Developing expert system for defects diagnostic for specific oil refinery pipelines via using artificial neural network

What is it about?

This research aims to develop an expert system for diagnosing defects in specific oil refinery pipelines using Artificial Neural Networks (ANN). Pipeline systems are critical components in oil refineries, responsible for transporting and processing crude oil and its derivatives. Any malfunction in these systems can lead to significant economic losses, environmental hazards, and operational downtime. Therefore, creating an intelligent system capable of accurately and rapidly detecting faults is essential for enhancing operational efficiency and ensuring system safety. The proposed system combines expert engineering knowledge with artificial intelligence techniques. Artificial Neural Networks are employed due to their ability to learn from historical data and detect complex patterns in sensor readings and operational parameters. The network is trained on actual refinery data, including types of defects, operating conditions, and observed symptoms, enabling it to classify and localize faults with high accuracy. Additionally, a knowledge base is designed to incorporate the expertise of engineers and technicians, which is integrated with the ANN outputs to improve diagnostic reliability. This hybrid approach enhances the system’s capability to provide early fault detection and support predictive maintenance strategies. By implementing such a system, refineries can significantly reduce maintenance costs, minimize unexpected failures, and extend the lifespan of critical equipment. Ultimately, this work contributes to the development of smart maintenance solutions and supports the digital transformation efforts within the oil and gas industry.

Featured Image

Photo by Hanson Lu on Unsplash

Photo by Hanson Lu on Unsplash