AIMC Topic: Hydrocarbons

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Machine Learning-Aided Screening and Design Rule Discovery for LWIR-Transparent Optical Materials.

Journal of chemical information and modeling
The development of low-cost, high-performance materials with enhanced transparency in the long-wavelength infrared (LWIR) region (800-1250 cm/8-12.5 μm) is essential for advancing thermal imaging and sensing technologies. Traditional LWIR optics rely...

Hybrid machine learning approach for prediction and design optimization of marshall stability in graphene oxide-modified asphalt concrete.

Environmental research
Marshall Stability (MS) is a key, yet costly and time-consuming, metric for designing asphalt concrete (AC) in general, and Graphene Oxide (GO)-modified AC in particular. To address this, this study introduces a novel hybrid machine learning framewor...

Predicting hydrocarbon presence in marine cold seep sediments using machine learning models trained with benthic bacterial 16S rRNA taxonomy.

Microbiology spectrum
UNLABELLED: Hydrocarbon seepage in marine sediments exerts selective pressure on benthic microbiomes. Accordingly, microbial community composition in these sediments can reflect the presence of hydrocarbons, with specific groups being more prolific i...

Quantitative evaluation of hydrocarbon contamination in soil using hyperspectral data-a comparative study of machine learning models.

Environmental monitoring and assessment
This study aims to evaluate the applicability of existing machine learning and deep learning techniques for the rapid prediction of hydrocarbon contamination in soils using hyperspectral data. Soil samples of three types, i.e., clayey, silty, and san...

Ecological risk assessment of oilfield soil through the use of machine learning combining with spatial interaction effects.

Ecotoxicology and environmental safety
With the intensification of oil extraction activities, total petroleum hydrocarbons (TPHs) and toxic elements contamination in soil around oil wells have become severe environmental problems. This paper proposed a novel method based on machine learni...

Development of a method for detecting and classifying hydrocarbon-contaminated soils via laser-induced breakdown spectroscopy and machine learning algorithms.

Environmental science and pollution research international
In recent years, there has been a significant increase in oil exploration and exploitation activities, resulting in spills that pose a severe threat to the environment and public health. The present work aims to develop a method to detect and classif...

Image recognition technology for bituminous concrete reservoir panel cracks based on deep learning.

PloS one
Detecting cracks in asphalt concrete slabs is challenging due to environmental factors like lighting changes, surface reflections, and weather conditions, which affect image quality and crack detection accuracy. This study introduces a novel deep lea...

A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution.

PloS one
Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction and early intervention are crucial for sustainable soil management. However, traditional soil analysis methods often rely on statistical methods, which means th...

New strategy to optimize in-situ fenton oxidation for TPH contaminated soil remediation via artificial neural network approach.

Chemosphere
In-situ remediation of total petroleum hydrocarbon (TPH) contaminated soils via Fenton oxidation is a promising approach. However, determining the proper injection amount of HO and Fe source over the Fenton reaction in the complex geological conditio...

Improved U-net network asphalt pavement crack detection method.

PloS one
Road crack detection is one of the important parts of road safety detection. Aiming at the problems such as weak segmentation effect of basic U-Net on pavement crack, insufficient precision of crack contour segmentation, difficult to identify narrow ...