A comprehensive approach for tri-objective optimization of a novel advanced energy system with gas turbine prime mover, ejector cooling system and multi-effect desalination

Abstract

This research presents an innovative approach for optimization based on a Genetic Algorithm optimization method. The system is configured by the integration of a gas turbine cycle, a dual-pressure heat recovery steam generator, a multi-effect desalination unit, a refrigeration organic Rankine cycle with an ejector, and a proton-exchange membrane electrolyzer. The proposed system is optimized utilizing five single- and multi-objective methods and investigating each objective's effect on the optimum range of the decision variables. As a result of these optimization five best points are extracted. The base condition, and these five best points are identified as six conditions, and the performance and reliability of the optimization results are investigated in a comparative parametric study. The single-objective optimizations results show that the maximum possible exergy efficiency and freshwater production rate are 72% and 1354 m3/day, respectively, and the lowest possible total cost rate is 611 $/h. However, tri-objective optimization demonstrates for these parameters that the best point has efficiency, cost, and freshwater production rate values of 69%, 791 $/h, and 1063 m3/day, respectively. The comparative parametric study shows that the tri-objective optimization result (Condition 5) is favorable in terms of objectives and reliability.