Bir yenilenebilir güç üretim sisteminin genetik algoritma yöntemi ile optimizasyonu

In this thesis, a genetic algorithm based thermodynamic optimization of biogas-powered cogeneration system which is active in GASKİ WWTP is presented. In this aim, our self-adaptive codes will be developed by using Matlab software. Cogeneration system produces 1000 kW electricity and supplies heat for anaerobic digestion. The objective of optimization is selected as exergy efficiency of the overall system and also exergy efficiencies of other components are optimized. Optimization variables are selected as air-fuel mixture ratio, the pressure of air-fuel mixture at the inlet of the gas engine and temperature of jacket cooling water at the outlet of the gas engine. Optimization is applied by using elitism and roulette wheel, separately. Gas engine exergy efficiency is determined by taking into account only the fuel input and power generation for the first approach, and by taking into account the addition of thermal effects to the first approach for the second approach. Exergy efficiency of the gas engine for 1st and 2nd approaches are found as 23.5% and 45.1% in elitism method and 26.7% and 46.7% in the roulette wheel method. Exergy efficiency of exhaust gas heat exchanger is determined 46.5% in elitism and 43% in roulette wheel. Exergy efficiencies of heat exchanger-1 and heat exchanger-2 are found to be 59% and 56% in elitism and 58.2% and 56.5% in roulette wheel, respectively. Overall exergy efficiency of the system is determined 33.2% in elitism and 33.5% in roulette wheel.