Improving the Real-Time Operations of an Industrial Facility using a Machine Learning based Self Adaptive System

Abstract

This paper relates generally to industrial process control and automation systems that automate the operation of one or more bulk fuel terminal for the products such as crude oil, refined oil, liquefied natural gas, petroleum gas and other fuel type. More specifically, it focuses on the machine learning based self-adaptive system that would be able to learn and automatically adapt and improve its behaviour based on the data stream and the history of past actions to improve the real-time operational efficiency of the bulk fuel terminal station. It is an integrated, modular, and scalable solution that can be extended to any industrial facilities.