[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122596-en":3,"doc-seo-122596-105":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},122596,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Review of Explainable Machine Learning for Anaerobic Digestion - Research paper summary","Anaerobic digestion (AD) supports sustainable waste management by recovering valuable resources from organic waste, while its performance depends on design and operating conditions. The review surveys recent advances in black-box machine learning and soft computing for AD modeling, emphasizing both global and local interpretability metrics such as Shapley values, partial dependence analysis, and permutation feature importance. It also covers scenario analysis, fault detection, long-term operation prediction, and ML integration with life cycle assessment, concluding with key research gaps and future directions.","Gupta, R. , Zhang, L., Hou, J., Zhang, Z., Liue, H., You, S. , Okh, Y.  \nS. and Li, W. (2023) Review of explainable machine learning for anaerobic digestion. Bioresource Technology, 369,  \n128468. (doi:  10.1016/j.biortech.2022.128468)  \nThis is the author version of the work deposited here under a Creative Commons license: [http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)  \n[Copyright](Copyright) © 2022 Elsevier Ltd.  \nThere may be differences between this version and the published version. You are advised to consult the published version if you wish to cite from it:  \n[https://doi.org/10.1016/j.biortech.2022.128468](https://doi.org/10.1016/j.biortech.2022.128468)  \n[https://eprints.gla.ac.uk/286299/](https://eprints.gla.ac.uk/286299/)  \nDeposited on 06 December 2022  \nEnlighten – Research publications by members of the University of Glasgow  \n[http://eprints.gla.ac.uk](http://eprints.gla.ac.uk)  \n1 Review of Explainable Machine Learning for Anaerobic Digestion  \n2 Rohit Guptaa, b, c, \\#, Le Zhangd, \\#, Jiayi Houe, Zhikai Zhangf,g, Hongtao Liue, Siming Youa, Yong Sik Okh,  \n3 Wangliang Lif,*  \n4 a James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK  \n5 b Nanoengineered Systems Laboratory, UCL Mechanical Engineering, University College London, London  \n6 WC1E 7JE, UK  \n7 c Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London  \n8 W1W 7TS, UK  \n9 d Department of Resources and Environment, School of Agriculture and Biology, Shanghai Jiao Tong  \n10 University, 800 Dongchuan Road, Shanghai 200240, China  \n11 e Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing  \n12 100101, China  \n13 f CAS Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy  \n14 of Sciences, Beijing 100190, China  \n15 g School of Water Resources and Environment, Hebei GEO University, Shijiazhuang 050031, Hebei, China  \n16 h Korea Biochar Research Center, APRU Sustainable Waste Management Program & Division of  \n17 Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea  \n18  \\# The first two authors contributed equally to this work.  \n19  *Corresponding author. Wangliang Li, [wlli@ipe.ac.cn](wlli@ipe.ac.cn)  \n20  Submitted to Bioresource Technology (November 2022)  \n1 Abstract  \n2 Anaerobic digestion (AD) is a promising technology for recovering value-added resources  \n3 from organic waste, thus achieving sustainable waste management. The performance of AD is  \n4 dictated by a variety of factors including system design and operating conditions. This  \n5 necessitates developing suitable modelling and optimization tools to quantify its off-design  \n6 performance, where the application of machine learning (ML) and soft computing approaches  \n7 have received increasing attention. Here, we succinctly reviewed the latest progress in black-  \n8 box ML approaches for AD modelling with a thrust on global and local model interpretability  \n9 metrics (e.g., Shapley values, partial dependence analysis, permutation feature importance) .  \n10 Categorical applications of the ML and soft computing approaches such as what-if scenario  \n11 analysis, fault detection in AD systems, long-term operation prediction, and integration of ML  \n12 with life cycle assessment are discussed. Finally, the research gaps and scopes for future work  \n13 are summarized.  \n14 Research Highlights  \n15 • Popularly used ML-based AD models are ANN, SVM, RF, and XGBOOST  \n16 • Predicted variables are biogas yield, process stability, and effluent characteristics  \n17 • Global and local model-agnostic explainability approaches are reviewed  \n18 • Potential applications are process parameter optimization, fault detection, and LCA  \n19 • It is necessary to inform ML models with biokinetic equations to improve accuracy  \n20 Keywords: Data-driven Modelling; S","cbCaimUPAUx4EaOR","https://ap.wps.com/l/cbCaimUPAUx4EaOR","pdf",471937,1,48,"English","en",105,"# Abstract\n# Research Highlights\n# 1. Introduction\n## Anaerobic digestion and its influencing parameters\n## Mechanistic models and ADM1","[{\"question\":\"Why is anaerobic digestion important in sustainable waste management?\",\"answer\":\"Anaerobic digestion recovers value-added resources from organic waste, enabling sustainable waste management and renewable bioenergy. Its performance is shaped by multiple design and operating factors.\"},{\"question\":\"What interpretability approaches are highlighted for machine learning AD models?\",\"answer\":\"The review focuses on global and local model-agnostic explainability metrics, including Shapley values, partial dependence analysis, and permutation feature importance.\"},{\"question\":\"Which future-work directions and applications are discussed?\",\"answer\":\"Applications include process parameter optimization, fault detection, and integrating ML with life cycle assessment. The review also notes the need to inform ML models with biokinetic equations to improve accuracy, while summarizing research gaps and future scopes.\"}]","Review of Explainable Machine Learning for Anaerobic Digestion - Research paper summary | PDF",1785811651,121,{"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},"review-of-explainable-machine-learning-for-anaerobic-digestion-research-paper-summary","",{"@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/review-of-explainable-machine-learning-for-anaerobic-digestion-research-paper-summary/122596/",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","2026-08-04",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 anaerobic digestion important in sustainable waste management?","Question",{"text":75,"@type":76},"Anaerobic digestion recovers value-added resources from organic waste, enabling sustainable waste management and renewable bioenergy. Its performance is shaped by multiple design and operating factors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What interpretability approaches are highlighted for machine learning AD models?",{"text":80,"@type":76},"The review focuses on global and local model-agnostic explainability metrics, including Shapley values, partial dependence analysis, and permutation feature importance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which future-work directions and applications are discussed?",{"text":84,"@type":76},"Applications include process parameter optimization, fault detection, and integrating ML with life cycle assessment. The review also notes the need to inform ML models with biokinetic equations to improve accuracy, while summarizing research gaps and future scopes.","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"]