AIMC Topic: Hydrocarbons

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A Connectionist Model for Dynamic Economic Risk Analysis of Hydrocarbons Production Systems.

Risk analysis : an official publication of the Society for Risk Analysis Nov 16, 2021
This study presents a connectionist model for dynamic economic risk evaluation of reservoir production systems. The proposed dynamic economic risk modeling strategy combines evidence-based outcomes from a Bayesian network (BN) model with the dynamic ...
Bayes Theorem Models, Economic Hydrocarbons Neural Networks, Computer Risk Assessment
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Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas.

Water research Oct 25, 2021
The content of fat, oil and grease (FOG) in the sewer network sediments is the key indicator for diagnosing sewer blockage and overflow. However, the traditional FOG detection is time-consuming and costly, and the establishment of mathematical models...
Hydrocarbons Fats Deep Learning Sewage Temperature
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Adaboost Algorithm in Artificial Intelligence for Optimizing the IRI Prediction Accuracy of Asphalt Concrete Pavement.

Sensors (Basel, Switzerland) Aug 24, 2021
The international roughness index (IRI) for roads is a crucial pavement design criterion in the Mechanistic-Empirical Pavement Design Guide (MEPDG). However, studies have shown that the IRI transfer function in the MEPDG is simply a linear combinatio...
Algorithms Hydrocarbons Motor Vehicles Artificial Intelligence
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Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations.

Water research Jul 31, 2021
Accumulation of fat, oil and grease (FOG) in the sumps of wastewater pumping stations is a common failure cause for these facilities. Floating solids are often not transported by the pump suction inlets and the individual solids can accumulate to sti...
Wastewater Deep Learning Hydrocarbons Water Netherlands
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Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils.

Ecotoxicology and environmental safety Mar 9, 2020
Environment pollutants, especially those from total petroleum hydrocarbons (TPH), have a highly complex chemical, biological and physical impact on soils. Here we study this influence via modelling the TPH acute phytotoxicity effects on eleven sample...
Russia Hordeum Soil Pollutants Hydrocarbons Petroleum Islands Artificial Intelligence Soil Models, Theoretical Soil Microbiology Neural Networks, Computer
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'Emulsion locks' for the containment of hydrocarbons during surfactant flushing.

Journal of environmental sciences (China) Dec 18, 2019
Reversible double water in oil in water (W/O/W) emulsions were developed to contain subsurface hydrocarbon spills during their remediation using surfactant flushing. Double emulsions were prepared by emulsifying CaCl solutions in canola oil, and subs...
Sodium Dodecyl Sulfate Emulsions Water Hydrocarbons Surface-Active Agents
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Single spectral imagery and faster R-CNN to identify hazardous and noxious substances spills.

Environmental pollution (Barking, Essex : 1987) Dec 2, 2019
The automatic identification (location, segmentation, and classification) by UAV- based optical imaging of spills of transparent floating Hazardous and Noxious Substances (HNS) benefits the on-site response to spill incidents, but it is also challeng...
Petroleum Pollution Hazardous Substances Water Pollution, Chemical Spectrum Analysis Hydrocarbons Neural Networks, Computer
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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning.

Nature communications Jul 1, 2019
Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist's toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy ...
Hydrocarbons Neural Networks, Computer Thermodynamics Benchmarking Machine Learning Isomerism Computer Simulation
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A Neural Network QSPR Model for Accurate Prediction of Flash Point of Pure Hydrocarbons.

Molecular informatics Nov 29, 2018
The present study introduces a QSPR model to predict the flash point of pure organic compounds from diverse chemical families. We used the Maximum-Relevance Minimum-Redundancy (MRMR) as an efficient descriptor selection algorithm to select 20 the mos...
Models, Chemical Hydrocarbons Quantitative Structure-Activity Relationship Neural Networks, Computer Drug Discovery
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Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models.

Chemosphere Oct 11, 2018
Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of co...
Soil Pollutants Metals, Heavy Charcoal Neural Networks, Computer Risk Assessment Petroleum Machine Learning Hydrocarbons Environmental Pollution Soil Microbiology Soil Complex Mixtures Biological Availability
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