[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116864-en":3,"doc-seo-116864-105":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},116864,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine learning in biohydrogen production - a review","Biohydrogen is an emerging carbon-neutral, sustainable energy carrier with high energy yield, offering a route to replace conventional fossil fuels. Commercial adoption is constrained mainly on the supply side, making optimization of key operating parameters essential for large-scale uptake. Machine learning algorithms can process large datasets with reduced system-specific knowledge and adapt to changing conditions. This review critically evaluates how machine learning categorizes and predicts biohydrogen production data, reports the accuracy of different algorithms, and discusses practical implications for transportation-sector deployment.","Biofuel Research Journal 38 (2023) 1844-1858  \nJournal [homepage: www.biofueljournal.com](homepage: www.biofueljournal.com)  \n| Review Paper\u003Cbr>Machine learning in biohydrogen production: a review\u003Cbr>Avinash Alagumalai 1,‡, Balaji Devarajan2,‡, Hua Song3, *, Somchai Wongwises4,5, Rodrigo Ledesma-Amaro6, Omid Mahian7,8,9, *, Mikhail Sheremet9, Eric Lichtfouse 10\u003Cbr>1 Department of Mechanical Engineering, GMR Institute of Technology, Rajam-532127, Andhra Pradesh, India.\u003Cbr>2 Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India.\u003Cbr>3 Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4, Canada.\u003Cbr>4 Fluid Mechanics, Thermal Engineering and Multiphase Flow Research Lab. (FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangmod, Bangkok 10140, Thailand.\u003Cbr>5 National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand.\u003Cbr>6 Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, UK.\u003Cbr>7 School of Chemical Engineering and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.\u003Cbr>8 Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.\u003Cbr>9 Laboratory on Convective Heat and Mass Transfer, Tomsk State University, 634050 Tomsk, Russia.\u003Cbr>10 State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi,710049 P.R. China. |  |\n| --- | --- |\n| HIGHLIGHTS GRAPHICAL ABSTRACT |  |\n| ➢The role of machine learning (ML) in biohydrogen production is detailed.\u003Cbr>➢ML can predict complex data and identify patterns in biohydrogen production.\u003Cbr>➢The patent landscape suggests promising potential for biohydrogen to replace fossil fuels.\u003Cbr>➢Improving ML predictive performance in biohydrogen production is a future need. |  |\n\n1845  \nAlagumalai et al. / Biofuel Research Journal 38 (2023) 1844-1858  \n\n| ARTICLE INFO | ABSTRACT\u003Cbr>Biohydrogen is emerging as a promising carbon-neutral and sustainable energy carrier with high energy yield to replace conventional fossil fuels. However, biohydrogen commercial uptake is mainly hindered by the supply side. As a result, various operating parameters must be optimized to realize biohydrogen commercial uptake on a large-scale. Recently, machine learning algorithms have demonstrated the ability to handle large amounts of data while requiring less in-depth knowledge of the system and being capable of adapting to evolving circumstances. This review critically reviews the role of machine learning in categorizing and predicting data related to biohydrogen production. The accuracy and potential of different machine learning algorithms are reported. Also, the practical implications of machine learning models to realize biohydrogen uptake by the transportation sector are discussed. The review indicates that machine learning algorithms can successfully model non-linear and complex interactions between operational and performance parameters in biohydrogen production. Additionally, machine learning algorithms can help researchers identify the most efficient methods for producing biohydrogen, leading to a more sustainable and cost-effective energy source.\u003Cbr>© 2023 BRTeam. All rights reserved. |\n| --- | --- |\n| Article history:\u003Cbr>Received 28 February 2023\u003Cbr>Received in revised form 2 April 2023 Accepted 13 May 2023\u003Cbr>Published 1 June 2023 |  |\n| Keywords:\u003Cbr>Waste Biofuel\u003Cbr>Biohydrogen\u003Cbr>Fermentation Machine learning\u003Cbr>Patent landscape |  |\n\nContents  \n1. Introduction............................................................................................................................................................................................................................ 1845  \n2. Importance of biohydrogen......................","cbCaiqWSYYKzmxhn","https://ap.wps.com/l/cbCaiqWSYYKzmxhn","pdf",5317046,1,15,"English","en",105,"# Contents\n## 1. Introduction\n## 2. Importance of biohydrogen\n## 3. Overview of biohydrogen production\n## 3.1. Dark fermentation\n## 3.2. Photofermentation\n## 3.3. Other methods\n## 4. Machine learning models in biohydrogen production\n## 5. Applications of machine learning to optimize biohydrogen production\n## 5.1. Biohydrogen production from wastewater\n## 5.2. Biohydrogen production from fatty acids","[{\"question\":\"Why is biohydrogen commercial adoption currently difficult?\",\"answer\":\"Commercial uptake is mainly hindered by the supply side, requiring optimization of operating parameters to enable large-scale adoption.\"},{\"question\":\"What role does machine learning play in biohydrogen production?\",\"answer\":\"Machine learning can categorize and predict data, model non-linear interactions between operational and performance parameters, and improve identification of efficient production methods.\"},{\"question\":\"How does the review position ML for real-world use?\",\"answer\":\"It reports the accuracy and potential of different ML algorithms and discusses practical implications for enabling biohydrogen uptake in the transportation sector.\"}]","Machine learning in biohydrogen production - a review | PDF",1785672134,38,{"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},"machine-learning-in-biohydrogen-production-a-review","",{"@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/machine-learning-in-biohydrogen-production-a-review/116864/",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","2026-08-02",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 is biohydrogen commercial adoption currently difficult?","Question",{"text":75,"@type":76},"Commercial uptake is mainly hindered by the supply side, requiring optimization of operating parameters to enable large-scale adoption.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does machine learning play in biohydrogen production?",{"text":80,"@type":76},"Machine learning can categorize and predict data, model non-linear interactions between operational and performance parameters, and improve identification of efficient production methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the review position ML for real-world use?",{"text":84,"@type":76},"It reports the accuracy and potential of different ML algorithms and discusses practical implications for enabling biohydrogen uptake in the transportation sector.","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"]