Application of Swarm Intelligence for the Multi-Objective Optimization of the Thermodynamic Performance and Economic of Double Reheat Steam Power Plants
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Abstract
This paper seeks to employ swarm intelligence (SI) based algorithms to perform the multi-objective optimization of a steam power plant using double reheat and feedwater heaters. The analysis is carried out to take into consideration the 3E aspects of energy, exergy, and economy. The first and second laws of thermodynamics are applied in the analysis of the cycle and the optimization of both the thermal and exergy efficiencies. Assessment of the economic aspect involves consideration of the fixed and operating costs. Three SI algorithm were tested and the resulting system performance improvements in terms of 3E analysis were compared. The algorithms were the sparrow search algorithm (SSA), artificial jellyfish search (JS) optimizer, and cooperation search algorithm (CSA). The testing revealed that the SSA method approach offered the best search potential at the optimal cost, thus proving more economical than either of the alternative algorithms. Furthermore, when comparing the SSA method multi-objective optimization in the steam power plant with the based case, the respective improvements in thermal and exergy efficiency were from 48.9% to 49.54%, and from 46.13% to 48.06%, while the overall cost saving was 25.8%.
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