Belt Conveyor Operating parameters Optimization for Eco-Efficient Ship-Unloading using machine learning techniques

One of the most important pieces of equipments used in maritime transportation for Self-Unloading (SUL) bulk carriers is the “conveyor belt,” which allows the ship to be a very effective and competitive solution. However, the conveyor belts use much energy, which means huge consumption of fuel materials that lead to the emission of polluting gases in harbor territory, calling for immediate actions to sustain the future green seaport vision. The operation parameters determination and their optimization are important to achieve high productivity during the unloading process, energy efficiency, and less pollution. Therefore, this project aims to use data science techniques involving machine learning to understand better conveyor belt operational parameters relationships, manipulating and processing sensors data to find a solution for energy consumption while maintaining the same efficiency and speed. The accuracy of the machine-learning techniques will be evaluated and validated.

Faculty Supervisor:

Amin Chaabane

Student:

Partner:

Groupe CSL Inc

Discipline:

Engineering

Sector:

Transportation and warehousing

University:

École de technologie supérieure

Program: