr>02 Jan 2025\u003Cbr>version 1\u003Cbr>04 Oct 2024 | \u003Cbr>view\u003Cbr>\u003Cbr>view view |  |  |  |\n|  |  |  |  |  |\n|  | view view |  |  |  |\n\n1. Lili Qian, Jiangsu University, Zhenjiang, China  \n2. Muntasir Shahabuddin, Worcester Polytechnic Institute, Worcester, USA Andrew Charlebois, Worcester Polytechnic Institute, Worcester, USA  \n3. Venu Babu Borugadda, University of Saskatchewan, Saskatoon, Canada  \n4. Muhammad Raziq Rahimi Kooh , Universiti Brunei Darussalam, Darussalam, Brunei  \nAny reports and responses or comments on the  \nResults  \nThe results reveal an increasing trend in the study of ML in the HTL process, with a growing interest from various countries.  \nConclusion  \nNotably, China currently holds the largest share of ML research in HTL processes, with most published works falling within the field of engineering. The keyword “liquefaction” emerges as the most popular term in these publications.  \nKeywords  \nAI, Clean energy, Data analytics, Climate action, bibliometric analysis  \n This article is included in the Energy gateway.  \narticle can be found at the end of the article.  \nCorresponding author: Nakorn Tippayawong ([n.tippayawong@yahoo.com](n.tippayawong@yahoo.com))  \nAuthor roles: Katongtung T: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Validation, Visualization, Writing – Original Draft Preparation; Sukpancharoen S: Formal Analysis, Writing – Review & Editing; Sinthupinyo S: Formal Analysi","cbCaionwhOPYNiF8","https://ap.wps.com/l/cbCaionwhOPYNiF8","pdf",1499085,5,1,24,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"Why is machine learning used for hydrothermal liquefaction?\",\"answer\":\"Hydrothermal liquefaction is described as complex and challenging to explain directly, so machine learning models are used to predict values and evaluate how process variables affect outcomes.\"},{\"question\":\"How does the study conduct its bibliometric analysis?\",\"answer\":\"It analyzes relevant publications in the Scopus database, focusing on indexed machine learning and hydrothermal liquefaction keywords to study keyword associations and co-citations.\"},{\"question\":\"Which country and research area dominate current ML work on HTL?\",\"answer\":\"China has the largest share of ML research in hydrothermal liquefaction, and most published works fall within engineering.\"}]","Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis - 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