Farah Abdoune
Papers
1
Total Citations
40
H-Index
1
About
Farah Abdoune is a rising researcher at the forefront of sustainable manufacturing and digital twin technology. Her work centers on developing data-driven methodologies to enhance energy efficiency in industrial processes, a critical challenge for modern production systems. In her most-cited paper, "Toward Digital twin for sustainable manufacturing: A data-driven approach for energy consumption behavior model generation" (2023, 40 citations), Abdoune introduces a novel framework that leverages real-time data to create predictive models of energy consumption behavior. This contribution enables manufacturers to simulate, monitor, and optimize energy use within digital twin environments, bridging the gap between virtual modeling and practical sustainability goals. Her research has already garnered attention for its potential to reduce industrial carbon footprints while maintaining operational performance. Abdoune’s work is particularly notable for its interdisciplinary approach, combining machine learning, systems engineering, and environmental science. As a young scholar, her growing citation count reflects the immediate relevance of her findings to both academia and industry, positioning her as a key voice in the transition toward greener, smarter factories.
Research Focus
Key Achievements
Top Papers
- 1