Multi-objective optimization of a new cogeneration system driven by gas turbine cycle for power and freshwater production

A novel power and freshwater generation system is presented based on the gas turbine cycle as the main system and Kalina cycle and humidification-dehumidification desalination unit as the waste heat recovery subsystems. To evaluate the system's performance, energy, exergy, economic, and environmental analysis is performed. The optimum performance of all optimization scenarios is found by applying the multi-objective genetic algorithm and using the technique for order of preference by similarity to ideal solution (TOPSIS) method. The sensitivity analysis is also performed to assess the effect of various parameters on the system's performance. By using Net present value, the feasibility of the plant for the construction from the economic viewpoint is analyzed. Based on the base case results, the performance metrics are evaluated as the energy efficiency of 0:9398, exergy efficiency of 43:11%, sum unit cost of 19:44 $.GJ(-1), Levelized total emission of 63571 kg.kW(-1), and freshwater production rate of 10:39 kg.s(-1). Among all system components, the combustion chamber is contributed to the highest rate of exergy destruction rate by 16544 kJ.s(-1). For fuel cost of 3 $.GJ(-1) and electricity price of 0:09 $.kWh(-1), the total net present value for the plant lifetime is obtained 1.736 x 10(7) $, which means that the plant is feasible for construction from the economic perspective. Based on the optimization results, the maximum value of exergy efficiency and minimum value of Levelized total emission are obtained in LTE-epsilon-TGOR scenario by 43:84% and 62602 kg.kW(-1), respectively. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

A novel power and freshwater generation system is proposed based on gas turbine cycle as the main system with Kalina cycle and humidification-dehumidification desalination unit as waste heat recovery subsystems. The system's performance is evaluated through energy, exergy, economic, and environmental analysis, with optimization achieved through a genetic algorithm and TOPSIS method. The combustion chamber contributes the highest exergy destruction rate, while economic feasibility is confirmed through net present value analysis.