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Heat recovery from oxy-supercritical carbon dioxide cycle incorporating Goswami cycle for zero emission power/heat/cooling production scheme; techno-economic study and artificial intelligence-based optimization

Yunhe ZouInner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot, 010051, ChinaMohammed A. AlghassabDepartment of Electrical, College of Engineering, Shaqra University, Saudi ArabiaAbdulkareem AbdulwahabAir Conditioning and Refrigeration Techniques Engineering Department, College of Engineering and Technologies, Al-Mustaqbal University , Babylon, IraqAman SharmaDepartment of Mechanical Engineering, GLA University, Mathura, IndiaRaymond GhandourCollege of Engineering and Technology, American University of the Middle East, Egaila, 54200, KuwaitSalem AlkhalafDepartment of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Saudi ArabiaFawaz S. AlharbiDepartment of Mechanical Engineering, College of Engineering, University of Hafr Al Batin, P.O. Box 1803, Hafr Al Batin, 39524, Saudi ArabiaBarno Sayfutdinovna AbdullaevaDepartment of Mathematics and Information Technologies, Faculty of Mathematics and Physics, Vice-Rector for Scientific Affairs, Tashkent State Pedagogical University, Tashkent, UzbekistanYasser ElmasryDepartment of Mathematics - Faculty of Science - King Khalid University, P.O. Box 9004, Abha, 61466, Saudi Arabia
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Abstract

This research aims to explore the economic and energy efficiency ramifications of combining the supercritical carbon dioxide oxy-fuel cycle with the Goswami cycle in the context of integrated heating and cooling systems, as well as power generation. Oxy-fuel systems, known for their high operating temperatures. In oxy-fuel systems, gas turbine exhaust gases typically preheat incoming flows to the combustion chamber. Nevertheless, even after preheating the combustion chamber inlets, the exhaust gases from the gas turbine retain a relatively high temperature, demonstrating considerable potential for energy production. Through a comprehensive parametric study, the impact of independent parameters on system performance is systematically explored. Subsequently, a three-stage economic analysis, total capital investment cost estimation, total operating cost estimation, and income evaluation, is conducted. The investment return time is assessed based on the selling price of system products. In the final step, a genetic algorithm, combined with artificial intelligence, optimizes the system for rational and optimal economic and energy conditions. The research findings reveal that in the absence of the Goswami cycle, the system yields 13.5 MW of power and 8.5 MW of heating, with an investment return time of approximately 16.32 years. However, with the inclusion of the Goswami cycle, power production increases to 15 MW, accompanied by 5 MW of cooling and 6.7 MW of heating. Despite augmented initial and operational costs, the investment return time is significantly reduced to 9.9 years. The results of a three-objective optimization strategy, focusing on maximizing power, heating, and cooling, highlight the system's versatility. Opting for maximum power production enhances power by 12.53 % and cooling by 27.48 %, compared to the reference mode. However, heating production decreases by 37.01 %. Notably, total capital investment costs show improvement by about 4.54 %, and the pay-back time experiences a substantial reduction of approximately 27.68 %. Moreover, the levelized cost of energy decreases from 111.4 $/MWh in the reference state to 106.9 $/MWh, underscoring the economic efficiency of the integrated system.

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