"American Ras Al Khaimah" It is involved in a practical study to reduce the emissions of gas turbine stations
Ras Al Khaimah on July 20/ WAM/ An international research team that includes a researcher from the American University in Ras Al Khaimah reached a new design for gas turbine stations based on a hybrid system that achieves a significant reduction in carbon dioxide emissions, as well as improving energy efficiency and reducing operational costs.
Gase turbine stations are responsible for about half of the global electricity production, but contribute 15% of all energy -related carbon dioxide emissions.
In the current study, researchers propose a hybrid design for the station that provides a 18% decrease in operating costs per hour and a recovery of 1.2 years of operating costs, in addition to a decrease in carbon dioxide emissions by 27%.
The new design presents a multi -stage approach to recovering heat, which surpasses traditional systems by integrating the Rankin cycle, along with modern technologies that achieve triple benefits that include: generation of electricity, cooling, and hydrogen production, and the design also benefits from part of the electricity resulting to sweeten sea water using reverse osmosis (RO).
Dr. David A. Schmidt, President of the American University in Ras Al Khaimah, is proud of the university’s contribution to this research, indicating that it reflects the level of high applied research at the university.
As for Dr. Uday Kumar Notaki, associate professor of chemical engineering and the CEO of sustainability at the American University of Ras Al Khaimah, he stressed that the proposed design is practically implemented and its accuracy was verified according to the standards of industry.
Researchers from: Yantai Technology Institute (China), Tashkent University of Educational University (Uzbekistan), King Saud University (Saudi Arabia), the Islamic University in Najaf (Iraq) and the American University in Ras Al Khaimah (Emirates) participated in the study.
The research team plans to continue developing the model through frequent improvements using artificial intelligence and real -time data integration, in order to achieve concrete environmental and economic goals to an actual experimental application.
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