In the sprawling landscapes of northeastern Brazil, where power transmission lines stretch across vast distances, a silent enemy is at work—atmospheric corrosion. This natural process, driven by environmental factors, can lead to costly failures and maintenance challenges for the energy sector. A recent study, published in the journal *Materials Research* (translated from Portuguese as “Pesquisa em Materiais”), offers a promising solution to this persistent problem.
Led by Camila Marçal Gobi Pacher, a researcher affiliated with the Federal University of Ceará, the study presents an innovative methodology for mapping atmospheric corrosivity and assessing corrosion-related failures in power transmission lines and substations. The research team exposed carbon and galvanized steel coupons along a 1,150 km power transmission line spanning Ceará, Piauí, and Maranhão. Over the course of a year, they meticulously measured the corrosion rates through mass and thickness loss, combining these findings with local meteorological data such as temperature, humidity, and precipitation rates.
The real breakthrough, however, came in the form of a feed-forward neural network model. This artificial intelligence tool was trained, tested, and validated using the collected data. The results were impressive, particularly for galvanized steel, with the model demonstrating low mean absolute and mean squared errors, enabling reliable forecasts. For carbon steel, the predictions were slightly less accurate but still sufficient for classifying local atmospheric corrosivity levels.
“The model’s ability to generate corrosion maps for both metals is a game-changer,” Pacher explained. “It allows for a detailed visual analysis across the entire transmission line, helping utility companies pinpoint areas at higher risk of corrosion-related failures.”
The commercial implications for the energy sector are significant. By accurately predicting corrosion rates and identifying high-risk areas, utility companies can optimize maintenance schedules, reduce downtime, and extend the lifespan of their infrastructure. This proactive approach can lead to substantial cost savings and improved reliability of power transmission systems.
Moreover, the study’s methodology can be adapted to other regions and environments, making it a versatile tool for the global energy sector. As Pacher noted, “This research provides a robust framework that can be applied to various settings, helping to mitigate the impacts of atmospheric corrosion worldwide.”
The study’s findings not only highlight the importance of advanced monitoring systems but also underscore the potential of artificial intelligence in solving real-world problems. As the energy sector continues to evolve, such innovative approaches will be crucial in ensuring the reliability and efficiency of power transmission infrastructure.
In the words of Pacher, “This is just the beginning. The integration of AI and corrosion science opens up new avenues for research and development, paving the way for smarter and more resilient energy systems.”
As the energy sector grapples with the challenges of atmospheric corrosion, this research offers a beacon of hope, demonstrating the power of innovation and technology in overcoming long-standing issues. The future of power transmission lines and substations looks brighter, thanks to the pioneering work of Pacher and her team.