AIMC Topic: Methane

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An ML-Enhanced Laser-Based Methane Slip Sensor Using Wavelength Modulation Spectroscopy.

ACS sensors Jan 15, 2025
Natural gas (NG) is a promising alternative to diesel for sustainable transport, potentially reducing GHG and air quality emissions significantly. However, the GHG benefits hinge on managing methane slip, the unburned methane in the exhaust of NG eng...
Machine Learning Methane Vehicle Emissions Natural Gas Spectrum Analysis Lasers
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Accelerated Screening of Highly Sensitive Gas Sensor Materials for Greenhouse Gases Based on DFT and Machine Learning Methods.

ACS sensors Jan 6, 2025
Greenhouse gases (GHGs) have caused great harm to the ecological environment, so it is necessary to screen gas sensor materials for detecting GHGs. In this study, we propose an ideal gas sensor design strategy with high screening efficiency and low c...
Machine Learning Gases Greenhouse Gases Methane Carbon Dioxide Adsorption Density Functional Theory
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Optimizing LandGEM model parameters using a machine learning method to improve the accuracy of landfill methane gas generation estimates in the United States.

Journal of environmental management Jan 5, 2025
Municipal solid waste (MSW) landfills significantly contribute to global methane gas production, underscoring the critical need for accurate emission gas estimation within an effective gas management strategy. While first-order models such as LandGEM...
United States Methane Environmental Monitoring Models, Theoretical Machine Learning Waste Disposal Facilities Air Pollutants
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Global forecasting of carbon concentration through a deep learning spatiotemporal modeling.

Journal of environmental management Nov 15, 2024
Given the global urgency to mitigate climate change, a key action is the development of effective carbon concentration reduction policies. To this end, an influential factor is the availability of accurate predictions of carbon concentration trends. ...
Climate Change Methane Carbon Dioxide Neural Networks, Computer Forecasting Models, Theoretical Carbon Deep Learning
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Microbial-Guided prediction of methane and sulfide production in Sewers: Integrating mechanistic models with Machine learning.

Bioresource technology Oct 15, 2024
Accurate modeling of methane (CH) and sulfide (HS) production in sewer systems was constrained by insufficient consideration of microbial processes under dynamic environmental conditions. This study introduces a microbial-guided machine learning (ML)...
Machine Learning Sulfides Sewage Hydrogen Sulfide Methane Models, Biological
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Neural Network Based Aliasing Spectral Decoupling Algorithm for Precise Mid-Infrared Multicomponent Gases Sensing.

ACS sensors Aug 16, 2024
Owing to the overlapping and cross-interference of absorption lines in multicomponent gases, the simultaneous measurement of such gases via laser absorption spectroscopy frequently necessitates the use of supplementary pressure sensors to distinguish...
Neural Networks, Computer Algorithms Limit of Detection Methane Water Spectrophotometry, Infrared Steam Ethane Gases
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Sludge bound-EPS solubilization enhance CH bioconversion and membrane fouling mitigation in electrochemical anaerobic membrane bioreactor: Insights from continuous operation and interpretable machine learning algorithms.

Water research Aug 9, 2024
Bound extracellular polymeric substances (EPS) are complex, high-molecular-weight polymer mixtures that play a critical role in pore clogging, foulants adhesion, and fouling layer formation during membrane filtration, owing to their adhesive properti...
Biofouling Extracellular Polymeric Substance Matrix Sewage Membranes, Artificial Machine Learning Waste Disposal, Fluid Bioreactors Methane Anaerobiosis Solubility
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Machine learning-aided unveiling the relationship between chemical pretreatment and methane production of lignocellulosic waste.

Waste management (New York, N.Y.) Jul 27, 2024
Chemical pretreatment is a common method to enhance the cumulative methane yield (CMY) of lignocellulosic waste (LW) but its effectiveness is subject to various factors, and accurate estimation of methane production of pretreated LW remains a challen...
Biofuels Refuse Disposal Methane Lignin Machine Learning
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Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass.

The Science of the total environment Jun 20, 2024
Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine le...
Methane Biofuels Machine Learning Carbon Dioxide Gases Biomass
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IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.

Environmental monitoring and assessment Jun 15, 2024
This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is a...
Methane Particulate Matter Carbon Monoxide Carbon Dioxide Air Pollutants Machine Learning Cameroon Air Pollution Environmental Monitoring Nitrogen Dioxide Volatile Organic Compounds
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