AIMC Topic: Methane

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Ensemble machine learning prediction of anaerobic co-digestion of manure and thermally pretreated harvest residues.

Bioresource technology
This study aimed to clarify the statistical accuracy assessment approaches used in recent biogas prediction studies using state-of-the-art ensemble machine learning approach according to 10-fold cross-validation in 100 repetitions. Three thermally pr...

Multitask Deep Learning Enabling a Synergy for Cadmium and Methane Mitigation with Biochar Amendments in Paddy Soils.

Environmental science & technology
Biochar has demonstrated significant promise in addressing heavy metal contamination and methane (CH) emissions in paddy soils; however, achieving a synergy between these two goals is challenging due to various variables, including the characteristic...

Optimization of biogas production from straw wastes by different pretreatments: Progress, challenges, and prospects.

The Science of the total environment
Lignocellulosic biomass (LCB) presents a promising feedstock for carbon management due to enormous potential for achieving carbon neutrality and delivering substantial environmental and economic benefit. Bioenergy derived from LCB accounts for about ...

Enhancing corn stalk-based anaerobic digestion with different types of zero-valent iron added during the acidification stage: Performance and mechanism.

Journal of environmental sciences (China)
Anaerobic digestion has been defined as a competitive approach to facilitate the recycling of corn stalks. However, few studies have focused on the role of direct interspecies electron transfer (DIET) pathway in the acidification stage under the addi...

Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects.

Bioresource technology
Anaerobic co-digestion (AcoD) offers several merits such as better digestibility and process stability while enhancing methane yield due to synergistic effects. Operation of an efficient AcoD system, however, requires full comprehension of important ...

Adaptively Optimized Gas Analysis Model with Deep Learning for Near-Infrared Methane Sensors.

Analytical chemistry
Noise significantly limits the accuracy and stability of retrieving gas concentration with the traditional direct absorption spectroscopy (DAS). Here, we developed an adaptively optimized gas analysis model (AOGAM) composed of a neural sequence filte...

Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models.

Water research
Interest in anaerobic co-digestion (AcoD) has increased significantly in recent decades owing to enhanced biogas productivity due to the utilization of different organic wastes, such as food waste and sewage sludge. In this study, a robust AcoD model...

Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data.

Water research
Modeling of anaerobic digestion (AD) is crucial to better understand the process dynamics and to improve the digester performance. This is an essential yet difficult task due to the complex and unknown interactions within the system. The application ...

Experimental investigation and optimal combustion control of untreated landfill gas via fuzzy logic rule knowledge based approach.

Waste management (New York, N.Y.)
Optimal combustion control of untreated landfill gas is proposed for an effective usage and a low-cost solution in waste to energy technologies. Variations of methane concentration in untreated landfill gas over time cause undesired performance of co...

Interpretable machine learning for predicting biomethane production in industrial-scale anaerobic co-digestion.

The Science of the total environment
The objective of this study is to apply machine learning models to accurately predict daily biomethane production in an industrial-scale co-digestion facility. The methodology involved applying elasticnet, random forest, and extreme gradient boosting...