AIMC Topic: Petroleum

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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...

Neuro-fuzzy modelling of a continuous stirred tank bioreactor with ceramic membrane technology for treating petroleum refinery effluent: a case study from Assam, India.

Bioprocess and biosystems engineering
A continuous stirred tank bioreactor (CSTB) with cell recycling combined with ceramic membrane technology and inoculated with Rhodococcus opacus PD630 was employed to treat petroleum refinery wastewater for simultaneous chemical oxygen demand (COD) r...

An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach.

PloS one
Crude Oil is one of the most important commodities in this world. We have studied the effects of Crude Oil inventories on crude oil prices over the last ten years (2011 to 2020). We tried to figure out how the Crude Oil price variance responds to inv...

A Deep Spiking Neural Network Anomaly Detection Method.

Computational intelligence and neuroscience
Cyber-attacks on specialized industrial control systems are increasing in frequency and sophistication, which means stronger countermeasures need to be implemented, requiring the designers of the equipment in question to re-evaluate and redefine thei...

Improved CEEMDAN, GA, and SVR Model for Oil Price Forecasting.

Journal of environmental and public health
Accurate prediction of crude oil prices (COPs) is a challenge for academia and industry. Therefore, the present research developed a new CEEMDAN-GA-SVR hybrid model to predict COPs, incorporating complete ensemble empirical mode decomposition with ad...

A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge.

Sensors (Basel, Switzerland)
Pipelines are integral components for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur, resulting not only in financial losses and energy waste but, most importantly, in immeas...

Use of gamma radiation and artificial neural network techniques to monitor characteristics of polyduct transport of petroleum by-products.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
This study presents a methodology based on the dual-mode gamma densitometry technique in combination with artificial neural networks to simultaneously determine type and quantity of four different fluids (Gasoline, Glycerol, Kerosene and Fuel Oil) to...

A novel deep learning method for marine oil spill detection from satellite synthetic aperture radar imagery.

Marine pollution bulletin
Oil spill discharges from operational maritime activities like ships, oil rigs and other structures, leaking pipelines, as well as natural hydrocarbon seepage pose serious threats to marine ecosystems and fisheries. Satellite synthetic aperture radar...

A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products.

Sensors (Basel, Switzerland)
Saybolt color is a standard measurement scale used to determine the quality of petroleum products and the appropriate refinement process. However, the current color measurement methods are mostly laboratory-based, thereby consuming much time and bein...

Forecasting the realized variance of oil-price returns: a disaggregated analysis of the role of uncertainty and geopolitical risk.

Environmental science and pollution research international
We contribute to the empirical literature on the predictability of oil-market volatility by comparing the predictive role of aggregate versus several disaggregated metrics of policy-related and equity-market uncertainties of the USA and geopolitical ...