Applied research in dynamic risk analytics using machine learning

Risk analysis has a primary role in safety-critical industries such as oil and gas explorations, marine and pipeline transportations, and downstream operations. This essential task is facing a series of challenges due to the increased complexity and volume of generated data. Due to the recent advancements in cloud computing power provided by the two giants: Google and Amazon, a powerful web-platform for advanced risk analytics and predictions is currently under development. SRCube Technologies Inc. is the first web platform to provide a direct link between applied research in machine learning for risk engineering and the petroleum and chemical industry. Engineers and executives can track current and predictive industrial risks such as toxic releases, fire hazards and explosions due to loss of containment. Real-time process analytics are generated, visualized, and processed to predict dangerous escalations based on research-proven models using machine-learning techniques.

Faculty Supervisor:

Carlos Bazan

Student:

Mohammed Taleb Berrouane

Partner:

SRCube Technologies Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Memorial University of Newfoundland