[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120712-en":3,"doc-seo-120712-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120712,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Thermochemical Biofuel Conversion Processes with Machine Learning - Recent Advances and Future Prospects","Biofuels are widely regarded as viable responses to the climate crisis while also strengthening energy security and long-term sustainability. Remaining barriers stem from techno-economic and environmental uncertainties, together with complex conversion mechanisms influenced by materials and process design. Machine learning has been integrated with thermochemical biofuel conversion theories to enable accurate, efficient process modeling. This review critically examines machine-learning applications for predicting biofuel yield and composition, compares model input/output variables and development workflows, and summarizes related techno-economic analyses, while proposing universal, data-driven directions for future research and practical commercialization.","1 Recent advances and future prospects of thermochemical biofuel conversion  \n2 processes with machine learning  \n3  \n4 Pil Rip Jeona,b,1, Jong-Ho Moonc,1, Ogunsola Nafiu Olanrewajud,1, See Hoon Leed,e,1,  \n5 Jester Lih Jie Linge, Siming Youf, Young-Kwon Parkg,* 6  \n7 a Department of Chemical Engineering, Kongju National University, Cheonan-daero 12  \n8 23-24, Seobuk-gu, Cheonan-si, Chungcheongnam-do 31080, Republic of Korea  \n9 b Department of Future Convergence Engineering, Kongju National University, Cheona 10 n-daero 1223-24, Seobuk-gu, Cheonan-si, Chungcheongnam-do 31080, Republic of Kor  \n11 ea  \n12 c Department of Chemical Engineering, Chungbuk National University, Chungdae-ro 1, 13 Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea  \n14 d Department of Mineral Resources & Energy Engineering, Jeonbuk National Universit 15 y, 567 Baekje-daero, Deokjin-gu, Jeonju, Jeonbuk 54896, Republic of Korea  \n16 e Department of Environment & Energy, Jeonbuk National University, 567 Baekje-daer 17 o, Deokjin-gu, Jeonju, Jeonbuk 54896, Republic of Korea  \n18 f James Watt School of Engineering, University of Glasgow, G12 8QQ, UK  \n19 g School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of  \n20 Korea  \n21  \n22 1 Co-first authors  \n23 * Corresponding author: [parkyk@uos.ac.kr](parkyk@uos.ac.kr), [catalica@uos.ac.kr](catalica@uos.ac.kr)  \n24  \n25  \n26  \n27 ABSTRACT 28  \n29 Biofuels have been widely recognized as potential solutions to addressing the climate crisis 30 and strengthening energy security and sustainability. However, techno-economic and 31 environmental challenges for the production of biofuels remain and complicated conversion 32 processes and factors, such as materials and process design, need to be taken into consideration 33 for solving the challenges, which is not easy. Machine Learning (ML) has been combined with 34 the theories of thermochemical biofuel conversion processes to achieve accurate and efficient 35 biofuel process modelling. In this review, existing ML applications to predict biofuel yield and 36 composition are critically reviewed. The details of the input and output variables of the 37 developed models for thermochemical biofuel conversion processes were summarized, and 38 their development procedures were compared. Techno-economic analysis results incorporating 39 ML applications in biofuels were also reviewed. Although developed models in literature 40 showed good performance for their targets, respectively, they can hardly be applied to other 41 feedstocks or operating conditions. To overcome the challenge and develop universal model, 42 perspective approaches were suggested in this study. It was suggested that it is essential to 43 develop systematic datasets to support more comprehensive machine learning-based modelling 44 towards practical applications. Potential prospective research and development directions on 45 machine learning-based thermochemical biofuel conversion process modeling were 46 recommended, so that it can assist in the commercialization and optimization of various biofuel 47 conversions leading to a sustainable and circular society.  \n48  \n49 Keywords: Thermochemical conversion processes; Theory-integrated machine learning;  \n50 Biofuel conversion; Techno-economic analysis  \n51 LIST OF ABBREVIATIONSAND NOMENCLATURE 52  \n53 ABC Artificial bee colony  \n54 AC Ash content  \n55 ACP Aqueous co-product  \n56 AI Artificial intelligence  \n57 ANFIS Adaptive neuro-fuzzy inference system  \n58 ANN Artificial neural network  \n59 ANN-PSO Artificial neural network-particle swarm optimization  \n60 BC Biomass composition  \n61 CART Classification and regression tree  \n62 Ce Cellulose content  \n63 CFD Computational fluid dynamics  \n64 CFNN Cascade forward neural network  \n65 CHP Combined heat and power  \n66 CR Carbon recovery  \n67 DCD Decarboxylation degree  \n68 DCF Discounted cash flow  \n69 DHD Dehydration degree  \n70 DNN Deep neural networks  \n71 DT Decision trees  \n7","cbCaijdJqWJYuqKX","https://ap.wps.com/l/cbCaijdJqWJYuqKX","pdf",1332227,1,51,"English","en",105,"# Abstract\n# Keywords\n# List of Abbreviations and Nomenclature","[{\"question\":\"Why are thermochemical biofuel conversion challenges difficult to address?\",\"answer\":\"Techno-economic and environmental challenges persist, and conversion processes are complicated by factors like feedstock materials and process design.\"},{\"question\":\"How is machine learning used in thermochemical biofuel conversion modeling?\",\"answer\":\"Machine learning is combined with thermochemical conversion theories to build accurate and efficient models for predicting biofuel yield and composition.\"},{\"question\":\"What limitation exists for existing machine learning models in the literature?\",\"answer\":\"Models may perform well for specific targets, but they are difficult to generalize to different feedstocks or operating conditions.\"}]","Thermochemical Biofuel Conversion Processes with Machine Learning - Recent Advances and Future Prospects | PDF",1785731665,129,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"thermochemical-biofuel-conversion-processes-with-machine-learning-recent-advances-and-future-prospects","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/thermochemical-biofuel-conversion-processes-with-machine-learning-recent-advances-and-future-prospects/120712/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are thermochemical biofuel conversion challenges difficult to address?","Question",{"text":75,"@type":76},"Techno-economic and environmental challenges persist, and conversion processes are complicated by factors like feedstock materials and process design.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is machine learning used in thermochemical biofuel conversion modeling?",{"text":80,"@type":76},"Machine learning is combined with thermochemical conversion theories to build accurate and efficient models for predicting biofuel yield and composition.",{"name":82,"@type":73,"acceptedAnswer":83},"What limitation exists for existing machine learning models in the literature?",{"text":84,"@type":76},"Models may perform well for specific targets, but they are difficult to generalize to different feedstocks or operating conditions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]