[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121175-en":3,"doc-seo-121175-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},121175,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Application of Machine Learning for FOS/TAC Soft Sensing in Bio-Electrochemical Anaerobic Digestion","This study explores the application of various machine learning (ML) models for the real-time prediction of the FOS/TAC ratio in microbial electrolysis cell anaerobic digestion (MEC-AD) systems using data collected during a 160-day trial treating brewery wastewater. Models including decision trees, XGBoost, support vector regression, a variant of SVM, and artificial neural networks (ANNs) are evaluated for soft sensing of system stability. ANNs achieve the best performance with explained variance of 0.77 and are further assessed via an out-of-fold ensemble across the full dataset. Results highlight ML’s role in improving operational efficiency and stability in bio-electrochemical systems (BES), reducing costs and the risks linked to process instability. ","Article  \nApplication of Machine Learning for FOS/TAC Soft Sensing in Bio-Electrochemical Anaerobic Digestion  \nHarvey Rutland 1, *, Jiseon You 2, Haixia Liu 3 and Kyle Bowman 4  \nAcademic Editors: Xiaolei Sun and Wenhe Xie  \nReceived: 16 January 2025  \nRevised: 14 February 2025  \nAccepted: 24 February 2025  \nPublished: 27 February 2025  \nCitation: Rutland, H.; You, J.; Liu, H.; Bowman, K. Application of Machine Learning for FOS/TAC Soft Sensing in Bio-Electrochemical Anaerobic Digestion. Molecules 2025, 30, 1092. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)molecules30051092  \nCopyright: © 2025 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://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, University of Bristol, Bristol BS8 1QU, UK  \n2 Bristol Robotics Laboratory, 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)  \n4 School of Life Sciences, University of the Westminster, London W1W 6UW, UK; [k.bowman1@westminster.co.uk](k.bowman1@westminster.co.uk)  \n* Correspondence: [zq21170@bristol.ac.uk](zq21170@bristol.ac.uk)  \nAbstract: This study explores the application of various machine learning (ML) models for the real-time prediction of the FOS/TAC ratio in microbial electrolysis cell anaerobic digestion (MEC-AD) systems using data collected during a 160-day trial treating brewery wastewater. This study investigated models including decision trees, XGBoost, support vector regression, a variant of support vector machine (SVM), and artificial neural networks (ANNs) for their effectiveness in the soft sensing of system stability. The ANNs demonstrated superior performance, achieving an explained variance of 0.77, and were further evaluated through an out-of-fold ensemble approach to assess the selected model’s performance across the complete dataset. This work underscores the critical role of ML in enhancing the operational efficiency and stability of bio-electrochemical systems (BES), contributing significantly to cost-effective environmental management. The findings suggest that ML not only aids in maintaining the health of microbial communities, which is essential for biogas production, but also helps to reduce the risks associated with system instability.  \nKeywords: machine learning; deep learning; microbial electrolysis cell anaerobic digestion; FOS/TAC  \n1. Introduction  \nAnaerobic digestion (AD) is an effective biotechnology for converting a variety of organic wastes into biogas. However, the stability and efficiency of AD processes are challenged by factors such as substrate variability, organic loading rates, and the accumulation of substances like volatile fatty acids (VFAs), which can lead to inhibition, causing fluctuations in methane production and pH [1] .  \nMEC-AD systems have been shown to achieve higher methane yields compared to standard AD practices. Systems integrating low-voltage (poised under 2 V) electrodes within the reactors have demonstrated greater operational stability under lower pH conditions, which is beneficial for maintaining the health and efficiency of the microbial communities responsible for biogas production [2–4] . Furthermore, the integration of MECs with AD systems enhances substrate degradation and increases biogas production. MEC-AD systems additionally offer potential improvements in process control by enabling real-time monitoring, which correlates electrical signals with substrate concentrations, significantly enhancing operational efficien","cbCaihp7Nq8IRNF6","https://ap.wps.com/l/cbCaihp7Nq8IRNF6","pdf",1314812,1,18,"English","en",105,"# Introduction\n## Anaerobic digestion and stability challenges\n## MEC-AD advantages and monitoring potential\n## VFA role and measurement needs\n## FOS/TAC ratio as an equilibrium metric\n## Soft sensing motivation and limitations","[{\"question\":\"What is the main goal of the study in MEC-AD systems?\",\"answer\":\"The study aims to predict the FOS/TAC ratio in real time using machine learning models, supporting soft sensing of system stability in MEC-AD.\"},{\"question\":\"Which ML models are evaluated for soft sensing performance?\",\"answer\":\"The work tests decision trees, XGBoost, support vector regression, a variant of SVM, and artificial neural networks (ANNs) to compare effectiveness for stability-related prediction.\"},{\"question\":\"Why is the FOS/TAC ratio important in anaerobic digestion?\",\"answer\":\"The FOS/TAC ratio characterizes total alkalinity/buffering capacity and helps detect process imbalances early, which can precede substantial pH changes and methane-production fluctuations.\"}]","Application of Machine Learning for FOS/TAC Soft Sensing in Bio-Electrochemical Anaerobic Digestion | 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