Exergo-economic analysis and multi-objective multi-verse optimization of a solar/biomass-based trigeneration system using externally-fired gas turbine, organic Rankine cycle and absorption refrigeration cycle

Highlights: • A solar/biomass-based trigeneration system has been proposed. • The system comprises an EFGT, an ORC, and a LiBr/H₂O absorbtion refrigeration cycle. • The system is studied from the energy, exergy, and exergo-economic perspective. • Solar thermal improves the thermodynamic efficiency of the system by about 10%. • MOMVO outperforms MOPSO and NSGA-II in solving multi-objective optimization problem. In the present work, an innovative configuration is proposed based on solar pre-heating concept and biomass direct-combustion for combined production of electricity, hot water and cooling load. The studied system consists of an externally fired gas turbine, a trans-critical organic Rankine cycle, and a Li-Br/H₂O absorption refrigeration cycle. The proposed system is thoroughly analyzed from the energy, exergy, and exergo-economic viewpoint. The results of the exergo-economic analysis demonstrate that the energy and exergy efficiencies of the reference case are 55.56% and 20.38% and the product cost rate is 26.4 $/h. Comparing the thermodynamic results to the literature, it is proved that the proposed system generates a considerably higher amount of power, heating, and cooling load which results in an improvement of energy efficiency by about 10%. In further, the main design parameters of each cycle are parametrically assessed to gain a comprehensive understanding of the system behavior. Finally, a new multi-objective optimization algorithm called multi-objective multi-verse optimizer (MOMVO) is employed to maximize exergy efficiency and minimize the product cost rate of the system. Compared to the base case, TOPSIS implementation revealed that the optimal final solution of the Pareto-frontier found by the MOMVO has about 9% higher second law efficiency while the product cost rate is decreased by around 6%. This result proved the better performance of the MOMVO compared to other conventional evolutionary-based multi-objective optimzation algorithms.