Aboveground Storage Tank (AST) tightness testing using statistical approach

The industry partner, Cantest is establishing a new leak detection procedure for analyzing data sources in aboveground storage tanks and statistical learning models to monitor AST shell dynamics and product activity over time. This is an important problem as identifying leak detection is usually associated with various environmental data and records collected from sensitive sensors attached to the ASTs. Current testing procedure for leak detection uses simple statistical rules and thresholds to detect anomalies. These methods are failing for preventing AST related environmental incidents. Incorporating data records from upgraded evaluation equipment, this research internship will help to identify and create appropriate leak detection procedures for the ASTs and extend and improve the functionality of Cantests current leak detection systems.

Faculty Supervisor:

Jingjing Wu

Student:

Wenyan Zhong

Partner:

Cantest Solutions

Discipline:

Mathematics

Sector:

Oil and gas

University:

Program: