[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116985-en":3,"doc-seo-116985-105":30,"detail-sidebar-cat-0-en-105":83},{"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},116985,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion","Machine learning in anaerobic digestion is rapidly expanding because it supports interpretation of complex process parameters for operation and optimization. This systematic literature review examines how ML is currently used in anaerobic digestion and clarifies implementation challenges and the benefits of integrating ML approaches. Studies draw on both laboratory and industry-scale datasets, yet generalization is hindered by diverse system designs and monitoring equipment, along with variability in bacterial communities and operating conditions. Commonly used artificial neural networks face scalability and interpretability limits, while real-time predictive modeling can improve stability and efficiency in biogas production and waste treatment. By reviewing ML methods from broader applied areas, the work identifies directions for future research.","bioengineering  \nReview  \nA Systematic Review of Machine-Learning Solutions in Anaerobic Digestion  \nHarvey Rutland 1, *, Jiseon You 2, Haixia Liu 3, Larry Bull 3 and Darren Reynolds 4  \nCitation: Rutland, H.; You, J.; Liu, H.; Bull, L.; Reynolds, D. A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion. Bioengineering 2023, 10, 1410 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)bioengineering10121410  \nAcademic Editor: Giovanni Esposito  \nReceived: 9 November 2023  \nRevised: 2 December 2023  \nAccepted: 4 December 2023  \nPublished: 11 December 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, University of Bristol, Bristol BS8 1QU, UK  \n2 School of Engineering, University of the West of England, Bristol BS16 1QY, UK; [jiseon.you@uwe.ac.uk](jiseon.you@uwe.ac.uk)  \n3 School of Computing and Creative Technologies, University of the West of England, Bristol BS16 1QY, UK; [haixia.liu@uwe.ac.uk](haixia.liu@uwe.ac.uk) (H.L.); [larry.bull@uwe.ac.uk](larry.bull@uwe.ac.uk) (L.B.)  \n4 School of Applied Sciences, University of the West of England, Bristol BS16 1QY, UK; [darren.reynolds@uwe.ac.uk](darren.reynolds@uwe.ac.uk)  \n* Correspondence: [zq21170@bristol.ac.uk](zq21170@bristol.ac.uk)  \nAbstract: The use of machine learning (ML) in anaerobic digestion (AD) is growing in popularity and improves the interpretation of complex system parameters for better operation and optimisation. This systematic literature review aims to explore how ML is currently employed in AD, with particular attention to the challenges of implementation and the benefits of integrating ML techniques. While both lab and industry-scale datasets have been used for model training, challenges arise from varied system designs and the different monitoring equipment used. Traditional machine-learning techniques, predominantly artificial neural networks (ANN), are the most commonly used but facedifficulties in scalability and interpretability. Specifically, models trained on lab-scale data often struggle to generalize to full-scale, real-world operations due to the complexity and variability in bacterial communities and system operations. In practical scenarios, machine learning can be employed in real-time operations for predictive modelling, ensuring system stability is maintained, resulting in improved efficiency of both biogas production and waste treatment processes. Through reviewing the ML techniques employed in wider applied domains, potential future research opportunities in addressing these challenges have been identified.  \nKeywords: machine learning; deep learning; anaerobic digestion  \n1. Introduction  \nAnaerobic digestion (AD) is a biological process where microorganisms break down biodegradable material in the absence of oxygen, resulting in biogas production—a mixture of methane, carbon dioxide, and trace gases. This biogas serves as a renewable energy source, and the digestate is a nutrient-rich fertilizer. The complexity of AD, influenced by numerous variables such as substrate composition, temperature, pH levels, hydraulic retention time, and microbial community dynamics, poses challenges in monitoring and optimisation. Machine Learning (ML) has emerged as a pivotal tool in interpreting the nonlinear relationships inherent in these AD systems, enhancing control, operational safety, and performance forecasting [1,2] . Literature reviews in the domain of ML applications for AD, such as those by Cruz et al. [3] and Gupta et al. [4], acknowledge the nascent stage of ML-based solutions in AD. They focus on algo","cbCaihiar8UB9L6V","https://ap.wps.com/l/cbCaihiar8UB9L6V","pdf",476004,1,21,"English","en",105,"# Introduction\n## Anaerobic digestion basics and challenges\n## Motivation for machine learning in AD\n## Research questions and scope\n# Abstract and research aims\n## Current ML use in anaerobic digestion\n## Implementation challenges and benefits\n## Dataset limitations and generalization issues\n## Future directions","[{\"question\":\"Which machine-learning methods are most commonly used, and what limitations are discussed?\",\"answer\":\"Artificial neural networks are the most commonly used, but they face difficulties in scalability and interpretability, especially when trained on lab-scale data.\"}]","A Systematic Review of Machine-Learning Solutions in Anaerobic Digestion | PDF",1785672968,53,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"a-systematic-review-of-machine-learning-solutions-in-anaerobic-digestion","",{"@graph":36,"@context":77},[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/a-systematic-review-of-machine-learning-solutions-in-anaerobic-digestion/116985/",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],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine-learning methods are most commonly used, and what limitations are discussed?","Question",{"text":75,"@type":76},"Artificial neural networks are the most commonly used, but they face difficulties in scalability and interpretability, especially when trained on lab-scale data.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]