Optimization of thermodynamic performance with simulated annealing algorithm: A geothermal power plant

2021 ◽  
Author(s):  
Gürcan Çetin ◽  
Ali Keçebaş
Keyword(s):  
Simulated Annealing ◽  
Power Plant ◽  
Simulated Annealing Algorithm ◽  
Geothermal Power Plant ◽  
Thermodynamic Performance ◽  
Annealing Algorithm ◽  
Geothermal Power

Analysis of the Thermodynamic Performance of Kalina Cycle System 11 (KCS11) for Geothermal Power Plant: Comparison With Kawerau ORMAT Binary Plant

2009 ◽  
Author(s):  
Xinli Lu ◽  
Arnold Watson ◽  
Joe Deans
Keyword(s):  
Power Generation ◽  
Power Plant ◽  
Heat Source ◽  
Gas Turbines ◽  
Effective Means ◽  
Specific Power ◽  
Kalina Cycle ◽  
Geothermal Power Plant ◽  
Thermodynamic Performance ◽  
Geothermal Power

Since the first geothermal power plant was built at Larderello (Italy) in 1904, many attempts have been made to improve conversion efficiency. Among innovative technologies, using the Kalina cycle is considered as one of the most effective means of enhancing the thermodynamic performance for both high and low temperature heat source systems. Although initially used as the bottoming cycle of gas turbines and diesel engines, in the late 1980s the Kalina cycle was found to be attractive for geothermal power generation [1, 2, 3]. Different versions (KSC11, KSC12 and KSC13) were designated. Comparison between Kalina cycle and other power cycles can be found in later studies [4, 5, 6]. Here we examine KSC11, because it is specifically designed for geothermal power generation, with lower capital cost [3]. We compare this design with the existing Kawerau ORMAT binary plant in New Zealand. In addition, parametric sensitivity analysis of KCS11 has been carried out for the specific power output and net thermal efficiency by changing the temperatures of both heat source and heat sink for a given ammonia-water composition.

A New Solution for Maxterm Problem in Trigonometric Functions by Simulated Annealing Algorithm

2013 ◽  
Vol 4 (2) ◽  
pp. 20-28
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Hadi Najafi ◽  
Kourosh Farahkhah
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Trigonometric Functions ◽  
Matlab Software ◽  
Annealing Algorithm ◽  
Do So

The present paper is an attempt to get total minimum of trigonometric Functions by Simulated Annealing. To do so the researchers ran Simulated Annealing. Sample trigonometric functions and showed the results through Matlab software. According the Simulated Annealing Solves the problem of getting stuck in a local Maxterm and one can always get the best result through the Algorithm.

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