AIMC Topic: Methane
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Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network.
Bioresource technology
Dec 16, 2016
Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato s...
Application of fuzzy neural networks for modeling of biodegradation and biogas production in a full-scale internal circulation anaerobic reactor.
Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Sep 9, 2016
This paper presents the development and evaluation of three fuzzy neural network (FNN) models for a full-scale anaerobic digestion system treating paper-mill wastewater. The aim was the investigation of feasibility of the approach-based control syste...
A fuzzy-logic-based controller for methane production in anaerobic fixed-film reactors.
Environmental technology
May 13, 2016
The main objective of this work was to develop a controller for biogas production in continuous anaerobic fixed-bed reactors, which used effluent total volatile fatty acids (VFA) concentration as control input in order to prevent process acidificatio...
Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor.
Bioresource technology
Mar 12, 2016
The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, veg...
Enhanced methane production in an anaerobic digestion and microbial electrolysis cell coupled system with co-cultivation of Geobacter and Methanosarcina.
Journal of environmental sciences (China)
Oct 23, 2015
The anaerobic digestion (AD) and microbial electrolysis cell (MEC) coupled system has been proved to be a promising process for biomethane production. In this paper, it was found that by co-cultivating Geobacter with Methanosarcina in an AD-MEC coupl...
Meta-tuning and fast optimization of machine learning models for dynamic methane prediction in anaerobic digestion.
Bioresource technology
Sep 1, 2025
This study evaluates the performance of several optimization algorithms for tuning a data preparation and hyperparameter optimization pipeline applied to machine and deep learning models predicting methane production. Bayesian ridge regression and re...
Modeling enteric methane emission from dairy cows using deep learning approach.
The Science of the total environment
Jul 1, 2025
This study explores the application of deep learning (DL) models to predict methane (CH) emissions from enteric fermentation in dairy cows using performance, feeding, behavioral and weather data from automated milking and feeding systems, behavioral ...
Machine learning-based prediction of ambient CO and CH concentrations with high temporal resolution in Seoul metropolitan area.
Environmental pollution (Barking, Essex : 1987)
Jul 1, 2025
Machine learning has the potential to support the growing need for high-resolution greenhouse gas monitoring in urban and industrial environments, where deploying extensive sensor networks is often limited by cost and operational challenges. This stu...
The machine learning prediction model of non-alcoholic fatty liver; the role of hydrogen and methane breath tests.
Journal of breath research
Jun 13, 2025
Nonalcoholic fatty liver disease (NAFLD) is now the leading cause of global chronic liver disease, affecting approximately 32.4% of the population in various regions and imposing healthcare and economic burdens. The gold standard for the diagnosis of...
Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning.
Journal of animal science
Jan 3, 2024
Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers th...
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