Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2

Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2

Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4

PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3

Is Internet becoming a Major Contributor for Global warming - The Online Carbon Footprint
Volume-2 | Issue-4

Augmented Reality in Education
Volume-2 | Issue-4

A Study on Various Task-Work Allocation Algorithms in Swarm Robotics
Volume-2 | Issue-2

IoT based Biotelemetry for Smart Health Care Monitoring System
Volume-2 | Issue-3

Tungsten DiSulphide FBG Sensor for Temperature Monitoring in Float Glass Manufacturing
Volume-2 | Issue-4

GUI based Industrial Monitoring and Control System
Volume-3 | Issue-2

AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT
Volume-1 | Issue-1

Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4

Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2

Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2

VIRTUAL REALITY GAMING TECHNOLOGY FOR MENTAL STIMULATION AND THERAPY
Volume-1 | Issue-1

Design of Digital Image Watermarking Technique with Two Stage Vector Extraction in Transform Domain
Volume-3 | Issue-3

Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century
Volume-4 | Issue-3

PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3

Image Augmentation based on GAN deep learning approach with Textual Content Descriptors
Volume-3 | Issue-3

Comparative Analysis for Personality Prediction by Digital Footprints in Social Media
Volume-3 | Issue-2

Home / Archives / Volume-4 / Issue-4 / Article-5

Volume - 4 | Issue - 4 | december 2022

AI and ML-based Assessment to Reduce Risk in Oil and Gas Retail Filling Station: A Literature Review
Jayameena Desikan  , A. Jayanthila Devi
Pages: 299-316
Cite this article
Desikan, Jayameena, and A. Jayanthila Devi. "AI and ML-based Assessment to Reduce Risk in Oil and Gas Retail Filling Station: A Literature Review." Journal of Information Technology and Digital World 4, no. 4 (2022): 299-316
DOI
10.36548/jitdw.2022.4.005
Published
23 January, 2023
Abstract

The oil crisis in recent years has pressurized petrol stations and associated service providers to improve efficiency and effectiveness. The accidents caused by human error and other technical incompetence lead to fatalities and environmental pollution. This paper analyses the role of Artificial Intelligence (AI) and Machine Learning (ML) in reducing the risk by various factors at retail oil and gas filling stations. The use of technology can help retail outlets in the oil and gas industry to reduce risks. This survey explores how to reduce workplace hazards at oil and gas filling stations to reduce fatalities, injuries, and other adverse health outcomes, which may be due to inhalation of toxic fumes, fire accidents, electrostatic charges, or any other artificial or natural reasons. Moreover, this review is done on how AI and ML can be used to reduce electrostatic discharges at the nozzles along with the automated replacement of human resources in hazardous situations. Therefore, the purpose includes the exploration of AI and ML technology to enhance safety at petrol and gas stations. This paper is a literature review of the articles published at different times.

Keywords

Oil and gas Industry artificial intelligence machine learning petrol stations risk assessment AI and ML

×

Currently, subscription is the only source of revenue. The subscription resource covers the operating expenses such as web presence, online version, pre-press preparations, and staff wages.

To access the full PDF, please complete the payment process.

Subscription Details

Category Fee
Article Access Charge
15 USD
Open Access Fee 100 USD
Annual Subscription Fee
200 USD
After payment,
please send an email to irojournals.contact@gmail.com / journals@iroglobal.com requesting article access.
Subscription form: click here