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Optimization strategies for mixing ratio of biogas and natural gas co-firing in a cogeneration of heat and power cycle

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  • Zare, A. Darabadi
  • Saray, R. Khoshbakhti
  • Mirmasoumi, S.
  • Bahlouli, K.

Abstract

The most suitable design parameters of the cogeneration system, from the exergetic and the exergo-economic viewpoints, were determined using the genetic algorithm based on optimization method. In this study, a cogeneration cycle, consisting of a gas turbine cycle with power generation capacity of 30 MW, an anaerobic digester, a steam generator, and a heat exchanger for heating the digester and pretreatment system, has been used. The air compressor pressure ratio, the isentropic efficiencies of gas turbine and air compressor, biogas and natural gas mixing ratio, air preheater outlet temperature, turbine inlet temperature, and evaporator pinch point temperature difference were selected as the decision variables. Multi-objective optimization based on genetic algorithm using MATLAB software is carried out to discover the optimum point with regard to the total cost rate and exergy efficiency as the objective functions. The optimization was conducted according two scenarios. While the fuel mixing ratio was considered as a decision variable in the first scenario, in the second one this mixing ratio was kept constant, 50%. Optimization of the cycle according to the first scenario raised the exergy efficiency from 46.59% to 53.25% and decreased the system total cost rate from 4385$/h to 2935$/h. As well, compared to the base case, the optimal case of the second scenario had more exergy efficiency and less total cost rate, 48.35% versus 50.5% and 4028$/h versus 3665$/h, respectively.

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  • Zare, A. Darabadi & Saray, R. Khoshbakhti & Mirmasoumi, S. & Bahlouli, K., 2019. "Optimization strategies for mixing ratio of biogas and natural gas co-firing in a cogeneration of heat and power cycle," Energy, Elsevier, vol. 181(C), pages 635-644.
    • Handle: RePEc:eee:energy:v:181:y:2019:i:c:p:635-644
    DOI: 10.1016/j.energy.2019.05.182
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    7. Teng, Sin Yong & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav & Stehlík, Petr, 2021. "Debottlenecking cogeneration systems under process variations: Multi-dimensional bottleneck tree analysis with neural network ensemble," Energy, Elsevier, vol. 215(PB).

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