A low-temperature driven organic Rankine cycle for waste heat recovery from a geothermal driven Kalina cycle: 4E analysis and optimization based on artificial intelligence. (February 2023)

• Record Type: Journal Article
• Authors: Hai, Tao; Ashraf Ali, Masood; Chaturvedi, Rishabh; Fahad Almojil, Sattam; Ibrahim Almohana, Abdulaziz; Fahmi Alali, Abdulrhman; Twfiq Almoalimi, Khaled; Qasim Ahmed Alyousuf, Farah; Shamseldin, Mohamed A.
• Is Part Of: Sustainable energy technologies and assessments. Volume 55(2023)
• Publication Date: 2023-02
• Subjects: Waste heat recovery -- Low-temperature organic Rankine cycle -- Exergoeconomic analysis -- AI based optimization; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102895
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: It has long been proven that geothermal energy may be used to generate electricity and heat sustainably. It emits less pollution, has a greater heat source temperature, and is compatible with a wide variety of energy systems. This research aims to use an ORC to utilize the excess energy of Kalina cycle systems (KCS) driven by a geothermal unit to generate clean, sustainable, and cost-effective low-temperature electricity. The most amazing feature of the Kalina cycle is that it gains more heat during heat addition in its evaporator owing to its significant thermo physical effects, as seen in Fig. 5 . The system's extensive modeling is based on energy, exergy, and economic considerations. Additionally, optimization is carried out in order to get the lowest Levelized cost of power (product). The sensitivity analysis is used to determine the most effective parameters for system implementation. The results indicate that the unit cost of the product for the hybrid system is at its minimum amount of 0.04898. For the Kalina, the system is 0.5023, in which the effectiveness hybrid scheme will be 48.57%, and the effectiveness of the Kalina system will be 44.21%. The most exergy destruction occurs in the evaporator and then the Kalina cycle condenser because these components have the highest temperature difference. Finally, the AI-based genetic algorithm is implemented to find the best solution point in terms of LCOC using neural networks.