Multi-Objective Evolutionary Optimization & 4E analysis of a bulky combined cycle power plant by CO2/ CO/ NOx reduction and cost controlling targets

Soheil Mohtaram, HongGuang Sun, Ji Lin, Wen Chen and Yonghui Sun

Renewable and Sustainable Energy Reviews, 2020, vol. 128, issue C

Abstract: The 4E analysis is utilized on a bulky combined cycle power plant (CCPP) with a dual pressure recovery boiler and an additional duct burner. Multi-objective evolutionary optimizations have been applied to obtain the best state of the heat recovery steam generator (HRSG), saturated temperature, cost reduction, and carbon dioxide emission, simultaneously. For the validation, the authentic data has been collected from an implemented CCPP. This comprehensive study has been performed to perceive the relation between the most significant parameters on the performance of CCPP. The main obtained results include five points. First, the thermal recovery boiler and combustion chamber have the highest exergy destruction among the power plant components. Second, the optimization based on the entire cycle at all temperatures has no economic justification, and its total exergy efficiency and cost are better than optimizations based on the recovery boiler and HRSG. Third, the value of the CCPP decision parameters is highly dependent on the ambient temperature. Therefore, it is not possible to apply the same value for CCPP at various temperatures. Fourth, the genetic algorithm improved the optimized cycle parameters by considering two objectives of the power plant costs reduction and CO2 emission. Fifth, the combined cycle with the nameplate function generates less NOx and monoxide than relative loads. Using such combined cycles with dual pressure recovery boiler and additional duct reduces the normalized Co2 emissions by 158.67kgMWh .

Keywords: Energy; Exergy; Environmental impacts; Exergoeconomic; Evolutionary algorithm (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.rser.2020.109898

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