[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127962-en":3,"doc-seo-127962-105":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},127962,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine learning-based prediction and optimization of green hydrogen production technologies from water industries for a circular economy","Machine learning-based prediction and optimization of green hydrogen production (GHP) technologies addresses scaling-up issues in green hydrogen production. A techno-economic and environmental feasibility assessment identifies proton exchange membrane (PEM) and dark fermentation (DF) as the most promising and environmentally friendly pathways. Multiple ML models are applied to predict and optimize DF and PEM performance, targeting influential parameters and improving process outcomes. K-nearest neighbor and random forest models provide the best regression fit, while permutation-based and partial dependency analyses highlight key operating variables and optimal temperature and COD ranges.","Elsevier required licence: © \u003C2023> . This manuscript version is made available under the CC-BY-NC  \nND 4.0 license [http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)  \n[The definitive publisher version is available online at](The definitive publisher version is available online at 10.1016/j.desal.2023.116992)[ ](The definitive publisher version is available online at 10.1016/j.desal.2023.116992)[10.1016/j.desal.2023.116992](The definitive publisher version is available online at 10.1016/j.desal.2023.116992)  \nMachine learning-based prediction and optimization of green hydrogen production technologies from water industries for a circular economy  \nMohammad Mahbub Kabir1,2,3, Sujit Kumar Roy4, Faisal Alam3, Sang Yong Nam5, Kwang Seop Im,5, Leonard Tijing1,2, Ho Kyong Shon1,2 *  \n1ARC Research Hub for Nutrients in a Circular Economy, School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, P. O. Box 123, Broadway, NSW 2007, Australia.  \n2 Center for Technology in Water and Wastewater, School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, P. O. Box 123, Broadway, NSW 2007, Australia.  \n3Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali 3814, Bangladesh.  \n4Institute of Water and Flood Management (IWFM), Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.  \n5 Research Institute for Green Energy Convergence Technology, Department of Materials Engineering and Convergence Technology, Gyeongsang National University, Jinju, 52828, Republic of Korea.  \n*Corresponding author [Email: Hokyong.Shon-1@uts.edu.au](Email: Hokyong.Shon-1@uts.edu.au) (H.K. Shon)  \nAbstract  \nCurrently, there exists a significant number of green hydrogen production (GHP) technologies based on scaling-up issues (SCUI) . Optimal prediction and process optimization could be oneof the most substantial SCUI of GHP. Machine learning (ML)-based prediction and optimization of GHP technologies from water industries for a circular economy could be a plausible solution for these SCUI. We studied a detailed techno-economic and environmental feasibility study, which recommended proton exchange membrane (PEM) and dark fermentation (DF) as the most promising and environment-friendly technologies for GHP. Thus, the present investigation aims to apply different ML models to predict and optimize the GHP ofDF and PEM technologies to solve the SCUI. The results revealed K-nearest neighbor and random forest are the best-fitted models to predict GHP for DF and PEM, correspondingly based on the regression co-efficient (R2), root mean squared error and mean absolute error. The permutation variable index recommended that chemical oxygen demand (COD), butyrate, temperature, pH and acetate/butyrate ratio are the most influential process parameters in decreasing order for DF, while temperature, cell areas, pressure, voltage and catalysts loadings are the most effective process parameters for PEM in reducing order. The partial dependency analysis demonstrated GHP increases with increasing COD values up to 10 mg/L, and the optimal temperature range in the DF process is between 25 to 30 0C. On the other hand, cell temperature up to 35 0C should be considered optimum for PEM, and 40-70 cm2 cell areas could produce a significant GHP. In summary, the present study underscores the potential of machine learning (ML) and artificial intelligence (AI) as promising techniques for optimizing GHP, ultimately addressing scaling-up challenges in large-scale industrial GHP production and ensuring a sustainable hydrogen economy (HE) .  \nKeywords: Machine learning, green hydrogen, bibliometric analysis, partial dependency analysis, dark fermentation, proton exchange membrane.  \nGraphical abstract  \n1. Introduction  \nThe efficient functioning of our society and","cbCaidJZOcQBA38a","https://ap.wps.com/l/cbCaidJZOcQBA38a","pdf",2024098,5,1,52,"English","en",105,"# Abstract\n# 1. Introduction","[{\"question\":\"该研究解决的核心问题是什么？\",\"answer\":\"研究聚焦绿色氢气生产技术的放大难题（scaling-up issues），并通过机器学习实现预测与过程优化以提升可行性。\"},{\"question\":\"研究中被认为最有前景的两类绿色氢气制备技术是什么？\",\"answer\":\"质子交换膜（PEM）与暗发酵（DF）被推荐为最有前景且更环保的路径。\"},{\"question\":\"哪些机器学习模型表现最好？\",\"answer\":\"用于DF预测的最佳模型为K-nearest neighbor；用于PEM预测的最佳模型为random forest，依据R2、RMSE和MAE等回归指标综合判断。\"}]","Machine learning-based prediction and optimization of green hydrogen production technologies from water industries for a circular economy | PDF",1785943336,131,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-based-prediction-and-optimization-of-green-hydrogen-production-technologies-from-water-industries-for-a-circular-economy","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-based-prediction-and-optimization-of-green-hydrogen-production-technologies-from-water-industries-for-a-circular-economy/127962/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"该研究解决的核心问题是什么？","Question",{"text":77,"@type":78},"研究聚焦绿色氢气生产技术的放大难题（scaling-up issues），并通过机器学习实现预测与过程优化以提升可行性。","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"研究中被认为最有前景的两类绿色氢气制备技术是什么？",{"text":82,"@type":78},"质子交换膜（PEM）与暗发酵（DF）被推荐为最有前景且更环保的路径。",{"name":84,"@type":75,"acceptedAnswer":85},"哪些机器学习模型表现最好？",{"text":86,"@type":78},"用于DF预测的最佳模型为K-nearest neighbor；用于PEM预测的最佳模型为random forest，依据R2、RMSE和MAE等回归指标综合判断。","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]